Machine Learning and Neural Networks - page 65
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Your AI Toolkit - Working with Jupyter Notebooks
Your AI Toolkit - Working with Jupyter Notebooks
I am Dr. Soper, and today I have the pleasure of introducing you to your artificial intelligence toolkit. Our main focus will be on an incredibly useful and user-friendly technology called Jupyter Notebooks.
But before we dive into the specifics, let's take a moment to go over what you can expect to learn in this lesson.
By the end of this video, you will have a clear understanding of:
Throughout this lesson, we will embark on a hands-on journey to build, train, and test an artificial neural network. You'll be pleasantly surprised by how straightforward the process is!
To kick things off, let's discuss why having an AI toolkit is essential.
This series of videos on cognitive computing and artificial intelligence goes beyond theory and concepts. You will learn how to build various types of AI models!
To construct any artificial intelligence or cognitive computing model, we need a set of tools. These tools include computational resources like CPUs, memory, and storage for our files. We also require a development environment where we can work on our AI projects. Lastly, we need a set of instructions to communicate our desired actions to the computer.
In terms of tools, we will be learning the Python programming language throughout this series, starting with the next video.
Regarding computational resources and the development environment, Jupyter Notebooks hosted in the cloud can provide both for our AI and cognitive computing projects.
Now, let's explore what Jupyter Notebooks are.
A Jupyter Notebook is an interactive, web-based environment consisting of an ordered collection of cells. Each cell within a Jupyter Notebook can contain text, programming code, mathematical formulas, images, or other media elements.
This versatility allows you to keep all your notes, code, diagrams, visualizations, and output from your AI and cognitive computing models in one place.
Jupyter Notebooks utilize kernels to run programming code and maintain the current state of your project. One of the most impressive features of Jupyter Notebooks is the ability to run one cell at a time. The notebook server automatically keeps track of the project's current state in memory.
This feature allows you to write code in one cell, execute it, and observe the results. You can then proceed to write additional code in subsequent cells, accessing and utilizing the results from previous cells. This incremental approach enables you to build and refine your project gradually without the need to rerun everything each time you make a change.
Another noteworthy aspect of Jupyter Notebooks is their support for multiple programming languages such as Julia, Python, and R. The name "Jupyter" actually originates from the combination of these three languages.
Now, you might wonder why Jupyter Notebooks are preferable over other development platforms.
While alternative approaches are available, Jupyter Notebooks offer numerous advantages for AI and cognitive computing projects:
Now that we understand the benefits of using Jupyter Notebooks, let's discuss how to create them for free in the Google Cloud and the Microsoft Cloud.
Both Google Cloud and Microsoft Cloud offer Jupyter Notebook services as part of their cloud platforms. These services provide you with a pre-configured environment to create and run Jupyter Notebooks.
In the Google Cloud, you can use Google Colab (short for Colaboratory), which is a free Jupyter Notebook environment that runs on Google's infrastructure. It provides access to GPUs and TPUs for accelerated machine learning computations.
To create a Jupyter Notebook in Google Colab, you can simply go to the Google Colab website (colab.research.google.com), sign in with your Google account, and start a new notebook. You can choose to create a blank notebook or open an existing notebook from Google Drive or GitHub.
Similarly, in the Microsoft Cloud, you can use Azure Notebooks, which is a free Jupyter Notebook service provided by Microsoft. Azure Notebooks offer a collaborative environment for data science and machine learning projects.
To create a Jupyter Notebook in Azure Notebooks, you can sign in to the Azure Notebooks website (notebooks.azure.com) with your Microsoft account. From there, you can create a new project, which will include a Jupyter Notebook by default.
Both Google Colab and Azure Notebooks provide a familiar Jupyter Notebook interface with the necessary computational resources to run your AI models. You can install additional libraries, upload datasets, and collaborate with others seamlessly.
In the next part of this lesson, we will dive into a practical example and demonstrate how to effectively utilize Jupyter Notebooks to develop and execute AI and cognitive computing projects.
Stay tuned, and let's continue our journey into the world of AI and Jupyter Notebooks!
Python Fundamentals - Part 01
Python Fundamentals - Part 01
I am Dr. Soper, and today I have the pleasure of presenting the first of three comprehensive lessons on the fundamentals of the Python programming language. While it's impossible to cover every detail of Python programming within a few videos, by the end of these three lessons, you will have gained sufficient knowledge to understand and embark on your Python programming journey.
Throughout these lessons, we will be utilizing Jupyter Notebooks, a powerful tool for interactive programming and data exploration. If you're unfamiliar with Jupyter Notebooks, I highly recommend watching the previous video in this series to familiarize yourself with this environment before diving into Python programming.
Let's begin by providing an overview of what you'll learn in this lesson. By the end of this video, you will have gained knowledge about the following aspects of Python:
Displaying text: We will learn how to use the print() function to showcase text on the screen. Text in Python is enclosed within single quotes to differentiate it from programming commands.
Variables: Variables are symbolically named storage locations in a computer's memory. They hold values that can be changed as needed. We will explore how to create variables and assign them values, whether they are text, integers, or floats.
Arithmetic operators: Python offers various arithmetic operators to perform mathematical operations on variables. We will cover addition, subtraction, multiplication, division, exponentiation, and modulo operations.
Comparison operators: Comparison operators allow us to compare two values and determine their relationship. We will learn about operators such as "equal to," "not equal to," "greater than," "less than," "greater than or equal to," and "less than or equal to."
Throughout the lesson, we will utilize examples and demonstrations to solidify your understanding of these Python skills and features. Let's start by discussing how to display text in Python. To showcase a line of text, we use the print() function. The text we want to display is passed as an argument to the print() function within single quotes. Additionally, we can include line breaks using the "\n" symbol. Comments, denoted by the pound sign (#), are for human use only and help explain code sections. Python ignores comments when executing the code.
To demonstrate these techniques, let's consider a code cell within a Jupyter Notebook. The code cell uses the print() function to display the text "Hello, my name is Dan!" on the screen. Another example showcases the use of "\n" to display multiple lines of text in a single print() function call.
