کتاب یادگیری ماشین عملی با Scikit-Learn و PyTorch

چشم‌انداز یادگیری ماشین

What Is Machine Learning? • Why Use Machine Learning? • Examples of Applications • Types of Machine Learning Systems • Training Supervision • Batch Versus Online Learning • Instance-Based Versus Model-Based Learning • Main Challenges of Machine Learning • Insufficient Quantity of Training Data • Nonrepresentative Training Data • Poor-Quality Data • Irrelevant Features • Overfitting the Training Data • Underfitting the Training Data • Deployment Issues • Stepping Back • Testing and Validating • Hyperparameter Tuning and Model Selection • Data Mismatch • Exercises

پروژه یادگیری ماشین سرتاسری

Working with Real Data • Look at the Big Picture • Frame the Problem • Select a Performance Measure • Check the Assumptions • Get the Data • Running the Code Examples Using Google Colab • Saving Your Code Changes and Your Data • The Power and Danger of Interactivity • Book Code Versus Notebook Code • Download the Data • Take a Quick Look at the Data Structure • Create a Test Set • Explore and Visualize the Data to Gain Insights • Visualizing Geographical Data • Look for Correlations • Experiment with Attribute Combinations • Prepare the Data for Machine Learning Algorithms • Clean the Data • Handling Text and Categorical Attributes • Feature Scaling and Transformation • Custom Transformers • Transformation Pipelines • Select and Train a Model • Train and Evaluate on the Training Set • Better Evaluation Using Cross-Validation • Fine-Tune Your Model • Grid Search • Randomized Search • Ensemble Methods • Analyzing the Best Models and Their Errors • Evaluate Your System on the Test Set • Launch, Monitor, and Maintain Your System • Try It Out! • Exercises

طبقه‌بندی

MNIST • Training a Binary Classifier • Performance Measures • Measuring Accuracy Using Cross-Validation • Confusion Matrices • Precision and Recall • The Precision/Recall Trade-Off • The ROC Curve • Multiclass Classification • Error Analysis • Multilabel Classification • Multioutput Classification • Exercises

آموزش مدل‌ها

Linear Regression • The Normal Equation • Computational Complexity • Gradient Descent • Batch Gradient Descent • Stochastic Gradient Descent • Mini-Batch Gradient Descent • Polynomial Regression • Learning Curves • Regularized Linear Models • Ridge Regression • Lasso Regression • Elastic Net Regression • Early Stopping • Logistic Regression • Estimating Probabilities • Training and Cost Function • Decision Boundaries • Softmax Regression • Exercises

درختان تصمیم

Training and Visualizing a Decision Tree • Making Predictions • Estimating Class Probabilities • The CART Training Algorithm • Computational Complexity • Gini Impurity or Entropy? • Regularization Hyperparameters • Regression • Sensitivity to Axis Orientation • Decision Trees Have a High Variance • Exercises

یادگیری ترکیبی و جنگل‌های تصادفی

Voting Classifiers • Bagging and Pasting • Bagging and Pasting in Scikit-Learn • Out-of-Bag Evaluation • Random Patches and Random Subspaces • Random Forests • Extra-Trees • Feature Importance • Boosting • AdaBoost • Gradient Boosting • Histogram-Based Gradient Boosting • Stacking • Exercises

کاهش ابعاد

The Curse of Dimensionality • Main Approaches for Dimensionality Reduction • Projection • Manifold Learning • PCA • Preserving the Variance • Principal Components • Projecting Down to d Dimensions • Using Scikit-Learn • Explained Variance Ratio • Choosing the Right Number of Dimensions • PCA for Compression • Randomized PCA • Incremental PCA • Random Projection • LLE • Other Dimensionality Reduction Techniques • Exercises

تکنیک‌های یادگیری بدون نظارت

Clustering Algorithms: k-means and DBSCAN • k-Means Clustering • Limits of k-Means • Using Clustering for Image Segmentation • Using Clustering for Semi-Supervised Learning • DBSCAN • Other Clustering Algorithms • Gaussian Mixtures • Using Gaussian Mixtures for Anomaly Detection • Selecting the Number of Clusters • Bayesian Gaussian Mixture Models • Other Algorithms for Anomaly and Novelty Detection • Exercises

