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Convolutional Neural Networks Explained (CNN Visualized)
Convolutional Neural Networks Explained (CNN Visualized)
The video explains convolutional neural networks (CNNs) and their structure for image recognition, using the example of number recognition.
The first hidden layer, the convolutional layer, applies kernels or feature detectors to transform the input pixels and highlight features, such as edges, corners, and shapes, leading to multiple feature maps that undergo a non-linearity function.
The newly produced feature maps are used as inputs for the next hidden layer, a pooling layer, that reduces the dimensions of the feature maps and helps build further abstractions towards the output by retaining significant information. The pooling layer reduces overfitting while speeding up calculation through downsampling feature maps. The second component of CNN is the classifier, which consists of fully connected layers that use high-level features abstracted from the input to correctly classify images.
Why do Convolutional Neural Networks work so well?
Why do Convolutional Neural Networks work so well?
The success of convolutional neural networks (CNNs) lies in their use of low-dimensional inputs, making them easily trainable with just tens of thousands of labeled examples.
Success is also achieved through the use of convolutional layers that output only small amounts of useful information due to the compressibility of patches of pixels that exist in the real world but not necessarily in artificially rearranged images. Although CNNs are used to perform various image processing tasks, their success cannot be fully attributed to their learning ability, since both humans and neural networks cannot learn from high dimensional data. Instead, hard-coded spatial structures in their architecture must exist before training in order to "see" the world.
Can A.I. Be Taught The Difference Between Right and Wrong? [4K] | ARTIFICIAL INTELLIGENCE | Spark
Can A.I. Be Taught The Difference Between Right and Wrong? [4K] | ARTIFICIAL INTELLIGENCE | Spark
The video discusses the current state and potential of AI and robotics, covering topics such as deep learning, robot capabilities, potential impact in various industries, ethics, emotional intelligence, and limitations.
While AI has transitioned seamlessly into various fields, experts still believe that humans are necessary to handle unexpected situations and ethical dilemmas. The fear of weaponizing robots and AI's potential to develop without human control are also discussed. However, AI's potential for creativity and emotional intelligence, as demonstrated by Yumi, is something to look forward to in the future. The key challenge is to gain public trust in AI's reliability and safety as its integration becomes increasingly vital in our society.
Jensen Huang — NVIDIA's CEO on the Next Generation of AI and MLOps
Jensen Huang — NVIDIA's CEO on the Next Generation of AI and MLOps
NVIDIA CEO Jensen Huang explains the company's history of focusing on machine learning, beginning with the acceleration of neural network models for the ImageNet competition. He discusses NVIDIA's full-stack computing type and its success in building a GPU that is universal for different applications. Huang predicts the growth of AI in chip manufacturing and design and the potential for deep learning algorithms to simulate climate change mitigation strategies. He also discusses the importance of MLOps and compares the refining process for machine learning to a factory. Lastly, Huang shares his excitement for the future of innovation and creativity in the virtual world.
him more vulnerable and attract more criticism, he sees it as a way to refine his ideas and make more informed decisions. Jensen also talks about his approach to leadership, stating that his behavior and way of tackling problems remain consistent regardless of the company's stock performance. As a public company, he acknowledges the outside pressure to succeed, but he believes that if they are clear in expressing their vision and why they're doing something, people are willing to give it a shot.
OpenAI CEO, CTO on risks and how AI will reshape society
OpenAI CEO, CTO on risks and how AI will reshape society
OpenAI CEO and CTO Sam Altman tells ABC News’ Rebecca Jarvis that AI will reshape society and acknowledges the risks: “I think people should be happy that we are a l...discuss the potential impact of AI on society, stressing the need for responsible development that aligns with human values and avoids negative consequences such as eliminating jobs or increasing racial bias.
They assert that although AI has potential dangers, not using this technology could be more dangerous. The CEOs also highlight the importance of human control and public input in defining guard rails for AI, as well as the potential for AI to revolutionize education and provide personalized learning for every student. While acknowledging the risks associated with AI, they express optimism about its potential benefits in areas like healthcare and education.
Neural Networks are Decision Trees (w/ Alexander Mattick)
Neural Networks are Decision Trees (w/ Alexander Mattick)
Neural Networks are Decision Trees are a type of machine learning algorithm that is suited for problems that have well-defined statistics. They are especially good at learning on tabular data, which is a type of data that is easy to store and understand.
In this video, Alexander Mattick from the University of Cambridge discusses a recent paper published on Neural Networks and Decision Trees.
This is a game changer! (AlphaTensor by DeepMind explained)
This is a game changer! (AlphaTensor by DeepMind explained)
AlphaTensor is a new algorithm that can speed up matrix multiplication by decomposing it into a lower rank tensor. This is a breakthrough in matrix multiplication that can potentially save a lot of time and energy.
This video explains how AlphaTensor, a tool developed by Google's DeepMind, could be a game changer in the field of artificial intelligence.
Google’s AI Sentience: How Close Are We Really? | Tech News Briefing Podcast | Wall Street Journal
Google’s AI Sentience: How Close Are We Really? | Tech News Briefing Podcast | Wall Street Journal
The controversy over whether Google's AI system, Lambda, could become sentient is discussed in this segment. While experts have dismissed the idea, there are concerns about the perception that it could happen and the potential dangers posed by policymakers and regulations. The discussion highlights that there is more focus on the consequences of AI systems being hyper-competent and discriminating or manipulating, rather than the harm that could come from them simply not working properly.
The Neural Network, A Visual Introduction | Visualizing Deep Learning, Chapter 1
The Neural Network, A Visual Introduction | Visualizing Deep Learning, Chapter 1
The video provides a clear visual introduction to the basic structure and concepts of a neural network, including artificial neurons, activation functions, weight matrices, and bias vectors.
It demonstrates the use of a neural network to find patterns in data, determining boundary lines and complex decision boundaries in datasets. The importance of the activation function is also highlighted, as it helps to tackle more complicated decision boundaries and classify data.
The video concludes by recognizing the support of deep learning pioneers and exploring what a trained neural network looks like.
Visualizing Deep Learning 2. Why are neural networks so effective?
Visualizing Deep Learning 2. Why are neural networks so effective?
This video explores the effectiveness of neural networks, diving into the softmax function, decision boundaries, and input transformations. The video explains how the signoid function can be used to assign a probability to each output instead of the traditional argmax function.
It then demonstrates the use of the softmax function to cluster similar points and make them linearly separable during training. However, when moving outside the initial training region, the neural network extends the decision boundaries linearly, leading to inaccurate classifications.
The video also explains how the first neuron in a neural network can be translated into a plane equation for decision boundaries and demonstrates an interactive tool to visualize the transformation of handwritten digits through a neural network.