Moving on to variables, they are named storage locations in a computer's memory. Variables can hold data of any type. To create a new variable in Python, we assign it a value by typing its name on the left side of the equals sign and the value on the right side. In a code cell, we can declare variables such as "product_name" with the value 'Delicious Nachos', "quantity_sold" with the value 33, and "unit_price" with the value 12.99. We can then print the values of these variables using the print() function and concatenation.
Alternatively, we can use the format() function to achieve the same result with placeholders for variable values. This simplifies the process by allowing us to define the desired output text and indicate the variable positions within curly braces. To demonstrate arithmetic operators, we utilize symbols such as "+" for addition, "-" for subtraction, "*" for multiplication, "/" for division, "**" for exponentiation, and "%" for modulo operation. These operators perform mathematical calculations on variables.
I hope you're all having a wonderful day. My name is Dr. Soper, and today I have the pleasure of presenting the first installment in a series of three lessons on the fundamentals of the Python programming language. Now, it's important to note that I won't be able to cover every single detail of Python programming in just a few videos. However, by the time you've completed these three lessons, you'll have acquired enough knowledge to understand and start working with Python projects.
Throughout these lessons, I'll be using a Jupyter Notebook to carry out all the examples. If you're not familiar with Jupyter Notebooks, I highly recommend watching the previous video in this series before diving into the world of Python programming. Without further ado, let's take a brief overview of what you'll learn in this lesson.
By the end of this video, you will have a good understanding of the following aspects of Python:
We'll explore each of these topics in detail, with plenty of illustrative examples and demonstrations to help you grasp the concepts and features of the Python programming language. Let's begin by learning how to display text in Python. To display a line of text in Python, we use the print() function. The text we want to display is passed as an argument to the print() function, enclosed in single quotes. In Python, it's customary to enclose literal strings of text in single quotes. This helps Python distinguish between text strings and other text-based programming commands.
In the example below, you'll notice a line preceding the print() function that starts with a pound sign (#). This line is called a comment. Comments are meant for human use only. They assist us in understanding the purpose of a particular section of code and make it easier for others to comprehend our code. Python ignores comments, considering them as non-executable statements. So, they don't affect the code's functionality. If you want to include a line break in your text output, you can use the escape sequence \n (new line). This will insert a line break at that point.
Now, let's see a demonstration of these techniques. In the first code cell of this notebook, we have a simple example that uses the print() function to display a line of text on the screen. When you click the run button, the text "Hello, my name is Dan!" will be displayed. In the next code cell, we'll use the \n new line symbol to display multiple lines of text with just one call to the print() function. Upon running the code, Python will print both lines of text to the screen. Now that we have covered displaying text, let's move on to variables in Python.
A variable is a symbolically named storage location in a computer's memory. Each variable has a name and a value, which can be changed as needed. Variables are incredibly useful for keeping track of data in a program. For example, you might use a variable to store the number of tickets sold for a concert. Every time an additional ticket is sold, you can update the value of the variable to reflect the correct count.
In Python, variables can hold data of any type, such as text, integers, or floats (numbers with decimals). To create a new variable, you simply assign it a name and a value. Let's take a look at a couple of examples to understand the concept better. In the first example, we declare a variable named "x" and assign it a value of 33. In the second example, we declare a variable named "current_price" and assign it a value of 42.99.
Note that the values assigned to variables can be numbers, text, or any other valid data type. Once we have assigned values to variables, we can use the print() function to display their values on the screen. In the third example, we use the print() function to display the value of the variable "x". We do the same for the variable "current_price" in the fourth example.
You can see that by printing the variables, we can view their values and work with them as needed. In addition to directly printing variables, there's another way to incorporate them into text output. We can use the format() function, which simplifies the process of combining text and variable values. In this case, you define the desired output text and indicate the positions of the variables using curly braces {} as placeholders. Inside the format() function, you provide the variables in the desired order.
Let's take a look at an example to see this in action.
In the fifth example, we have a variable named "product_name" with the value "Delicious Nachos". We want to display a message that includes the product name. We define the text "I love {}!" as our desired output, with {} as a placeholder for the variable value. Inside the format() function, we provide the variable "product_name". Upon running the code, Python substitutes the placeholder with the value of the variable and prints the result, which is "I love Delicious Nachos!". This method allows for more flexibility and dynamic text output, especially when working with multiple variables or more complex messages. Now that we've covered variables, let's move on to arithmetic operators in Python.
Python provides various arithmetic operators that allow us to perform mathematical operations on variables.
The most commonly used arithmetic operators are:
These operators can be used with numerical variables to perform calculations.
In the following example, we'll use two variables, "a" and "b", to demonstrate some of these arithmetic operators.
First, we declare a variable named "a" and assign it a value of 5. Next, we declare another variable named "b" and assign it the expression "a + 2". The expression "a + 2" adds the value of "a" (which is 5) to 2, resulting in the value of "b" being 7. We can then use the print() function to display the values of "a" and "b" on the screen.
Upon running the code, Python will evaluate the expression and display the values of "a" and "b", which are 5 and 7, respectively.
In addition to addition, we can use the subtraction operator (-) to subtract values, the multiplication operator (*) to multiply values, the division operator (/) to divide values, the exponentiation operator (**) to raise values to a power, and the modulo operator (%) to calculate the remainder of a division operation. These arithmetic operators can be combined and used in various ways to perform complex calculations.
Lastly, let's briefly discuss comparison operators in Python. Comparison operators allow us to compare two values and determine their relationship.
The most commonly used comparison operators are:
When used, these operators return a Boolean value of either True or False, indicating the result of the comparison.
For example, the expression a == b returns True if the value of "a" is equal to the value of "b" and False otherwise. In the following example, we'll compare the values of two variables, "a" and "b", using different comparison operators. We'll use the print() function to display the results of these comparisons on the screen. Upon running the code, Python will evaluate each comparison expression and display the corresponding Boolean value. You can see that the output shows the result of each comparison: True or False.
Comparison operators are useful for conditionally executing different parts of your code based on the relationship between variables.
That concludes our first lesson on the fundamentals of Python programming. In this lesson, we covered:
I hope this lesson has provided you with a solid foundation in Python programming. In the next lesson, we'll dive deeper into data types, including strings, integers, and floats.
