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Machine Learning Course for Beginners (parts 6-10)
Machine Learning Course for Beginners
Part 6Part 7
Part 8
Part 9
Part 10
Machine Learning for Everybody – Full Course
Machine Learning for Everybody – Full Course
00:00:00 - 01:00:00 This part of the video discusses the basics of machine learning, including supervised and unsupervised learning. It also covers the different models available and how to use them. Finally, it explains how to measure the performance of a machine learning model.
01:00:00 - 02:00:00 This part explains how to use machine learning to predict outcomes of events. It discusses linear regression, logistic regression, and support vector machines. It also explains how to use a grid search to train a machine learning model.
02:00:00 - 03:00:00 This part covers the basics of machine learning, including linear regression and backpropagation. It explains how to normalize data and fit a linear regression model using the TensorFlow library.
03:00:00 - 03:50:00 This video introduces the concepts of machine learning, including supervised and unsupervised learning. It demonstrates how to use a linear regression and a neural network to make predictions. The presenter also explains how to use machine learning to cluster data.
Part 1
Part 2
Part 3
Part 4
TensorFlow 2.0 Crash Course
TensorFlow 2.0 Crash Course
The "TensorFlow 2.0 Crash Course" video covers the basics of neural networks and their architecture, with a focus on image classification. The instructor uses a snake game and fashion mnist dataset as examples to train the neural network through the process of adjusting weights and biases based on loss functions. The video shows the importance of data pre-processing and using activation functions, such as sigmoid and ReLU, to create more complex models. The speaker also emphasizes the significance of testing and training data and demonstrates how to load and modify image data for the model. Finally, the presenter shows how to define the architecture of a model in Keras, train it using compile and fit methods, and make predictions on specific images using "model.predict".
The second part of the video tutorial covers various aspects of creating a basic neural network that can classify fashion items and conduct sentiment analysis on movie reviews. Starting with loading and preparing data for training, the tutorial goes on to explain the importance of pre-processing data and normalizing the lengths of the input sequences. The tutorial then covers the creation of a suitable model architecture, including using different layers such as embedding and dense layers. Finally, the tutorial explains how to fine-tune hyperparameters, validate the model, save and load models, and evaluate the model's performance on external data. Overall, the tutorial provides an essential structure on which to build more advanced neural network knowledge. Also it covers different topics related to TensorFlow 2.0, including encoding data for the model, running a saved model for prediction, and installing TensorFlow 2.0 GPU version on Ubuntu Linux. In the encoding section, the presenter walks through the process of trimming and cleaning data to ensure proper word mapping, and creating a lookup function to encode the data for prediction. They then demonstrate the importance of preparing input data in the correct format for the model to process, before moving on to a tutorial on installing TensorFlow 2.0 GPU version on a Linux system, advising the audience to be patient due to the size of the downloads involved.
Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial
Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial
In this YouTube tutorial, the presenter covers a range of topics related to Python TensorFlow for machine learning and neural network text classification. They begin by discussing the set-up process in Google Colab and the import of necessary libraries, before focusing on the Wine Reviews dataset and using Matplotlib to plot histograms of the various features. The tutorial covers machine learning concepts, including supervised learning, and the difference between qualitative and quantitative data, as well as inputs and predictions in classification scenarios such as binary and multi-class classification. Other topics covered include loss functions, neural networks, activation functions, gradient descent, and backpropagation, as well as the implementation of neural nets within TensorFlow. Finally, the presenter implements a neural net using TensorFlow for text classification, demonstrating the benefits of using packages and libraries to increase efficiency.
The second part of the video tutorial covers various aspects of machine learning with TensorFlow in Python, specifically focusing on neural network text classification. The tutorial covers splitting data into training and testing sets, creating a simple model with TensorFlow and Keras, scaling and balancing datasets, using recurrent neural networks, and using TensorFlow Hub for text classification. The tutorial emphasizes the importance of evaluating model accuracy and the use of various neural network components, such as activation functions, dropout layers, and different types of cells. The tutorial concludes by summarizing the key takeaways, including building neural networks, using TensorFlow for text classification, and working with numerical data.
TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial (parts 1-4)
TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
00:00:00 - 01:00:00 This video provides an introduction to TensorFlow 2.0, a library for data manipulation and machine learning. The instructor explains what a tensor is and how to use tensors to store partially defined computations. He also demonstrates how to use the TF dot rank and TF dot reshape functions to control the number of dimensions in a tensor.
01:00:00 - 02:00:00 The video tutorial explains how to use linear regression to predict values in a data set. The Titanic data set is used as an example. The presenter explains how linear regression is used to predict values in a data set and how to create feature columns in a data set using TensorFlow.
02:00:00 - 03:00:00 This video tutorial covers the basics of using Python for neural networks. The video starts with a description of how a neural network is composed of layers of interconnected neurons. The video then covers how to create a random number generator and how to train a neural network. Finally, the video shows how to connect neurons and weights, how to pass information through the network, and how to calculate the output value of a neuron.
03:00:00 - 04:00:00 This video explains how to use TensorFlow to build a convolutional neural network for image recognition. The video covers the basics of convolutional neural networks, including how they work and how to use pre-trained models.
04:00:00 - 05:00:00 This video explains how to use TensorFlow to train a machine learning model that can predict the class of an image. The video covers basic concepts such as deep learning and Convolutional Neural Networks.
05:00:00 - 06:00:00 This video is a complete guide to using TensorFlow 2.0 for training neural networks. It covers the input and output shapes of the neural network, how to create a loss function, and how to use the model to predict a sequence. The video also demonstrates how to generate text with TensorFlow.
06:00:00 - 06:50:00 This video tutorial introduces the basics of TensorFlow 2.0, a powerful machine learning library. After introducing TensorFlow and its key concepts, the tutorial walks viewers through a series of tutorials on different machine learning tasks such as deep learning and reinforcement learning.
Part 1
Part 2
Part 3
Part 4
TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial (parts 5-7)
TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
Part 5
Part 6
Part 7
Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial
Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial
The Keras with TensorFlow course is focused on teaching users how to use Keras, a neural network API written in Python and integrated with TensorFlow. It covers the basics of organizing and pre-processing data, building and training artificial neural networks, and the importance of data normalization and creating validation sets. The course also provides resources such as video and text files and a guide on how to set up a GPU for increased efficiency. Users also learn how to save and load models, including options to save everything, only the architecture or just the weights. The course is suitable for those with basic programming skills and some experience with Python.
The second section of the "Keras with TensorFlow Course" covers a variety of topics, starting with loading weights into a new Keras model with the same architecture as the original model. The instructor then explains how to prepare and preprocess image data for training a convolutional neural network to classify images as either cats or dogs before moving on to building and training a Keras sequential model for the first CNN. The section includes details for training the model using a generator containing label data for validation during model fit, and how to plot a confusion matrix to evaluate model performance. It concludes by demonstrating how to fine-tune a pre-trained VGG 16 model to classify images of cats and dogs, adjust its pre-processing, and train it as well.
In the third section the instructor introduces MobileNets, a smaller and faster alternative to more complex models. They demonstrate downloading and using MobileNets in a Jupyter Notebook, organizing a data set for sign language digits, and fine-tuning the model for a new classification task. The instructor emphasizes the importance of correctly pointing the iterator to the data set's location on disk, the number of layers to freeze during training, and tuning hyperparameters to reduce overfitting issues. The final section introduces data augmentation and its potential to reduce overfitting and increase the dataset's size, and provides instructions on the different types of augmentation (e.g., shifting, flipping, rotating), saving augmented images to disk, and adding them back to the training set.
Scikit-learn Crash Course - Machine Learning Library for Python
Scikit-learn Crash Course - Machine Learning Library for Python
The "Scikit-learn Crash Course" video provides an overview of using the Scikit-learn library for machine learning in Python. The video covers data preparation, model creation and fitting, hyperparameter tuning through grid search, and model evaluation. The importance of pre-processing and transformers in enhancing model performance is emphasized, with examples of standard scaler and quantile transformer. The video also discusses the significance of model evaluation and choosing the right metric for the problem, as well as handling imbalanced datasets and unknown categories in one-hot encoding. The speaker emphasizes understanding the data set and potential biases in model predictions, and provides an example of credit card fraud detection.
