# ageron/handson-ml2

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29,938 stars · 13,183 forks · Jupyter Notebook · Apache-2.0

## Links

- GitHub: https://github.com/ageron/handson-ml2
- awesome-repositories: https://awesome-repositories.com/repository/ageron-handson-ml2.md

## Description

This project provides a collection of practical machine learning code examples, including implementations for supervised, unsupervised, and reinforcement learning algorithms. It features deep learning model implementations for convolutional, recurrent, and generative architectures, alongside specific examples of reinforcement learning agents that maximize rewards in simulated environments.

The repository includes dedicated data preprocessing pipelines for sanitization, feature scaling, and dimensionality reduction. It also provides implementations for a wide range of specific models, such as random forests, support vector machines, autoencoders, and generative adversarial networks.

Broad capability areas cover the entire machine learning lifecycle, including data engineering, model evaluation through cross-validation, hyperparameter tuning, and MLOps deployment workflows. It also incorporates mathematical foundations like linear algebra and differential calculus.

The project is delivered as a set of Jupyter Notebooks and includes configurations for containerized environments to ensure consistent execution of the examples.

## Tags

### Artificial Intelligence & ML

- [Machine Learning Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-implementations.md) — Provides a comprehensive collection of practical code implementations for a wide range of machine learning algorithms. ([source](https://github.com/ageron/handson-ml2/blob/master/index.ipynb))
- [Convolutional Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-neural-networks.md) — Provides practical implementations of convolutional neural networks for recognizing visual patterns. ([source](https://github.com/ageron/handson-ml2/blob/master/14_deep_computer_vision_with_cnns.ipynb))
- [Data Preparation](https://awesome-repositories.com/f/artificial-intelligence-ml/data-preparation.md) — Implements data preparation utilities to handle outliers and missing values for numerical consistency. ([source](https://github.com/ageron/handson-ml2/blob/master/ml-project-checklist.md))
- [Deep Learning Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-architectures.md) — Constructs complex neural network architectures including convolutional, recurrent, and transformer layers. ([source](https://github.com/ageron/handson-ml2/blob/master/changes_in_2nd_edition.md))
- [Deep Learning Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/architectures/neural-network-components/deep-learning-implementations.md) — Provides manual implementations of deep neural network architectures from first principles. ([source](https://github.com/ageron/handson-ml2/blob/master/11_training_deep_neural_networks.ipynb))
- [Neural Network Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/neural-network-layers.md) — Demonstrates the construction of deep learning models using specialized architectural building blocks like convolutional and recurrent layers.
- [Data Preprocessing](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/data-and-checkpointing/data-preprocessing.md) — Provides comprehensive methods for cleaning, normalizing, and encoding diverse data types before model training. ([source](https://github.com/ageron/handson-ml2/blob/master/13_loading_and_preprocessing_data.ipynb))
- [Hyperparameter Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/model-fine-tuning-resources/hyperparameter-tuning.md) — Implements hyperparameter optimization via random search and Bayesian methods to minimize generalization error. ([source](https://github.com/ageron/handson-ml2/blob/master/ml-project-checklist.md))
- [Neural Network Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-implementations.md) — Provides implementations of artificial neural networks to solve classification and regression problems. ([source](https://github.com/ageron/handson-ml2/blob/master/10_neural_nets_with_keras.ipynb))
- [Reinforcement Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning.md) — Implements agents that learn optimal behaviors through interaction and reward maximization.
- [Reinforcement Learning Algorithms](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-algorithms.md) — Implements various reinforcement learning algorithms to train agents that maximize rewards in simulated environments. ([source](https://github.com/ageron/handson-ml2/blob/master/18_reinforcement_learning.ipynb))
- [Reinforcement Learning Training Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-training-utilities.md) — Implements reinforcement learning agents that learn optimal behaviors through interaction with simulated environments. ([source](https://github.com/ageron/handson-ml2/blob/master/changes_in_2nd_edition.md))
- [Supervised Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-learning.md) — Implements a variety of supervised learning algorithms for classification and regression tasks. ([source](https://github.com/ageron/handson-ml2/blob/master/changes_in_2nd_edition.md))
- [Unsupervised Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/unsupervised-learning.md) — Provides implementations of unsupervised learning algorithms for discovering hidden patterns in unlabeled data. ([source](https://github.com/ageron/handson-ml2/blob/master/09_unsupervised_learning.ipynb))
- [Autoencoders](https://awesome-repositories.com/f/artificial-intelligence-ml/autoencoders.md) — Implements autoencoders for unsupervised learning of efficient data encodings through compression and reconstruction. ([source](https://github.com/ageron/handson-ml2/blob/master/17_autoencoders_and_gans.ipynb))
- [Decision Trees](https://awesome-repositories.com/f/artificial-intelligence-ml/decision-trees.md) — Implements classification and regression trees to partition data based on feature thresholds. ([source](https://github.com/ageron/handson-ml2/blob/master/06_decision_trees.ipynb))
- [Dimensionality Reduction](https://awesome-repositories.com/f/artificial-intelligence-ml/dimensionality-reduction.md) — Implements dimensionality reduction techniques to simplify complex datasets and visualize hidden patterns. ([source](https://github.com/ageron/handson-ml2/blob/master/08_dimensionality_reduction.ipynb))
- [Ensemble Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/ensemble-learning.md) — Provides practical implementations of ensemble learning models to improve predictive accuracy and reduce overfitting. ([source](https://github.com/ageron/handson-ml2/blob/master/07_ensemble_learning_and_random_forests.ipynb))
- [Generative Adversarial Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-adversarial-networks.md) — Implements generative adversarial networks that use competing generator and discriminator networks to synthesize data.
- [Generative Models](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-models.md) — Develops generative models such as autoencoders and GANs to synthesize or compress complex data. ([source](https://github.com/ageron/handson-ml2/blob/master/changes_in_2nd_edition.md))
- [Linear Regression](https://awesome-repositories.com/f/artificial-intelligence-ml/linear-regression.md) — Implements linear regression algorithms to find the best-fit line for numerical data. ([source](https://github.com/ageron/handson-ml2/blob/master/04_training_linear_models.ipynb))
- [Custom Neural Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-implementations/custom-neural-architectures.md) — Demonstrates how to construct tailored neural network architectures using low-level components. ([source](https://github.com/ageron/handson-ml2/blob/master/12_custom_models_and_training_with_tensorflow.ipynb))
- [Tensor Computing Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/hardware-and-acceleration/tensor-computing-libraries.md) — Utilizes tensor computing libraries for multi-dimensional array operations and optimized linear algebra.
- [Model Deployment](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning/pre-trained-model-zoos/model-deployment.md) — Provides workflows for transitioning trained models into production environments for inference. ([source](https://github.com/ageron/handson-ml2/blob/master/changes_in_2nd_edition.md))
- [Sequential Pattern Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/speech-processing/sequence-to-sequence-tasks/sequential-pattern-analysis.md) — Implements recurrent and convolutional networks for analyzing and predicting patterns in sequences. ([source](https://github.com/ageron/handson-ml2/blob/master/15_processing_sequences_using_rnns_and_cnns.ipynb))
- [MLOps Platforms](https://awesome-repositories.com/f/artificial-intelligence-ml/mlops-platforms.md) — Demonstrates practical workflows for deploying and monitoring machine learning models in production.
- [Model Deployment Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/model-deployment-pipelines.md) — Integrates models into production pipelines with alerts for performance degradation. ([source](https://github.com/ageron/handson-ml2/blob/master/ml-project-checklist.md))
- [Hyperparameter Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/training-efficiency/hyperparameter-optimization.md) — Includes methods for searching and selecting optimal hyperparameter configurations to minimize generalization error.
- [Cross-Validation Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/model-validation-tools/cross-validation-utilities.md) — Implements cross-validation techniques to iteratively train and test models for more robust performance estimation.
- [Natural Language Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing.md) — Implements natural language processing techniques using recurrent networks and attention mechanisms. ([source](https://github.com/ageron/handson-ml2/blob/master/16_nlp_with_rnns_and_attention.ipynb))
- [Random Forest Ensembles](https://awesome-repositories.com/f/artificial-intelligence-ml/random-forest-ensembles.md) — Provides implementations of random forest classifiers to create stable and generalized predictive models. ([source](https://github.com/ageron/handson-ml2/blob/master/07_ensemble_learning_and_random_forests.ipynb))
- [Support Vector Machines](https://awesome-repositories.com/f/artificial-intelligence-ml/support-vector-machines.md) — Implements support vector machines to establish optimal decision boundaries between data classes. ([source](https://github.com/ageron/handson-ml2/blob/master/05_support_vector_machines.ipynb))
- [Clustering and Density Estimation](https://awesome-repositories.com/f/artificial-intelligence-ml/unsupervised-learning/clustering-and-density-estimation.md) — Implements specific unsupervised techniques including clustering, anomaly detection, and density estimation. ([source](https://github.com/ageron/handson-ml2/blob/master/changes_in_2nd_edition.md))