If you have any questions or need further clarification on any of the topics covered, please feel free to ask. Thank you for watching, and I'll see you in the next lesson!
Python Fundamentals - Part 02
Python Fundamentals - Part 02
I am Dr. Soper, and today I have the pleasure of presenting the second installment of our three-part series on the fundamentals of the Python programming language.
Before we dive into today's lesson, I want to emphasize that the information I'll be sharing builds upon the knowledge and skills we developed in the previous lesson. Therefore, if you haven't had the chance to watch the previous video, I highly recommend doing so before starting this lesson on Python.
Now, let's take a moment to briefly review what you can expect to learn in this lesson.
By the end of this video, you will gain knowledge about the following aspects of Python:
Throughout the lesson, we will explore each of these topics in detail, complete with illustrative examples and demonstrations showcasing their features within the Python programming language.
Let's begin by discussing lists in Python.
In Python, a list is simply a named collection of items. These items can be of any type, including numbers, text, variables, objects, and even other lists! If a list contains other lists as its items, it is referred to as a multidimensional list.
To illustrate, let's consider a couple of examples. In the first example, we create a list called "int list" and assign the values -3, 7, 4, 0, -2, and 342 to its elements. You can envision a simple, one-dimensional list as a vector. Python identifies a list by its square brackets. To assign values to the elements of the list, we separate them with commas inside the square brackets. Remember, lists can accommodate items of any data type.
In the second example, we declare a list of planets and assign the names of all known planets in our solar system as its elements. It's worth noting that Pluto was demoted to the status of a "dwarf planet" by the International Astronomical Union in 2006, so it is not included in this list. Moving on to the third example, we declare a two-dimensional list. In other words, the elements of this list are also lists. You can think of it as a 2x3 matrix with two rows and three columns.
Now, let's observe a few demonstrations where lists are declared and utilized in Python. In the first code cell, we simply declare the three lists we discussed earlier. When we run this cell, no output will be displayed because we are merely instructing Python to create these three lists and store them in the computer's memory. In the subsequent code cell, we will explore how to access specific values within a list. However, before we proceed, it's important to understand indexing in Python.
Python employs a zero-based indexing system. This means that when dealing with collections like lists or arrays, the first item has an index of zero, the second item has an index of one, and so on. To illustrate, let's consider our "int list" as an example. This list contains six values. If we want to access, let's say, the fifth item in the list, that item would have an index of 4.
Having grasped this zero-based indexing system, the next code cell simply prints the name of the third planet in the "planets" list, which, in this case, is "Earth." Since it is the third element in the list, it should be located at index position 2. Let's click the run button to verify that the output is as expected and confirm that Earth is indeed the third rock from the sun.
Moving on, let's delve into our next topic for this lesson: NumPy arrays in Python. Now, let's move on to our next topic in this lesson: if statements and logical operators. Python provides us with the ability to use conditional statements using if statements. An if statement allows us to execute different blocks of code based on whether a certain condition is true or false. In addition, Python also provides logical operators that allow us to combine multiple conditions together.
In the first example, we have a simple if-else structure that checks if a variable named 'x' is less than 10. If the condition is true, it prints "x is less than 10" to the screen. Otherwise, if the condition is false, it prints "x is greater than or equal to 10". The else statement is used to specify the code that should be executed when the condition in the if statement is false.
We can extend this structure to handle multiple possibilities using an if-elif-else structure. In the second example, we introduce an additional condition by checking if the person's age is less than 13. Based on the person's age, the code determines whether the person is a child, teenager, or adult. The elif statement allows us to check for additional conditions before falling back to the else statement if none of the conditions are true.
Let's see some demonstrations of these if statements and logical operators in action in our Jupyter Notebook.
In the first code cell, we declare a variable named 'x' and set its value. We then use an if-else structure to print a specific message depending on whether 'x' is less than 10. Let's run the code cell and observe the output. Since the value of 'x' is currently 10, Python prints "x is greater than or equal to 10" to the screen. If we change the value of 'x' to -7 and run the code cell again, we will get a different result. After changing the value of 'x' to -7, Python now prints "x is less than 10".
In the next code cell, we implement the if-elif-else structure to determine if a person is a child, teenager, or adult based on their age. Let's run the cell and see what happens. As expected, Python prints "child" because the value of the 'age' variable is currently set to 5. If we change the value of 'age' and rerun the code cell, we will get different results based on the person's age. Moving on to the next topic, let's discuss logical operators in Python. Python provides three logical operators: 'and', 'or', and 'not'. These operators allow us to test multiple conditions simultaneously.
In the first example, we demonstrate how to use the 'and' and 'or' operators to determine if two variables, 'x' and 'y', are positive. The if statement checks if both 'x' and 'y' are positive. If at least one of the conditions is false, the code proceeds to the elif statement, which checks if either 'x' or 'y' is positive. If neither 'x' nor 'y' is positive, the else statement is executed.
In the second example, we introduce the 'not' operator, which is used to reverse or invert the result of a comparison. We check if a person's age is not less than 13. If the person is not less than 13, then they must be at least 13 years old and, hence, not a child. Otherwise, they are considered a child.
Let's see some demonstrations of these logical operators being used in Python.
In the first code cell, we use the 'and' and 'or' logical operators to determine if 'x' and 'y' are positive. We have set 'x' to 5 and 'y' to -2. Let's run the cell and observe the output. Since 'y' is negative, the condition for the 'and' operator is false. However, the condition for the 'or' operator is true because 'x' is positive. Therefore, the code prints "x is positive" to the screen. Now, let's change the value of 'x' to -3 and run the code cell again. This time, both conditions for the 'and' and 'or' operators are false, so the code proceeds to the else statement and prints "x and y are not positive".
In the next code cell, we use the 'not' operator to check if a person is not a child based on their age. We have set the 'age' variable to 10, which means the person is considered a child. Let's run the code cell and observe the output. Since the person's age is less than 13, the condition for the 'not' operator is false, and the code prints "Child" to the screen.
Now, change the value of 'age' to 18 and rerun the code cell. This time, the person's age is not less than 13, so the condition for the 'not' operator is true, and the code prints "Not a child". That concludes our lesson on lists, NumPy arrays, if statements, and logical operators in Python. I hope you found this information useful and that it helps you in your Python programming journey.