مقدمه‌ای بر شبکه‌های عصبی مصنوعی

From Biological to Artificial Neurons • Biological Neurons • Logical Computations with Neurons • The Perceptron • The Multilayer Perceptron and Backpropagation • Building and Training MLPs with Scikit-Learn • Regression MLPs • Classification MLPs • Hyperparameter Tuning Guidelines • Number of Hidden Layers • Number of Neurons per Hidden Layer • Learning Rate • Batch Size • Other Hyperparameters • Exercises

ساخت شبکه‌های عصبی با PyTorch

PyTorch Fundamentals • PyTorch Tensors • Hardware Acceleration • Autograd • Implementing Linear Regression • Linear Regression Using Tensors and Autograd • Linear Regression Using PyTorch’s High-Level API • Implementing a Regression MLP • Implementing Mini-Batch Gradient Descent Using DataLoaders • Model Evaluation • Building Nonsequential Models Using Custom Modules • Building Models with Multiple Inputs • Building Models with Multiple Outputs • Building an Image Classifier with PyTorch • Using TorchVision to Load the Dataset • Building the Classifier • Fine-Tuning Neural Network Hyperparameters with Optuna • Saving and Loading PyTorch Models • Compiling and Optimizing a PyTorch Model • Exercises

آموزش شبکه‌های عصبی عمیق

The Vanishing/Exploding Gradients Problems • Glorot Initialization and He Initialization • Better Activation Functions • Batch Normalization • Layer Normalization • Gradient Clipping • Reusing Pretrained Layers • Transfer Learning with PyTorch • Unsupervised Pretraining • Pretraining on an Auxiliary Task • Faster Optimizers • Momentum • Nesterov Accelerated Gradient • AdaGrad • RMSProp • Adam • AdaMax • NAdam • AdamW • Learning Rate Scheduling • Exponential Scheduling • Cosine Annealing • Performance Scheduling • Warming Up the Learning Rate • Cosine Annealing with Warm Restarts • 1cycle Scheduling • Avoiding Overfitting Through Regularization • ℓ1 and ℓ2 Regularization • Dropout • Monte Carlo Dropout • Max-Norm Regularization • Practical Guidelines • Exercises

بینایی عمیق کامپیوتر با استفاده از شبکه‌های عصبی کانولوشنی

The Architecture of the Visual Cortex • Convolutional Layers • Filters • Stacking Multiple Feature Maps • Implementing Convolutional Layers with PyTorch • Pooling Layers • Implementing Pooling Layers with PyTorch • CNN Architectures • LeNet-5 • AlexNet • GoogLeNet • ResNet • Xception • SENet • Other Noteworthy Architectures • Choosing the Right CNN Architecture • GPU RAM Requirements: Inference Versus Training • Reversible Residual Networks (RevNets) • Implementing a ResNet-34 CNN Using PyTorch • Using TorchVision’s Pretrained Models • Pretrained Models for Transfer Learning • Classification and Localization • Object Detection • Fully Convolutional Networks • You Only Look Once • Object Tracking • Semantic Segmentation • Exercises

پردازش توالی‌ها با استفاده از RNN و CNN

Recurrent Neurons and Layers • Memory Cells • Input and Output Sequences • Training RNNs • Forecasting a Time Series • The ARMA Model Family • Preparing the Data for Machine Learning Models • Forecasting Using a Linear Model • Forecasting Using a Simple RNN • Forecasting Using a Deep RNN • Forecasting Multivariate Time Series • Forecasting Several Time Steps Ahead • Forecasting Using a Sequence-to-Sequence Model • Handling Long Sequences • Fighting the Unstable Gradients Problem • Tackling the Short-Term Memory Problem • Exercises

