# dragen1860/tensorflow-2.x-tutorials

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6,351 stars · 2,191 forks · Jupyter Notebook

## Links

- GitHub: https://github.com/dragen1860/TensorFlow-2.x-Tutorials
- awesome-repositories: https://awesome-repositories.com/repository/dragen1860-tensorflow-2-x-tutorials.md

## Topics

`artificial-intelligence` `computer-vision` `deep-learning` `machine-learning` `neural-network` `nlp` `tensorflow` `tensorflow-2` `tensorflow-examples` `tensorflow-tutorials`

## Description

This project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a deep learning implementation guide for constructing diverse neural network architectures, including convolutional, recurrent, and generative networks.

The repository provides templates and examples for several specialized domains, including computer vision for image classification and object detection, natural language processing for text generation and language understanding, and generative AI for synthesizing data using adversarial networks and autoencoders. It also includes implementations for reinforcement learning agents and graph convolutional networks.

The content covers the broader machine learning workflow, including tensor manipulation, model optimization, training visualization, and the conversion of code into static computational graphs for improved execution speed.

The project is delivered as a series of Jupyter Notebooks.

## Tags

### Artificial Intelligence & ML

- [Deep Learning Reference Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-reference-implementations.md) — Provides standardized reference implementations for building and running diverse deep learning models.
- [Neural Network Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/neural-network-layers.md) — Provides pre-defined architectural building blocks like convolutional and recurrent layers for constructing deep learning models. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials))
- [Automatic Differentiation](https://awesome-repositories.com/f/artificial-intelligence-ml/automatic-differentiation.md) — Provides mechanisms for calculating gradients through automatic differentiation and backpropagation for model training.
- [Computer Vision Training](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-training.md) — Provides standardized training routines and scripts for image-based neural network architectures. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials))
- [Deep Learning Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-architectures.md) — Provides frameworks for constructing multi-layered neural networks using pooling and dropout. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/01-TF2.0-Overview))
- [Deep Learning Development](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-development.md) — Facilitates the design, construction, and training of multi-layered artificial neural networks.
- [Generative AI Models](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-models.md) — Implements generative AI models capable of synthesizing new content such as images and text. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials))
- [Language Model Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-architectures.md) — Develops transformer-based architectures for complex natural language understanding and generation tasks. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/blob/master/README.md))
- [Machine Learning Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-implementations.md) — Implements a wide range of neural network architectures, including convolutional, recurrent, and generative networks. ([source](https://github.com/dragen1860/tensorflow-2.x-tutorials#readme))
- [Computer Vision](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/computer-vision.md) — Provides a toolkit for training and deploying deep learning models for image processing and vision tasks.
- [Computer Vision Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/computer-vision/computer-vision-pipelines.md) — Provides automated workflows to process and manipulate visual data for machine learning tasks. ([source](https://github.com/dragen1860/tensorflow-2.x-tutorials#readme))
- [Modular Layer Compositions](https://awesome-repositories.com/f/artificial-intelligence-ml/model-composition-architectures/hybrid-layer-compositions/modular-layer-compositions.md) — Demonstrates techniques for stacking modular building blocks to construct deep learning neural network architectures.
- [Adversarial Training Procedures](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training/adversarial-training-procedures.md) — Implements training workflows for optimizing generator and discriminator networks in adversarial architectures.
- [Natural Language Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing.md) — Applies transformer-based natural language processing techniques for advanced text understanding and generation. ([source](https://github.com/dragen1860/tensorflow-2.x-tutorials#readme))
- [Generative Adversarial Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-implementations/generative-adversarial-networks.md) — Implements generative adversarial networks that use competing generator and discriminator networks to synthesize realistic imagery. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/blob/master/README.md))
- [Neural Network Model Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-model-implementations.md) — Implements convolutional, recurrent, and generative architectures to solve image classification and sequence modeling tasks. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/blob/master/README.md))
- [Neural Network Training](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-training.md) — Implements the process of adjusting internal weights via iterative cycles to train deep learning models. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/03-Play-with-MNIST))
- [Tensor Data Flows](https://awesome-repositories.com/f/artificial-intelligence-ml/tensor-data-flows.md) — Processes multi-dimensional arrays through a network of numerical operations using tensor data flows.
- [Transformer Language Models](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-language-models.md) — Implements bidirectional transformer architectures like BERT for advanced language context understanding. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/19-BERT))
- [Computer Vision Models](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-models.md) — Provides a collection of neural network architectures for image classification and object detection.
- [Object Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-detection-tracking/object-detection.md) — Deploy models that identify and locate objects within images or video frames using bounding boxes. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E4%B8%8ETensorFlow%E5%85%A5%E9%97%A8%E5%AE%9E%E6%88%98-%E6%BA%90%E7%A0%81%E5%92%8CPPT))
- [Domain-to-Domain Translation](https://awesome-repositories.com/f/artificial-intelligence-ml/domain-to-domain-translation.md) — Trains CycleGAN architectures to translate images between domains without the need for paired examples. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/15-CycleGAN))
- [Latent Space Encoders](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-models/latent-space-generative-models/latent-space-projections/latent-space-encoders.md) — Provides mechanisms to map high-dimensional input into compressed latent representations for generative reconstruction.
- [Image-to-Image Translation](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-pipelines/text-to-image-generators/image-inpainting/image-to-image-translation.md) — Trains convolutional networks to map images from one visual domain to another. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/14-Pixel2Pixel))
- [Graph Compilation Optimizations](https://awesome-repositories.com/f/artificial-intelligence-ml/graph-compilation-optimizations.md) — Pre-compile functions into computational graphs to accelerate processing and improve overall performance. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/01-TF2.0-Overview))
- [Graph Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/graph-neural-networks.md) — Provides implementations of neural network architectures designed to process and classify graph-structured data. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/20-GCN))
- [Convolutional Classifiers](https://awesome-repositories.com/f/artificial-intelligence-ml/image-classification/transformer-based-image-classifiers/convolutional-classifiers.md) — Implements deep convolutional classifiers using specialized architectures like InceptionV3 for image recognition. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/07-Inception))
- [Generative Adversarial Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/image-generation/generative-adversarial-architectures.md) — Uses convolutional generative adversarial architectures to synthesize new images based on training patterns. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/13-DCGAN))
- [Machine Learning Tutorials](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-tutorials.md) — Provides educational content and practical implementations of machine learning models using TensorFlow 2.x.
- [Machine Learning Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training.md) — Provides practical guides for training deep vision networks, including techniques like gradient clipping for convergence. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/06-CIFAR-VGG))
- [Model Performance Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/profiling-and-benchmarking/model-performance-optimization.md) — Applies regularization and learning rate adjustments to enhance model speed and accuracy. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E4%B8%8ETensorFlow%E5%85%A5%E9%97%A8%E5%AE%9E%E6%88%98-%E6%BA%90%E7%A0%81%E5%92%8CPPT))
- [Variational Autoencoders](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training/variational-autoencoders.md) — Implements variational autoencoders that map data to a continuous latent distribution for image generation. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/12-VAE))
- [Natural Language Processing Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing-implementations.md) — Implements reference models for language understanding and sequence generation, including BERT and GPT.
- [Object Detection Models](https://awesome-repositories.com/f/artificial-intelligence-ml/pytorch-computer-vision-pipelines/object-detection-models.md) — Implements object detection model architectures for identifying and locating multiple objects within images. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/16-fasterRCNN))
- [Reinforcement Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning.md) — Provides frameworks for training agents to make sequences of decisions through reinforcement learning. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/blob/master/README.md))
- [Reinforcement Learning Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-implementations.md) — Implements reinforcement learning agents using synchronous actor-critic algorithms to solve environment tasks. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/17-A2C))
- [Residual Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/residual-networks.md) — Implements residual networks using skip connections to enable the training of very deep image classifiers. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/08-ResNet))
- [Sequence-to-Sequence Mappings](https://awesome-repositories.com/f/artificial-intelligence-ml/sequence-decoding-models/sequence-to-sequence-mappings.md) — Implements architectures that map an input sequence to a target sequence via a latent representation.
- [Static Graph Compilations](https://awesome-repositories.com/f/artificial-intelligence-ml/static-graph-compilations.md) — Transforms dynamic Python computation graphs into optimized static versions to improve execution efficiency.
- [Training Progress Monitors](https://awesome-repositories.com/f/artificial-intelligence-ml/training-progress-monitors.md) — Provides interfaces for tracking real-time metrics and status during the model training process. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/01-TF2.0-Overview))