In the next and final part of this series, we will explore more advanced topics, including loops, functions, and file handling. So, stay tuned for that!
Thank you for your attention, and see you in the next lesson!
Python Fundamentals - Part 03
Python Fundamentals - Part 03
I hope you're all having a good day. It's Dr. Soper here, and I'm delighted to be back with you for our third lesson on the fundamentals of the Python programming language. In today's session, we will delve deeper into Python and explore some key concepts that will enhance your understanding and proficiency.
Before we begin, I would like to emphasize that the information presented in this lesson builds upon the knowledge and skills that we developed in the two previous lessons. If you haven't already watched those videos, I highly recommend doing so before diving into this lesson on Python.
Now, let's take a moment to discuss what you can expect to learn in this lesson. By the end of this session, you will have a comprehensive understanding of the following aspects of Python:
Throughout this lesson, we will explore these concepts through illustrative examples and demonstrations, allowing you to grasp their practical applications in the Python programming language.
Let's start by delving into the world of "for" and "while" loops in Python.
In general, loops enable us to execute a set of instructions repeatedly. Python provides two types of loops: "for" loops and "while" loops. The key distinction between the two is that "for" loops run a specific number of times, while "while" loops continue running until a certain condition is met.
Let's begin with an example of a "for" loop that prints the first 10 natural numbers, which are integers ranging from 1 to 10. To create a "for" loop, we use the keyword "for" followed by a variable name. In this case, we'll use the variable "x". As the "for" loop iterates, the variable "x" will be assigned a different value for each iteration. We then specify the set of items that will be iteratively assigned to the variable, followed by a colon. In this specific example, we create the set of items using the Python "range" function. The "range" function returns a range of numbers between a lower bound and an upper bound. Notably, the lower bound is inclusive, while the upper bound is exclusive. Therefore, the range of numbers in this example will be 1 through 10.
During the first iteration of the loop, the value of "x" will be 1. Subsequently, "x" will be assigned 2 during the second iteration, and so on until it reaches 10. Any indented lines of code following the "for" statement will be executed with each iteration of the loop. In this example, we're simply printing the value of "x", resulting in the numbers 1 through 10 being displayed.
Now, let's explore another "for" loop that prints the names of planets. In this case, we'll use the variable name "planet" to control the loop, and we'll iterate over a list of planets. As the loop progresses, the "planet" variable will be assigned each planet's name one by one, allowing us to print the name of each planet in the list.
Moving on, let's discuss nested loops in Python. With nested loops, one loop (known as the inner loop) runs inside another loop (known as the outer loop). The inner loop will execute once for each iteration of the outer loop. For instance, consider a scenario where the outer loop fills a variable named "row" with integers ranging from 0 to 1, while the inner loop fills a variable named "column" with integers ranging from 0 to 2. These numbers correspond to the row and column indexes of a two-dimensional NumPy array. As the nested loop progresses, it first prints the values of all elements in the first row of the array, and then moves on to the second row.
Finally, let's explore the "while" loop. In this type of loop, we rely on a control variable, such as "x", which is initially set to a specific value. The loop will continue executing as long as the value of "x" satisfies a certain condition. For example, we can initialize "x" to 1, and the loop will continue running as long as "x" remains below 10. In each iteration, the value of "x" will be updated, allowing us to perform specific actions within the loop until the condition is no longer met.
That wraps up our overview of "for" and "while" loops in Python. In the next segment, we will explore functions, a fundamental concept in programming that allows us to organize and reuse code effectively.
Foundations of Reinforcement Learning
Foundations of Reinforcement Learning
I am Dr. Soper, and today I will be discussing the foundations of reinforcement learning, which is a crucial area within the broader domain of artificial intelligence. Before we delve into the foundations of reinforcement learning, let's take a moment to review what you will learn in this lesson.
By the end of this video, you will have a clear understanding of the following:
Once we grasp these concepts, we will be fully equipped to start building real AI models. So, let's not waste any time and get started!
First, let's explore what is meant by "reinforcement learning." Alongside supervised learning and unsupervised learning, reinforcement learning is one of the three primary paradigms of machine learning.
In supervised learning, a machine learns a general function to predict outputs based on input-output pairs. In unsupervised learning, a machine discovers patterns in a dataset without prior knowledge about the data. On the other hand, reinforcement learning aims to train a machine to understand its environment in a way that allows it to take actions to maximize cumulative rewards. To achieve this, reinforcement learning involves finding the optimal balance between exploring the environment and exploiting what has been learned so far. Now, let's delve into the five principles underlying reinforcement learning-based AI.
The first principle we will discuss is the input and output system. This system is not unique to reinforcement learning but is fundamental to all artificial intelligence and cognitive computing systems. It involves converting inputs into outputs.
In the context of reinforcement learning, the inputs are referred to as "states," representing the state of the environment. The outputs are called "actions," answering the question, "What should I do next?" The goal of reinforcement learning is to identify an optimal policy that guides actions in each state.
Moving on, let's talk about rewards. Rewards play a crucial role in all AI and cognitive computing systems. They act as metrics that inform the system about its performance. Reward functions can be designed to maximize gains or minimize losses, depending on the problem being solved. Immediate and cumulative rewards are considered to maximize total accumulated rewards over time.
The third principle is the environment, which refers to the setting or surroundings in which the reinforcement learning system operates. The environment provides information about states and rewards. It also defines the rules of the game, determining what actions are possible at any given time. Initially, the system has no knowledge of the consequences of its actions and must experiment to learn.
Next, we have Markov Decision Processes (MDP). Named after mathematician Andrey Andreyevich Markov, MDPs provide a mathematical framework for modeling decision-making when outcomes are partly random and partly under the control of a decision maker. In reinforcement learning, the AI system acts as the decision maker operating in the environment. MDPs involve discrete units of time, and the system transitions from one state to the next based on observations, actions, rewards, and subsequent states.
Lastly, we have training mode and inference mode. Reinforcement learning systems go through two phases: training and inference. In training mode, the system learns and seeks to identify an optimal policy through multiple training cycles. It updates its policy based on the knowledge gained. In inference mode, the system has been fully trained and is deployed to perform its task using the learned policy without further updates.