پردازش زبان طبیعی با RNN و Attention

Generating Shakespearean Text Using a Character RNN • Creating the Training Dataset • Embeddings • Building and Training the Char-RNN Model • Generating Fake Shakespearean Text • Sentiment Analysis Using Hugging Face Libraries • Tokenization Using the Hugging Face Tokenizers Library • Reusing Pretrained Tokenizers • Building and Training a Sentiment Analysis Model • Bidirectional RNNs • Reusing Pretrained Embeddings and Language Models • Task-Specific Classes • The Trainer API • Hugging Face Pipelines • An Encoder-Decoder Network for Neural Machine Translation • Beam Search • Attention Mechanisms • Exercises

ترانسفورمرها برای پردازش زبان طبیعی و چت‌بات‌ها

Attention Is All You Need: The Original Transformer Architecture • Positional Encodings • Multi-Head Attention • Building the Rest of the Transformer • Building an English-to-Spanish Transformer • Encoder-Only Transformers for Natural Language Understanding • BERT’s Architecture • BERT Pretraining • BERT Fine-Tuning • Other Encoder-Only Models • Decoder-Only Transformers • GPT-1 Architecture and Generative Pretraining • GPT-2 and Zero-Shot Learning • GPT-3, In-Context Learning, One-Shot Learning, and Few-Shot Learning • Using GPT-2 to Generate Text • Using GPT-2 for Question Answering • Downloading and Running an Even Larger Model: Mistral-7B • Turning a Large Language Model into a Chatbot • Fine-Tuning a Model for Chatting and Following Instructions Using SFT and RLHF • Direct Preference Optimization (DPO) • Fine-Tuning a Model Using the TRL Library • From a Chatbot Model to a Full Chatbot System • Model Context Protocol • Libraries and Tools • Encoder-Decoder Models • Exercises

ترانسفورمرهای بینایی و چندوجهی

Vision Transformers • RNNs with Visual Attention • DETR: A CNN-Transformer Hybrid for Object Detection • The Original ViT • Data-Efficient Image Transformer • Pyramid Vision Transformer for Dense Prediction Tasks • The Swin Transformer: A Fast and Versatile ViT • DINO: Self-Supervised Visual Representation Learning • Other Major Vision Models and Techniques • Multimodal Transformers • VideoBERT: A BERT Variant for Text plus Video • ViLBERT: A Dual-Stream Transformer for Text plus Image • CLIP: A Dual-Encoder Text plus Image Model Trained with Contrastive Pretraining • DALL·E: Generating Images from Text Prompts • Perceiver: Bridging High-Resolution Modalities with Latent Spaces • Perceiver IO: A Flexible Output Mechanism for the Perceiver • Flamingo: Open-Ended Visual Dialogue • BLIP and BLIP-2 • Other Multimodal Models • Exercises

افزایش سرعت ترانسفورمرها

خودرمزگذارها، GANها و مدل‌های انتشار

Efficient Data Representations • Performing PCA with an Undercomplete Linear Autoencoder • Stacked Autoencoders • Implementing a Stacked Autoencoder Using PyTorch • Visualizing the Reconstructions • Anomaly Detection Using Autoencoders • Visualizing the Fashion MNIST Dataset • Unsupervised Pretraining Using Stacked Autoencoders • Tying Weights • Training One Autoencoder at a Time • Convolutional Autoencoders • Denoising Autoencoders • Sparse Autoencoders • Variational Autoencoders • Generating Fashion MNIST Images • Discrete Variational Autoencoders • Generative Adversarial Networks • The Difficulties of Training GANs • Diffusion Models • Exercises

یادگیری تقویتی

What Is Reinforcement Learning? • Policy Gradients • Introduction to the Gymnasium Library • Neural Network Policies • Evaluating Actions: The Credit Assignment Problem • Solving the CartPole Using Policy Gradients • Value-Based Methods • Markov Decision Processes • Temporal Difference Learning • Q-Learning • Exploration Policies • Approximate Q-Learning and Deep Q-Learning • Implementing Deep Q-Learning • DQN Improvements • Actor-Critic Algorithms • Mastering Atari Breakout Using the Stable-Baselines3 PPO Implementation • Overview of Some Popular RL Algorithms • Exercises • Thank You!