The second part pf the video covers several topics, including grid search, metrics, pipelines, threshold tuning, time series modeling, and outlier handling. The instructor explores the use of custom-defined metrics and the importance of balancing precision and recall in model creation. Additionally, the voting classifier is showcased as a meta-estimator that increases model flexibility and expressiveness. The video concludes by introducing the Human Learn tool, which helps construct and benchmark rule-based systems that can be combined with machine learning algorithms. Furthermore, the FunctionClassifier tool is explored, which allows users to create customized logic as a classifier model and add behaviors such as outlier detection. Overall, the video provides a comprehensive overview of Scikit-learn and its flexible API, emphasizing the importance of understanding the relevant metrics for model creation and customization.
PyTorch for Deep Learning & Machine Learning – Full Course (parts 1-4)
PyTorch for Deep Learning & Machine Learning – Full Course
00:00:00 - 01:00:00 The "PyTorch for Deep Learning & Machine Learning" online course instructor Daniel Bourke introduces viewers to the course, which focuses on implementing machine learning concepts in PyTorch, using Python code. Key topics covered in the course include transfer learning, model deployment, and experiment tracking. The video provides an introduction to machine learning and deep learning, and their differences, with deep learning being better for complex problems that require large amounts of data, and providing insights in unstructured data. The anatomy of a neural network is explained, and the course covers the different paradigms of machine learning, such as supervised learning and transfer learning. Additionally, the video explores the potential applications of deep learning, particularly in object detection and natural language processing. Finally, the benefits of PyTorch are explained, such as standardizing research methodologies and enabling the running of machine learning code on GPUs for efficient mining of numerical calculations.
01:00:00 - 02:00:00 This part covers the basics of PyTorch, pre-processing data, building and using pre-trained deep learning models, fitting a model to a dataset, making predictions, and evaluating the model's predictions. The instructor emphasizes the importance of experimentation, visualization, and asking questions, as well as using the course's resources, including GitHub, discussions, and learnpytorch.io. Learners are also introduced to Google Colab, which provides the ability to use GPU or TPU acceleration for faster compute time, pre-installed PyTorch, and other data science packages. The course goes in-depth about tensors as the fundamental building blocks of deep learning, demonstrating how to create tensors with different dimensions and shapes, including scalar, vector, and matrix tensors. The course also covers creating random tensors, tensors of zeros and ones, and how to specify data types, devices, and requires grad parameters when creating tensors.
02:00:00 - 03:00:00 In this PyTorch tutorial, the instructor covers various aspects of tensor operations, including troubleshooting, manipulation, matrix multiplication, transposing, and aggregation. They explain the importance of maintaining the correct tensor shape and data type when working with deep learning models and demonstrate how to check and change these parameters using PyTorch commands. The tutorial includes challenges for viewers, such as practicing matrix multiplication and finding the positional min and max of tensors, and provides useful tips for avoiding common shape errors and improving performance, such as using vectorization over for loops. Additionally, the instructor introduces several helpful PyTorch functions for reshaping, stacking, squeezing, and unsqueezing tensors.
03:00:00 - 04:00:00 This part covers various topics related to PyTorch, including tensor manipulation methods such as reshape, view, stacking, squeeze, unsqueeze, and permute. The instructor provides code examples, emphasizes the importance of tensor shape manipulation in machine learning and deep learning, and challenges viewers to try indexing tensors to return specific values. The course also covers converting data between PyTorch tensors and NumPy arrays and the default data types of each, as well as the concept of reproducibility in neural networks and the use of random seeds to reduce randomness in experiments. The instructor explains how to access GPUs for faster computations and provides options such as Google Colab, Colab Pro, using your own GPU, or using cloud computing services like GCP, AWS, or Azure.