Now that we have a solid understanding of the principles of reinforcement learning, we can start building real reinforcement learning models. In the next two videos, we will explore reinforcement learning models that utilize Thompson Sampling to solve practical problems. The first model will address the exploration-exploitation dilemma in the multi-armed bandit problem, and the second model will optimize results in a complex advertising campaign using simulations.
These videos will provide hands-on experience in creating AI models using Python. I hope you'll join me in these exciting adventures in cognitive computing and artificial intelligence!
That concludes our lesson on the foundations of reinforcement learning. I hope you found this information interesting, and I wish you all a great day.
Reinforcement Learning: Thompson Sampling & The Multi Armed Bandit Problem - Part 01
Reinforcement Learning: Thompson Sampling & The Multi Armed Bandit Problem - Part 01
I am Dr. Soper, and it is my pleasure to present to you the first part of our comprehensive lesson on reinforcement learning, specifically focusing on Thompson Sampling and the renowned Multi-Armed Bandit Problem.
Before we delve into the intricacies of reinforcement learning in the context of Thompson Sampling and the Multi-Armed Bandit Problem, I would like to emphasize the importance of watching the previous videos in this series. These preceding lessons serve as a foundation for the concepts we will explore today, and I highly recommend familiarizing yourself with them if you haven't already done so.
To provide a brief overview of what you can expect to learn in this lesson, let me outline the key points:
The journey ahead promises to be an enlightening one, as we uncover various applications and implications of multi-armed bandit problems. So without further ado, let's commence our exploration!
To grasp the concept of reinforcement learning within the context of the Multi-Armed Bandit Problem, it is essential to first define what this problem entails.
The Multi-Armed Bandit Problem refers to any scenario where we must determine how to allocate a fixed quantity of a limited resource among a set of competing options. The primary objective is to maximize our expected rewards while facing uncertainty.
This limited resource could take various forms, such as time, money, turns, and so on. Additionally, the rewards we might receive from each available option are not completely known. However, as we allocate resources to different options, we gradually gain a better understanding of the potential rewards associated with each.
The name "Multi-Armed Bandit Problem" originates from a gambling analogy. Imagine a gambler faced with a row of slot machines, attempting to identify the machine that maximizes her chances of winning. Slot machines are games of chance commonly found in casinos, where players deposit money and engage in turns. If luck favors the player, the machine dispenses a monetary reward, which the player hopes will exceed her initial investment.
Traditionally, slot machines were referred to as "one-armed bandits" due to the mechanical lever (arm) used to initiate the game. Therefore, when a gambler encounters several slot machines and must decide which one to play, it presents a classic Multi-Armed Bandit Problem. This problem inherently embodies the exploration-exploitation dilemma that is fundamental to reinforcement learning.
The exploration-exploitation dilemma revolves around determining how many times the gambler should play each machine. If a gambler discovers a machine that appears to offer frequent rewards, should she continue playing that particular machine (exploitation) or risk potential losses by trying other machines in the hope of finding an even more rewarding option (exploration)?
Now, you may wonder why the Multi-Armed Bandit Problem holds such great importance. Well, the truth is that multi-armed bandit problems are ubiquitous in the real world, permeating both our daily lives and business environments.
Consider the choices you encounter in your personal life. For instance, deciding whether to visit your favorite restaurant yet again on a Friday night or exploring a new eatery that you haven't experienced before. Similarly, imagine having multiple intriguing TV series available for streaming but limited free time to watch them. How do you determine which show to invest your time in?
Thompson Sampling is a popular algorithm used to address the exploration-exploitation dilemma in the Multi-Armed Bandit Problem. It provides a principled approach to balancing exploration and exploitation by leveraging Bayesian inference.
The core idea behind Thompson Sampling is to maintain a belief or probability distribution about the true underlying reward probabilities of each option (arm) in the bandit problem. This belief is updated based on the observed rewards from previous interactions with the arms.
Thompson Sampling takes a probabilistic approach to decision-making. Instead of strictly selecting the arm with the highest expected reward (exploitation) or randomly exploring arms, it samples an arm from the belief distribution in a way that balances exploration and exploitation.
Let's walk through the steps of the Thompson Sampling algorithm:
Initialization: Start by initializing the belief distribution for each arm. This distribution represents the uncertainty about the true reward probability of each arm. Typically, a Beta distribution is used as the prior distribution, as it is conjugate to the binomial distribution commonly used to model the rewards in bandit problems.
Sampling: For each round of interaction, sample a reward probability from the belief distribution for each arm. This step incorporates exploration by considering arms with higher uncertainty in their reward probabilities.
Selection: Select the arm with the highest sampled reward probability. This step incorporates exploitation by favoring arms that are likely to have higher expected rewards based on the belief distribution.
Update: Observe the reward from the selected arm and update the belief distribution for that arm based on Bayesian inference. This step updates the posterior distribution using the prior distribution and the observed reward.
By repeatedly sampling, selecting, and updating, Thompson Sampling adapts its belief distribution based on the observed rewards, gradually improving the selection of arms over time.
Thompson Sampling has proven to be an effective algorithm for solving the exploration-exploitation dilemma in various applications. It has been widely used in online advertising, clinical trials, recommendation systems, and many other domains where sequential decision-making under uncertainty is involved.
One of the key advantages of Thompson Sampling is its simplicity and ease of implementation. The algorithm does not require complex computations or tuning of hyperparameters, making it a practical choice in many real-world scenarios.
In conclusion, Thompson Sampling offers an elegant solution to the Multi-Armed Bandit Problem by balancing exploration and exploitation through Bayesian inference. Its ability to adapt to changing reward probabilities and its wide applicability make it a valuable tool in reinforcement learning and decision-making.
In the next part of our lesson, we will delve deeper into the mathematical foundations of Thompson Sampling and explore its performance guarantees. Stay tuned for an exciting journey into the intricacies of this powerful algorithm!