### Data & Databases

- [Data Preprocessing Pipelines](https://awesome-repositories.com/f/data-databases/data-preprocessing-pipelines.md) — Provides dedicated pipelines for data sanitization, scaling, and dimensionality reduction.
- [Data Transformation Pipelines](https://awesome-repositories.com/f/data-databases/data-transformation-pipelines.md) — Provides data transformation pipelines for sanitization, scaling, and encoding to prepare feature sets for training.
- [Data Exploration](https://awesome-repositories.com/f/data-databases/data-analysis-visualization/analytical-platforms-engines/data-exploration.md) — Demonstrates how to analyze attribute types and distributions to identify effective data transformations. ([source](https://github.com/ageron/handson-ml2/blob/master/ml-project-checklist.md))

### Development Tools & Productivity

- [Interactive Notebooks](https://awesome-repositories.com/f/development-tools-productivity/interactive-notebooks.md) — Delivers all practical examples as interactive notebooks combining live code, narrative text, and visualizations.
- [Machine Learning Pipelines](https://awesome-repositories.com/f/development-tools-productivity/task-pipeline-managers/machine-learning-pipelines.md) — Provides pipelines for sanitization, scaling, and feature engineering to prepare data for modeling.

### Testing & Quality Assurance

- [Model Evaluation](https://awesome-repositories.com/f/testing-quality-assurance/model-testing/model-evaluation.md) — Implements cross-validation techniques to evaluate and compare different model architectures. ([source](https://github.com/ageron/handson-ml2/blob/master/ml-project-checklist.md))

### Part of an Awesome List

- [Numerical Computation](https://awesome-repositories.com/f/awesome-lists/devtools/numerical-computation.md) — Performs array-based mathematical operations and optimized linear algebra routines for large-scale data processing. ([source](https://github.com/ageron/handson-ml2/blob/master/tools_numpy.ipynb))

### Security & Cryptography

- [Data Acquisition & Sanitization](https://awesome-repositories.com/f/security-cryptography/data-sanitization/data-acquisition-sanitization.md) — Provides workflows for collecting raw data and sanitizing it for use in machine learning models. ([source](https://github.com/ageron/handson-ml2/blob/master/ml-project-checklist.md))