04:00:00 - 05:00:00 This part covers a wide range of topics for beginners, including how to set up GPU access with PyTorch, using the nn module in PyTorch, creating linear regression models, and more. The instructor emphasizes the importance of device agnostic code to run on different devices and to keep in mind the type of device that tensors and models are stored on. The course also includes exercises and extra curriculum to practice what has been learned, and the instructor provides tips on how to approach the exercises in Colab. The course covers training and evaluating machine learning models, splitting data into training and test sets for generalization, and visualizing data. The instructor explains how to create a linear regression model using pure PyTorch, which involves creating a constructor with the init function, creating a weights parameter using nn.parameter, and setting it to random parameters using torch.rand.
05:00:00 - 06:00:00 This part covers topics such as creating a linear regression model using PyTorch, implementing optimization algorithms like gradient descent and backpropagation through PyTorch, and understanding how to test a PyTorch model's predictive power. The importance of using the torch.inference_mode context manager when making predictions, initializing model parameters, using loss functions to measure the accuracy of a model's predictions, and optimizing model parameters to improve the model's accuracy are also discussed. Additionally, fundamental modules in PyTorch, including torch.nn, torch.nn.module, torch.optim, and torch.utils.dataset, are presented.
06:00:00 - 07:00:00 This part covers various aspects of PyTorch and machine learning. One section focused on the steps needed to build a training loop in PyTorch, including looping through the data, computing loss, and performing back propagation. The instructor emphasized the importance of choosing the appropriate loss function and optimizer and introduced the concept of gradient descent. Another section discussed the optimizer and learning rate, and how they impact the model's parameters. The video also emphasized the importance of testing and provided an overview of creating test predictions and calculating test loss. The course provides additional resources for those interested in the mathematical background of backpropagation and gradient descent.
07:00:00 - 08:00:00 This part covers multiple topics related to PyTorch. The course discusses the importance of tracking the progress of a model by keeping a record of the loss values and plotting the loss curves, which should show a decreasing trend. The instructor also explains the methods of saving and loading PyTorch models, which include saving a state dictionary, loading the model using the torch.nn.module.loadStateDict method or the torch.load method, and testing the loaded model. In later sections, the course covers creating linear regression models and using pre-existing models in PyTorch, such as the linear layer, by subclassing nn.module.
08:00:00 - 09:00:00 The part covers a wide range of topics in the realm of deep learning and machine learning. The first section covers the different layers available in torch.nn, pre-built implementations of these layers, and how to train PyTorch models using loss and optimizer functions. In subsequent sections, the instructor explains the importance of device agnostic code, saving and loading PyTorch models, and how to approach classification problems. The instructor provides examples and emphasizes the importance of numerical encoding for inputs, creating custom data, and the design complexities involved in a classification model such as the number of hidden layers, neurons, loss function, and optimizer. Finally, the instructor emphasizes that starting any machine learning problem with data is the most important step.
09:00:00 - 10:00:00 This part provides an overview of how to create and train a neural network using PyTorch for binary classification. The video covers a wide range of topics, including creating a custom dataset, checking input and output shapes, preparing data for training, creating and sending a model to a GPU, selecting an optimizer and loss function for a model, and making predictions. The course includes practical demonstrations of these concepts and aims to provide a comprehensive understanding of using PyTorch for machine learning projects.
10:00:00 - 11:00:00 This part covers several topics, including loss functions, optimizers, activation functions, training loop, and evaluation metrics. The instructor explains how to set up the loss function and optimizer, create an accuracy function, and convert raw logits to prediction probabilities and labels. The course also reviews the difference between BCE loss and BCE with logits loss, and how to calculate test loss and accuracy for a classification model. Additionally, the instructor provides tips on improving a model's performance, such as increasing the depth of the neural network, adjusting hyperparameters, and importing and using helper functions from external Python scripts.
11:00:00 - 12:00:00 In this part the instructor explains how to improve a model by changing hyperparameters such as the number of hidden units, the number of layers, and the number of epochs, and highlights the importance of testing changes one at a time to identify improvements or degradations. They also discuss the differences between parameters and hyperparameters and why it's important to make this distinction. Additionally, the instructor covers troubleshooting techniques when a model is not working and introduces the importance of nonlinearity in machine learning and deep learning models. The instructor demonstrates these concepts with various examples, including linear and nonlinear regression problems, and shows how to train and evaluate models while testing different hyperparameters and loss functions.