Reinforcement Learning: Thompson Sampling & The Multi Armed Bandit Problem - Part 02
Reinforcement Learning: Thompson Sampling & The Multi Armed Bandit Problem - Part 02
I am Dr. Soper, and I'm here to present part two of our lesson on reinforcement learning in the context of Thompson Sampling and the famous Multi-Armed Bandit Problem.
In the previous video in this series, we gained an understanding of the Multi-Armed Bandit Problem and how Thompson Sampling can be utilized to address it.
Before we proceed, I highly recommend that you watch the previous video if you haven't already, as it provides essential knowledge that will greatly benefit your understanding of this lesson.
Today, our focus will be on implementing a reinforcement learning-based AI system that utilizes Thompson Sampling to solve a real multi-armed bandit problem. To do this, we will switch over to Python and get started! To begin, let's briefly review the scenario we will be working with. Imagine that you are at a casino with $1,000 to play the slot machines. There are six slot machines available, and each turn costs $1 to play. The conversion rate, which represents the probability of winning on any given turn, varies across the machines and is unknown to you.
Your goal is to maximize your chances of winning by identifying the slot machine with the highest conversion rate as quickly as possible.
In our Python implementation, we will start by importing the required libraries. Fortunately, for this project, we only need to import numpy. Next, we will define the environment. Defining the environment is a crucial step in any reinforcement learning project. Here, we will begin by specifying the total number of turns we will play the slot machines. Since we have $1,000 and each turn costs $1, we will have a total of 1,000 turns.
We also need to define the total number of slot machines, which in this case is six. Additionally, we will create arrays to keep track of our wins and losses for each slot machine. These arrays will serve as shape parameters for the beta distribution, as discussed in the previous lesson. Furthermore, we will set a seed for the random number generator to ensure reproducibility of our results.
Next, we will generate random conversion rates between 1% and 15% for each slot machine. These conversion rates represent how often a gambler would win if they played that particular machine. Please note that in a real-world scenario, the gambler would not have access to this information. After generating the conversion rates, we will print them to the screen to observe the values stored in the computer's memory.
In the subsequent step, we will create the primary dataset. This dataset will be a matrix with one row for each turn and one column for each slot machine. In this case, our dataset will have 1,000 rows and 6 columns, representing the 1,000 turns and 6 possible slot machines. Each entry in the matrix will indicate the outcome of playing a particular slot machine on a specific turn, with "1" indicating a win and "0" indicating a loss.
To generate the dataset, we will use nested "for" loops. After generating the dataset, we will print the first 15 rows to get a sense of its structure.
Running the code cell will display a matrix filled with ones and zeros, representing wins and losses, respectively. Each row corresponds to a turn, and each column corresponds to a slot machine. For example, in the first turn, playing any slot machine would result in a loss. The dataset allows us to understand the outcomes if we were to play a specific slot machine on a given turn.
Next, we will display the means for each column in the dataset. These means represent the true conversion rates we can expect for each slot machine in our simulation. Running the code cell will show these values, which should be close to the theoretical conversion rates defined earlier, although not exact due to the random number generator and the limited number of turns in our dataset.
Now, it's time to simulate playing the slot machines 1,000 times while adhering to the constraint of playing only one machine per turn.
Using nested "for" loops, with the outer loop iterating through each turn and the inner loop iterating through each slot machine, we will conduct the simulation. At the start of each turn, we will set the "max_beta" variable to -1. This variable will help us keep track of the largest beta value observed for the current turn.
For each slot machine, we will draw a random value from the machine's beta distribution, where the shape of the distribution is determined by the number of wins and losses accumulated from playing that particular machine. We will compare the beta value of the current slot machine with the largest beta value observed thus far in the current round. If it's larger, we will update the "index_of_machine_to_play" variable with the index of the current slot machine.
After examining the beta values of all six slot machines, the "index_of_machine_to_play" variable will store the index of the machine with the highest beta value for the current turn. We will then play the selected slot machine by looking up the outcome in our dataset and recording whether it was a win or loss by incrementing the corresponding element in the "number_of_positive_rewards" or "number_of_negative_rewards" array.
This process will continue until we have completed all 1,000 rounds. Our AI system will continuously learn from the environment in each round, utilizing its accumulated knowledge to decide between exploration and exploitation. Once all 1,000 rounds are finished, we will calculate the total number of times our AI agent played each slot machine and print the results to the screen.
Running the code cell will display the number of times each slot machine was played. As you can see, our reinforcement learning-based AI system successfully identified slot machine 4 as having the highest probability of winning. It chose to play that machine on 695 out of the 1,000 turns in an attempt to maximize its cumulative rewards.
Finally, it is crucial to compare these results with a relevant baseline. In this case, the naïve approach would be to randomly select a slot machine to play for each round. The last code cell demonstrates this random sampling approach by calculating the number of wins if we were to randomly choose a slot machine to play on each turn.
Running the code cell will reveal the comparison between the Thompson Sampling approach and the random sampling approach. As you can see, the Thompson Sampling approach resulted in significantly more wins compared to the naïve, random sampling approach. Thus, our gambler would be wise to utilize Thompson Sampling!
In this lesson, we applied the knowledge gained so far in our series to solve a real-world decision problem. Specifically, we successfully built a complete reinforcement learning-based artificial intelligence system in Python that uses Thompson Sampling to address a real Multi-Armed Bandit Problem.
At this stage of our series, I hope you are starting to develop an understanding of the usefulness of AI tools in supporting decision-making. You may also be envisioning clever and innovative applications of these technologies to solve other real-world problems.
In the next video of this series, we will explore a more sophisticated version of Thompson Sampling-based reinforcement learning applied to a complex advertising campaign. I invite you to join me for that video as well.
That concludes part two of our lesson on reinforcement learning in the context of Thompson Sampling and the famous Multi-Armed Bandit Problem. I hope you found this lesson interesting, and until next time, have a great day!
A Profit-Maximizing Reinforcement Learning-Based AI System in Python
A Profit-Maximizing Reinforcement Learning-Based AI System in Python
Good day, everyone! This is Dr. Soper speaking. Today, we will delve into a comprehensive example in Python that demonstrates how a reinforcement learning-based AI system can effectively maximize corporate profits in a complex scenario involving multiple options and millions of customers.
