کتاب اصول یادگیری عمیق، ویرایش دوم

مبانی جبر خطی برای یادگیری عمیق

Data Structures and Operations • Matrix Operations • Vector Operations • Matrix-Vector Multiplication • The Fundamental Spaces • The Column Space • The Null Space • Eigenvectors and Eigenvalues • Summary

مبانی احتمال

Events and Probability • Conditional Probability • Random Variables • Expectation • Variance • Bayes’ Theorem • Entropy, Cross Entropy, and KL Divergence • Continuous Probability Distributions • Summary

شبکه عصبی

Building Intelligent Machines • The Limits of Traditional Computer Programs • The Mechanics of Machine Learning • The Neuron • Expressing Linear Perceptrons as Neurons • Feed-Forward Neural Networks • Linear Neurons and Their Limitations • Sigmoid, Tanh, and ReLU Neurons • Softmax Output Layers • Summary

آموزش شبکه‌های عصبی پیش‌خور

The Fast-Food Problem • Gradient Descent • The Delta Rule and Learning Rates • Gradient Descent with Sigmoidal Neurons • The Backpropagation Algorithm • Stochastic and Minibatch Gradient Descent • Test Sets, Validation Sets, and Overfitting • Preventing Overfitting in Deep Neural Networks • Summary

پیاده‌سازی شبکه‌های عصبی در PyTorch

Introduction to PyTorch • Installing PyTorch • PyTorch Tensors • Tensor Init • Tensor Attributes • Tensor Operations • Gradients in PyTorch • The PyTorch nn Module • PyTorch Datasets and Dataloaders • Building the MNIST Classifier in PyTorch • Summary

فراتر از گرادیان کاهشی

The Challenges with Gradient Descent • Local Minima in the Error Surfaces of Deep Networks • Model Identifiability • How Pesky Are Spurious Local Minima in Deep Networks? • Flat Regions in the Error Surface • When the Gradient Points in the Wrong Direction • Momentum-Based Optimization • A Brief View of Second-Order Methods • Learning Rate Adaptation • AdaGrad—Accumulating Historical Gradients • RMSProp—Exponentially Weighted Moving Average of Gradients • Adam—Combining Momentum and RMSProp • The Philosophy Behind Optimizer Selection • Summary

شبکه‌های عصبی کانولوشنی

Neurons in Human Vision • The Shortcomings of Feature Selection • Vanilla Deep Neural Networks Don’t Scale • Filters and Feature Maps • Full Description of the Convolutional Layer • Max Pooling • Full Architectural Description of Convolution Networks • Closing the Loop on MNIST with Convolutional Networks • Image Preprocessing Pipelines Enable More Robust Models • Accelerating Training with Batch Normalization • Group Normalization for Memory Constrained Learning Tasks • Building a Convolutional Network for CIFAR-10 • Visualizing Learning in Convolutional Networks • Residual Learning and Skip Connections for Very Deep Networks • Building a Residual Network with Superhuman Vision • Leveraging Convolutional Filters to Replicate Artistic Styles • Learning Convolutional Filters for Other Problem Domains • Summary

یادگیری تعبیه و بازنمایی

Learning Lower-Dimensional Representations • Principal Component Analysis • Motivating the Autoencoder Architecture • Implementing an Autoencoder in PyTorch • Denoising to Force Robust Representations • Sparsity in Autoencoders • When Context Is More Informative than the Input Vector • The Word2Vec Framework • Implementing the Skip-Gram Architecture • Summary

مدل‌هایی برای تحلیل توالی

Analyzing Variable-Length Inputs • Tackling seq2seq with Neural N-Grams • Implementing a Part-of-Speech Tagger • Dependency Parsing and SyntaxNet • Beam Search and Global Normalization • A Case for Stateful Deep Learning Models • Recurrent Neural Networks • The Challenges with Vanishing Gradients • Long Short-Term Memory Units • PyTorch Primitives for RNN Models • Implementing a Sentiment Analysis Model • Solving seq2seq Tasks with Recurrent Neural Networks • Augmenting Recurrent Networks with Attention • Dissecting a Neural Translation Network • Self-Attention and Transformers • Summary

مدل‌های مولد

Generative Adversarial Networks • Variational Autoencoders • Implementing a VAE • Score-Based Generative Models • Denoising Autoencoders and Score Matching • Summary

روش‌های تفسیرپذیری

Overview • Decision Trees and Tree-Based Algorithms • Linear Regression • Methods for Evaluating Feature Importance • Permutation Feature Importance • Partial Dependence Plots • Extractive Rationalization • LIME • SHAP • Summary

شبکه‌های عصبی تقویت‌شده با حافظه

Neural Turing Machines • Attention-Based Memory Access • NTM Memory Addressing Mechanisms • Differentiable Neural Computers • Interference-Free Writing in DNCs • DNC Memory Reuse • Temporal Linking of DNC Writes • Understanding the DNC Read Head • The DNC Controller Network • Visualizing the DNC in Action • Implementing the DNC in PyTorch • Teaching a DNC to Read and Comprehend • Summary

یادگیری تقویتی عمیق

Deep Reinforcement Learning Masters Atari Games • What Is Reinforcement Learning? • Markov Decision Processes • Policy • Future Return • Discounted Future Return • Explore Versus Exploit • ϵ -Greedy • Annealed ϵ -Greedy • Policy Versus Value Learning • Pole-Cart with Policy Gradients • OpenAI Gym • Creating an Agent • Building the Model and Optimizer • Sampling Actions • Keeping Track of History • Policy Gradient Main Function • PGAgent Performance on Pole-Cart • Trust-Region Policy Optimization • Proximal Policy Optimization • Q-Learning and Deep Q-Networks • The Bellman Equation • Issues with Value Iteration • Approximating the Q-Function • Deep Q-Network • Training DQN • Learning Stability • Target Q-Network • Experience Replay • From Q-Function to Policy • DQN and the Markov Assumption • DQN’s Solution to the Markov Assumption • Playing Breakout with DQN • Building Our Architecture • Stacking Frames • Setting Up Training Operations • Updating Our Target Q-Network • Implementing Experience Replay • DQN Main Loop • DQNAgent Results on Breakout • Improving and Moving Beyond DQN • Deep Recurrent Q-Networks • Asynchronous Advantage Actor-Critic Agent • UNsupervised REinforcement and Auxiliary Learning • Summary