### Part of an Awesome List

- [GPT Implementations](https://awesome-repositories.com/f/awesome-lists/ai/model-implementations/gpt-implementations.md) — Provides a practical implementation of the GPT generative pre-training architecture for text generation. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/18-GPT))
- [Natural Language Models](https://awesome-repositories.com/f/awesome-lists/ai/natural-language-models.md) — Implements natural language models for text classification, sentiment analysis, and generation using transformer architectures. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials))
- [Autoencoder Reconstructions](https://awesome-repositories.com/f/awesome-lists/ai/image-reconstruction/autoencoder-reconstructions.md) — Builds autoencoders to compress and recover image data to match the original input. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/11-AE))
- [Translation Model Training](https://awesome-repositories.com/f/awesome-lists/ai/sequence-to-sequence-models/translation-model-training.md) — Provides training workflows for sequence-to-sequence models to translate text between different languages. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/21-CN-EN-Translation-BERT))

### Data & Databases

- [Image Classifiers](https://awesome-repositories.com/f/data-databases/data-categorization/classification-labelers/image-classifiers.md) — Builds automated analysis tools that categorize images into predefined labels using neural networks. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/05-FashionMNIST))
- [Array and Tensor Manipulation](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/data-transformation/array-tensor-manipulation.md) — Performs mathematical and programmatic operations for reshaping, filtering, and transforming multi-dimensional tensor data. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E4%B8%8ETensorFlow%E5%85%A5%E9%97%A8%E5%AE%9E%E6%88%98-%E6%BA%90%E7%A0%81%E5%92%8CPPT))

### Development Tools & Productivity

- [Computational Graph Compilation](https://awesome-repositories.com/f/development-tools-productivity/visual-to-code-sync-engines/code-to-graph-parsers/computational-graph-compilation.md) — Converts Python control flow and operations into optimized computational graphs to increase execution speed. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/02-AutoGraph))

### Education & Learning Resources

- [Generative Model Examples](https://awesome-repositories.com/f/education-learning-resources/use-case-examples/generative-model-examples.md) — Offers practical implementations of adversarial and variational networks for content synthesis.

### Scientific & Mathematical Computing

- [Eager Execution Modes](https://awesome-repositories.com/f/scientific-mathematical-computing/data-modeling-processing/computational-graphs/graph-based-computational-execution/eager-execution-modes.md) — Implements eager execution of computational graphs to allow immediate operation processing and dynamic debugging.

### Testing & Quality Assurance

- [Model Evaluation](https://awesome-repositories.com/f/testing-quality-assurance/model-testing/model-evaluation.md) — Provides tools for measuring model accuracy and performance using validation and test datasets. ([source](https://github.com/dragen1860/TensorFlow-2.x-Tutorials/tree/master/01-TF2.0-Overview))