The techniques showcased in this video have been introduced and extensively discussed in previous lessons of this series. If you haven't had the opportunity to watch those previous videos, I highly recommend doing so before proceeding with this one.
Before we dive into coding in Python, let's discuss the business problem we aim to solve in this video using an AI system based on Thompson sampling reinforcement learning.
Imagine you work for a wireless company that boasts 10 million customers. The company has decided to boost its profits by launching a smartphone upgrade program. To entice customers to upgrade their smartphones, the company's marketing team has devised eight distinct advertising campaigns. Each campaign offers customers specific features, promotions, or discounts. However, the average profit from each campaign will vary since the associated features, promotions, and discounts will incur varying costs for the company. Although the company can calculate the cost and profit per sale for each advertising campaign, it remains uncertain how effective each campaign will be. Certain campaigns may prove highly effective, while others may not yield significant results.
Our goal is to build an AI system that can maximize the company's profits for its smartphone upgrade program. It's important to note that our objective is not simply to maximize the number of customers participating in the program. Instead, we aim to optimize profits, which depend not only on the number of customers exposed to each advertising campaign but also on the effectiveness of each campaign in generating sales and the average profit per sale. With a clear understanding of the business problem, let's switch to Python and commence the implementation.
Before describing the code in this notebook, I want to inform you that a link to this notebook is available in the video description. Feel free to download a copy of the notebook to experiment with or adapt to your specific requirements. As usual, we begin by importing the necessary Python libraries. We'll require two libraries for this project: NumPy, which we'll utilize to generate random values from various probability distributions, and locale, which we'll employ to format currency values appropriately. Since our aim is to maximize profits, we'll be working with monetary values extensively in this project. Setting our current locale to the United States ensures that Python formats currency values into US Dollars and employs commas to separate large numbers. If you prefer a different currency formatting, feel free to modify the locale accordingly.
The next line of code sets a seed for the random number generator. This guarantees that you can reproduce the exact results observed in this video if you choose to download and execute the notebook.
Our next task involves defining a class to store information about the different advertising campaigns. We'll utilize this class to create objects representing each advertising campaign, which will retain the attributes or characteristics of the respective campaigns. Using campaign objects in this manner allows us to segregate all campaign-related details from the rest of the program logic, significantly enhancing our understanding of how the AI learns and makes decisions.
As you can observe, each campaign object is initialized by providing a unique campaign ID. The __init__ function subsequently assigns a random conversion rate between 1% and 20% to the campaign using NumPy to draw a random value from a uniform probability distribution. The conversion rate signifies the percentage of customers who will choose to upgrade their smartphones if exposed to a specific advertising campaign. It's worth noting that the wireless company lacks knowledge of this information. We also employ NumPy to assign a random profit per successful sale ranging from $100 to $200 for each campaign. Although we randomly assign these profit values, it is equally possible to use specific values provided by the wireless company for each advertising campaign. Finally, the class includes a method get_profit() that returns the profit per successful sale for the campaign.
Here's the code for the Campaign class:
Now that we have defined the Campaign class, we can proceed to implement the reinforcement learning algorithm based on Thompson sampling. We'll create a class called ThompsonSampling that will encapsulate the algorithm.
The ThompsonSampling class will have the following attributes and methods: num_campaigns: The number of advertising campaigns.
The methods of the ThompsonSampling class are as follows:
Here's the code for the ThompsonSampling class:
Now that we have implemented the ThompsonSampling class, we can proceed to create an instance of the class and run the algorithm. We'll set the number of campaigns to 8 and the number of trials to 1000 for this example. After running the trials, we'll display the total sales and profits for each campaign.
You can modify the code according to your specific requirements, such as the number of campaigns and trials. Additionally, you can extend the Campaign class with more attributes and methods to capture additional information about each campaign.Here's the code to run the Thompson sampling algorithm:
Foundations of Q-Learning
Foundations of Q-Learning
Good day, everyone! I am Dr. Soper, and today I am excited to delve into the foundations of Q-learning, a powerful technique within the domain of artificial intelligence. Before we embark on this learning journey, I recommend watching the previous video in this series titled "Foundations of Reinforcement Learning" if you are new to the concept.
In this lesson, we will explore the fundamental concepts of Q-learning, including its characteristics, Q-values, temporal differences, the Bellman equation, and the overall Q-learning process. By the end of this lesson, you will have a solid grasp of these concepts and be well-equipped to build AI models that rely on Q-learning. So, without further ado, let's get started!
To begin, let's briefly discuss what Q-learning entails. As mentioned earlier, Q-learning is a form of reinforcement learning, where an AI agent interacts with an environment composed of states and rewards. The agent's objective is to construct an optimal policy directly by interacting with the environment, without the need to learn an underlying mathematical model or probability distribution. Q-learning embraces trial and error, as the agent continually attempts to solve the problem using different approaches across multiple episodes while updating its policy based on the knowledge gained.
Now, let's delve into the characteristics of Q-learning models. Since Q-learning is a type of reinforcement learning, it shares the fundamental characteristics of all reinforcement learning models. These characteristics include an input and output system, rewards, environment, Markov decision processes, and both training and inference modes. In addition to these characteristics, Q-learning models have two specific attributes. Firstly, the number of possible states in Q-learning models is finite, meaning the AI agent will always find itself in one of a fixed number of possible situations. Secondly, the number of possible actions in Q-learning models is also finite, requiring the AI agent to choose from a fixed set of possible actions in each state.
Now that we have an understanding of the characteristics, let's explore a few classic Q-learning problems. One such problem is the maze, where each location represents a state, and the agent's actions involve moving up, right, down, or left. The objective is to navigate through the maze and reach the exit as quickly as possible. Another classic example is the cliff walking problem, where the agent must navigate through a grid-like environment to reach a specific location without falling off the cliff. In both scenarios, the AI agent learns about the environment by relying on and updating Q-values.
So, what are Q-values? Q-values represent the quality of a specific action (a) in a given state (s). They indicate the expected sum of future rewards if that action is taken from the current state. In other words, Q-values estimate the additional reward the agent can accumulate by taking a particular action and proceeding optimally from there. The AI agent aims to maximize its total rewards or minimize its total punishments in scenarios with negative rewards. By updating and refining the Q-values, the agent learns through both positive and negative reinforcement.