12:00:00 - 13:00:00 This PyTorch for Deep Learning and Machine Learning course covers basic to advanced PyTorch concepts for building models. The instructor introduces the concept of nonlinearity and demonstrates how to build classification models using nonlinearity with PyTorch. They also discuss building optimizers, loss functions, and custom activation functions. The importance of combining linear and nonlinear functions to find patterns in data by stacking layers of these functions to create a model is emphasized. The course covers both binary and multi-class classification models and explains how to set them up in PyTorch. The section concludes by demonstrating how to initialize multi-class classification models with input features and output features.
13:00:00 - 14:00:00 The instructor in this part discusses creating a linear layer stack model using PyTorch's nn.Sequential method to perform multi-class classification. They explain the creation of the loss function and optimizer using cross-entropy loss and stochastic gradient descent (SGD). The instructor also discusses dropout layers and the importance of troubleshooting machine learning code to resolve errors. They demonstrate the evaluation of the trained model using various classification evaluation methods such as accuracy, precision, recall, F1 score, confusion matrix, and classification report using torchmetrics and scikit-learn libraries. Finally, the instructor shows how to import and use pre-built metrics functions in PyTorch using the torchmetrics package and provides links to the torchmetrics module and extracurricular articles for further exploration.
14:00:00 - 15:00:00 This part covers various topics related to PyTorch and computer vision using machine learning. This includes understanding computer vision problems such as binary or multi-class classification problems, and learning how a machine learning model learns patterns from various examples of images. The video also explains PyTorch libraries, such as TorchVision, and how it contains datasets, pre-trained models, and transforms for manipulating vision data into numbers usable by machine learning models. In addition, the instructor covers the input and output shapes of the FashionMNIST dataset, the importance of visualizing and exploring datasets to identify potential issues, and provides demonstrations on how to plot and visualize image data using PyTorch and Matplotlib.
15:00:00 - 16:00:00 This video course on PyTorch for Deep Learning and Machine Learning covers the importance of preparing data and using PyTorch data sets and data loaders. The concept of mini-batches in deep learning is emphasized, and the process of creating train and test data loaders is explained using PyTorch, with the batch size hyperparameter set to 32. The importance of visualizing images in a batch is discussed, and the concept of flattening is introduced to transform multi-dimensional data into a single vector for use in a PyTorch model. The process of creating a simple neural network model with a flatten layer and two linear layers is covered, and the concept of using helper functions in Python machine learning projects is explained. Finally, the importance of timing functions for measuring how long a model takes to train and the use of TQDM for a progress bar is demonstrated.
16:00:00 - 17:00:00 This part of the course covers various topics related to PyTorch, starting with setting up the training and testing loops, troubleshooting common errors, evaluating models, and making predictions. The instructor emphasizes the importance of experimentation to find the best neural network model for a given dataset and discusses the benefits of nonlinearity for modeling nonlinear data. They also demonstrate how to create helper functions in PyTorch, optimize and evaluate loops, and perform training and testing steps. The course further explores device-agnostic code and the advantages of training models on CPUs and GPUs, concluding with a demonstration of how to measure training time on both devices.
17:00:00 - 18:00:00 This part covers many topics in deep learning and machine learning. The instructor demonstrates how to create and test a deep learning model, build a convolutional neural network (CNN) using PyTorch, and create blocks in PyTorch. Additionally, the tutorial goes over the composition of a PyTorch model and how convolutions work in practice, changes to stride and padding values in a convolutional layer, and the convolutional and max pooling layers in PyTorch. Throughout the video, the instructor shares resources, provides PyTorch code and step-by-step explanations, and offers guidance on how to create efficient and reusable code.
19:00:00 - 20:00:00 This part covers various topics such as visualizing machine learning model predictions, evaluating a multi-class classification model using confusion matrix in PyTorch, installing and upgrading packages in Google Colab, saving and loading a PyTorch model, and working with custom datasets. The course also demonstrates the process of building a computer vision model using PyTorch. The instructor emphasizes the importance of utilizing domain libraries for data loading functions and customizable data loading functions and provides examples for various categories such as vision, text, audio, and recommendation. They also provide helpful resources such as the learn pytorch.io website and the PyTorch deep learning repo.
20:00:00 - 21:00:00 The instructor of this PyTorch for Deep Learning & Machine Learning course starts by introducing the Food 101 dataset, but provides a smaller subset with three food categories and only 10% of the images as practicing with PyTorch. The instructor emphasizes the importance of having a separate directory for data and then shows how to open, visualize, and transform images using the Python image library Pillow and PyTorch methods. The section also covers data transformations with PyTorch, such as resizing and flipping images, and the instructor demonstrates how to load and transform images as tensors for machine learning models with PyTorch. The section ends with a suggestion to explore the various image transformation options available in PyTorch.
21:00:00 - 22:00:00 In this PyTorch course, the instructor explains how to load and transform image data into tensors, create and customize data loaders for training and testing, and create a custom data loading class. They demonstrate the functionality of the prebuilt data sets function, image folder, which can be used to customize transforms for all images. They also walk through the steps required to build a custom data loader, including creating a function to get class names and mappings from directories, sub-classing torch.utils.data.Dataset, and overwriting the get item and len methods. While the customization capabilities of data loaders are useful, there is a risk of writing code with errors.
22:00:00 - 23:00:00 This part of the course covers how to create and utilize custom datasets and custom loaders in PyTorch, as well as how to implement data augmentation techniques. The instructor demonstrates how to build a convolutional neural network using the PyTorch library and provides advice on areas to experiment, including hyperparameters such as kernel size and stride. The course also covers how to test the augmentation pipeline and leverage trivial augment techniques to improve model accuracy. The takeaways from the course include the flexibility of PyTorch and the ability to inherit from the base dataset class to create custom data set loading functions.
23:00:00 - 24:00:00 The instructor covers various aspects of PyTorch for deep learning and machine learning, including troubleshooting shape errors in models, using Torch Info to print summaries of PyTorch models, creating train and test step functions for evaluating performance on datasets, and combining these functions into a train function for easier model training. The instructor also discusses timing the training process of a deep learning model, plotting loss curves to track model progress over time, and evaluating model performance by experimenting with different settings, such as adding layers or adjusting the learning rate. Ultimately, these skills will provide a solid foundation for building and evaluating advanced models using PyTorch.
24:00:00 - 25:00:00 In this section of the PyTorch for Deep Learning & Machine Learning course, the instructor discusses the concepts of overfitting and underfitting in models, along with ways to deal with them, such as data augmentation, early stopping, and simplifying the model. They emphasize the importance of evaluating the model's performance over time using loss curves and provide tools for comparing different models' performance. The section also covers how to prepare custom images for prediction and demonstrates how to load an image into PyTorch using torch vision.io and convert it to a tensor. The instructor notes that before passing the image through a model, it may need to be resized, converted to float32, and put on the right device.
25:00:00 - 26:35:00 This part of the PyTorch course covers various topics such as data types and shapes, transforming image data using PyTorch's transform package and making predictions on custom data using a pre-trained model. To ensure that data is in the correct format before being fed to the model, it is important to preprocess it, scaling it to be between 0 and 1, transform it if necessary and check that it has the correct device, data type, and shape. The instructor also encourages learners to practice by doing the PyTorch custom data set exercises and offers solutions as references. The instructor also mentions that there are five additional chapters to explore in learnpytorch.io, covering topics such as transfer learning, pytorch model experiment tracking, pytorch paper replicating, and pytorch model deployment.
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Part 2
Part 3
Part 4
PyTorch for Deep Learning & Machine Learning – Full Course (description for parts 5-10)
PyTorch for Deep Learning & Machine Learning – Full Course
Part 5
Part 6
Part 7
Part 8
Part 9
Part 10