Q-values are stored in a Q-table, which has rows representing the possible states and columns representing the possible actions. The Q-table serves as the agent's policy, guiding its actions in the environment. An optimal Q-table contains values that allow the agent to select the best action in any given state, leading to the highest potential reward.
Temporal differences (TD) play a crucial role in Q-learning. TD provides a method for calculating how much the Q-value for the previous action should be adjusted based on what the agent has learned about the Q-values for the current state's actions. This adjustment helps the agent make better decisions in subsequent episodes. The TD value is computed by considering the immediate reward received for the previous action, a discount factor (gamma) that discounts future rewards, and the maximum Q-value of the next state.
The TD error, often denoted as δ, is calculated as the difference between the TD value and the current Q-value for the previous state-action pair. It represents the discrepancy between the agent's prediction and the actual reward observed in the environment. The TD error is used to update the Q-value of the previous state-action pair, thereby gradually refining the Q-values over time.
Now, let's introduce the Bellman equation, which is at the heart of Q-learning. The Bellman equation expresses the relationship between the Q-value of a state-action pair and the Q-values of its neighboring state-action pairs. It is defined as follows:
Q(s, a) = R(s, a) + γ * max[Q(s', a')]
In this equation, Q(s, a) represents the Q-value of state s and action a, R(s, a) denotes the immediate reward obtained when taking action a in state s, γ (gamma) is the discount factor that determines the importance of future rewards compared to immediate rewards, s' is the next state reached after taking action a in state s, and a' represents the best action to take in state s'.
The Bellman equation essentially states that the Q-value of a state-action pair should be equal to the immediate reward obtained plus the discounted maximum Q-value of the next state-action pairs. By iteratively applying the Bellman equation and updating the Q-values based on observed rewards and future estimates, the agent gradually converges towards an optimal policy.
Now, let's move on to the overall Q-learning process. Q-learning follows an iterative approach consisting of the following steps:
Through this iterative process, the Q-values are updated and gradually converge towards their optimal values, leading to an improved policy. The exploration-exploitation strategy allows the agent to balance between exploring new actions to discover better strategies and exploiting the learned knowledge to make decisions based on the current best actions.
It's worth mentioning that Q-learning is an off-policy learning algorithm, which means that the agent can learn from the experiences generated by a different policy. This property enables more efficient learning and greater flexibility in exploring different strategies.
In summary, Q-learning is a powerful technique within the domain of reinforcement learning. It involves learning optimal policies through trial and error without requiring a mathematical model of the environment. By using Q-values to estimate the expected sum of future rewards, updating them through temporal differences and the Bellman equation, and following an iterative learning process, the agent gradually improves its policy and achieves better performance in the given task.
I hope this lesson has provided you with a solid understanding of the foundations of Q-learning. In the next lesson, we will dive deeper into the implementation details and explore practical examples of Q-learning in action. Thank you for your attention, and I look forward to seeing you in the next video!
Q-Learning: A Complete Example in Python
Q-Learning: A Complete Example in Python
I am Dr. Soper, and today I am excited to present a detailed walkthrough of a Python-based AI system using Q-learning. This lesson builds upon the concepts discussed in the previous video, so if you are unfamiliar with Q-learning, I highly recommend watching the previous video before proceeding with this one.
In this lesson, we will address a business problem faced by a growing e-commerce company. The company is constructing a new warehouse and wants to automate the picking operations using warehouse robots. Warehouse robots are autonomous ground vehicles designed to handle various warehouse tasks, including picking.
Picking refers to the process of collecting individual items from different locations within the warehouse to fulfill customer orders. Once the items are picked from the shelves, the e-commerce company wants the robots to transport them to a specific packaging area within the warehouse for shipping.
To ensure maximum efficiency and productivity, the robots need to learn the shortest paths between the packaging area and all other locations within the warehouse where they are allowed to travel. In this video, our goal is to use Q-learning to accomplish this task.
First, let's introduce the environment for our warehouse robot scenario. The warehouse can be represented as a diagram, where each black square represents an item storage location (shelf or storage bin), and each white square represents an aisle that the robots can use for navigation. The green square indicates the location of the item packaging area.
In total, there are 121 locations in the warehouse, and each location represents a state or situation in which a robot might find itself at a particular point in time. Each state can be identified by a row and column index. For example, the item packaging area is located at position (0, 5). The black and green squares are terminal states, meaning that if the AI agent drives a robot into one of these areas during training, the training episode will be finished. The green square represents the goal state, while the black squares represent failure states since crashing the robot into an item storage area is considered a failure.
Next, let's discuss the actions available to the AI agent. The AI agent can choose one of four directions: Up, Right, Down, or Left. The goal of the agent is to learn actions that prevent the robot from crashing into item storage areas.
Now, let's explore the reward structure for our scenario. Each state (location) in the warehouse is assigned a reward value. To help the AI agent learn, negative rewards (punishments) are used for all states except the goal state. The packaging area (goal state) is assigned a reward value of 100, while all other states have a reward value of -100. The use of negative rewards encourages the AI agent to find the shortest path to the goal by minimizing its punishments. Positive rewards for white squares are not used because the agent's goal is to maximize cumulative rewards, and using positive rewards for white squares could lead to the agent aimlessly accumulating rewards without reaching the goal.
Now that we have defined the environment with its states, actions, and rewards, let's switch to Python and take a closer look at the code implementation.
We start by importing the necessary Python libraries. For this project, we only need the numpy library, which will be used for creating multidimensional arrays, generating random values, and performing numeric tasks.
The next step is to define the environment, starting with the states. The warehouse is represented as an 11x11 grid, resulting in 121 possible states. We use a three-dimensional numpy array to store the Q-values for each combination of state and action. The first two dimensions represent the rows and columns of the states, while the third dimension contains one element for each possible action the AI agent can take.
Next, we define the four actions available to the agent: Up, Right, Down, Left.
Let's continue with the code implementation.
Here's the code that implements the Q-learning algorithm for our warehouse robot scenario:
To test the learned policy, we can use the Q-table to select actions based on the highest Q-values for each state: