# binroot/tensorflow-book

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4,431 stars · 1,181 forks · Jupyter Notebook · MIT

## Links

- GitHub: https://github.com/BinRoot/TensorFlow-Book
- Homepage: http://www.tensorflowbook.com
- awesome-repositories: https://awesome-repositories.com/repository/binroot-tensorflow-book.md

## Topics

`autoencoder` `book` `classification` `clustering` `convolutional-neural-networks` `linear-regression` `logistic-regression` `machine-learning` `regression` `reinforcement-learning` `tensorflow`

## Description

This project is a collection of TensorFlow machine learning examples providing reference implementations for various neural network paradigms. It covers supervised, unsupervised, reinforcement, and sequential learning models.

The repository includes implementations for convolutional neural networks focused on image classification and ranking, as well as recurrent neural networks for time-series forecasting and sequence-to-sequence translation. It further provides examples of reinforcement learning agents trained via reward optimization and unsupervised learning techniques such as autoencoders and self-organizing maps for data clustering.

Additional capabilities cover supervised regression and classification, semantic embedding generation, and the use of hidden Markov models for sequential data modeling. The project also includes utilities for tensor operation management and model performance visualization via dashboards.

The content is delivered as a series of Jupyter Notebooks.

## Tags

### Education & Learning Resources

- [TensorFlow Recipes](https://awesome-repositories.com/f/education-learning-resources/educational-resources/reference-and-media/books-docs-reference/code-examples/tensorflow-recipes.md) — Offers a comprehensive collection of TensorFlow recipes and examples for various machine learning paradigms.
- [Computer Vision Tutorials](https://awesome-repositories.com/f/education-learning-resources/computer-vision-tutorials.md) — Provides practical examples and implementations for categorizing visual content into distinct classes. ([source](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch09_cnn))

### Artificial Intelligence & ML

- [Convolutional Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/artificial-intelligence-tooling/language-model-integrations/computer-vision-models/convolutional-neural-networks.md) — Implements convolutional neural networks for feature extraction in image and video processing tasks. ([source](https://github.com/BinRoot/TensorFlow-Book/blob/master/README.md))
- [Convolutional Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-neural-networks.md) — Provides reference implementations of convolutional neural networks for image classification and feature extraction.
- [Image Classification](https://awesome-repositories.com/f/artificial-intelligence-ml/image-classification.md) — Implements systems that assign labels or categories to images based on their visual content.
- [General Regression Models](https://awesome-repositories.com/f/artificial-intelligence-ml/linear-regression-models/general-regression-models.md) — Implements general regression models to predict continuous numeric values using neural networks.
- [Recurrent Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/recurrent-neural-networks.md) — Provides implementations of recurrent neural networks for processing sequential data and time-series prediction.
- [Reinforcement Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning.md) — Implements reinforcement learning frameworks to train agents that maximize rewards through environment interaction.
- [Reinforcement Learning Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-implementations.md) — Provides functional implementations of reinforcement learning agents using reward-based optimization and TensorFlow. ([source](https://github.com/BinRoot/TensorFlow-Book/blob/master/README.md))
- [Reinforcement Learning Reward Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-reward-systems.md) — Implements reward systems and agent training to develop optimal behaviors through environmental interaction. ([source](https://github.com/BinRoot/TensorFlow-Book))
- [Supervised Classification](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-classification.md) — Implements supervised classification workflows for categorizing data into distinct classes.
- [Supervised Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-learning.md) — Provides code for supervised learning models that perform classification and regression on labeled datasets.
- [Multiclass](https://awesome-repositories.com/f/artificial-intelligence-ml/classification/multiclass.md) — Provides implementations for assigning data to multiple discrete categories using softmax probability distributions. ([source](https://github.com/BinRoot/TensorFlow-Book))
- [Encoder-Decoder Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/encoder-decoder-architectures.md) — Implements neural network designs that map input sequences to output sequences via intermediate representations.
- [Image Ranking Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/image-ranking-systems.md) — Implements systems to order images based on visual similarity or relevance using neural networks. ([source](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch12_rank))
- [Discrete Label Assignments](https://awesome-repositories.com/f/artificial-intelligence-ml/label-assignment-strategies/entity-labeling/programmatic-weak-supervision-labelers/self-supervised-label-generators/discrete-label-assignments.md) — Implements the assignment of input feature vectors into discrete classes using trained supervised algorithms. ([source](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch04_classification))
- [Learning to Rank Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/learning-to-rank-frameworks.md) — Provides algorithms and strategies for ordering items based on relevance labels. ([source](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch12_rank))
- [Regression Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/algorithms/linear-regression-implementations/regression-implementations.md) — Implements a range of regression methods to predict continuous values by fitting linear and non-linear relationships. ([source](https://github.com/BinRoot/TensorFlow-Book/blob/master/README.md))
- [Convolution Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/neural-network-layers/convolution-layers.md) — Implements various convolution and transposed convolution layers for spatial feature extraction.
- [Recurrent Model Definitions](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/neural-network-layers/recurrent-layers/recurrent-model-definitions.md) — Provides high-level abstractions for constructing recurrent neural network architectures for temporal data.
- [Hidden Markov Models](https://awesome-repositories.com/f/artificial-intelligence-ml/markov-state-transition-models/hidden-markov-models.md) — Provides probabilistic Hidden Markov Models for estimating latent states in sequential observation data. ([source](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch06_hmm))
- [Viterbi Decoding](https://awesome-repositories.com/f/artificial-intelligence-ml/markov-state-transition-models/hidden-markov-models/viterbi-decoding.md) — Implements the Viterbi algorithm to decode the most likely sequence of hidden states from observations. ([source](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch06_hmm))
- [Regression Predictions](https://awesome-repositories.com/f/artificial-intelligence-ml/model-predictions/regression-predictions.md) — Provides regression modeling to predict continuous numerical values by fitting curves to data points. ([source](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch03_regression))
- [Autoencoders](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-implementations/autoencoders.md) — Implements autoencoder architectures that compress input into hidden representations for feature encoding.
- [Autoencoder Compression](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-model-compression/autoencoder-compression.md) — Uses autoencoder architectures to compress image data into lower-dimensional latent representations for noise removal. ([source](https://github.com/BinRoot/TensorFlow-Book))
- [Policy Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/policy-optimization.md) — Trains autonomous agents to optimize decision-making policies by maximizing cumulative reward signals.
- [Sequence Modeling](https://awesome-repositories.com/f/artificial-intelligence-ml/sequence-modeling.md) — Provides recurrent architectures designed for the analysis and prediction of time-series sequences. ([source](https://github.com/BinRoot/TensorFlow-Book/blob/master/README.md))
- [Sequence-to-Sequence Models](https://awesome-repositories.com/f/artificial-intelligence-ml/sequence-to-sequence-models.md) — Implements sequence-to-sequence models that convert sequences between different domains using encoder-decoder networks. ([source](https://github.com/BinRoot/TensorFlow-Book))
- [Sequential Data Models](https://awesome-repositories.com/f/artificial-intelligence-ml/sequential-data-models.md) — Implements sequential data models for state estimation and pattern analysis using forward and Viterbi algorithms. ([source](https://github.com/BinRoot/TensorFlow-Book))
- [Sequential Pattern Prediction](https://awesome-repositories.com/f/artificial-intelligence-ml/sequential-pattern-prediction.md) — Provides capabilities for predicting future values in a sequence based on historical temporal patterns. ([source](https://github.com/BinRoot/TensorFlow-Book))
- [Similarity-Based Clustering](https://awesome-repositories.com/f/artificial-intelligence-ml/similarity-based-clustering.md) — Implements unsupervised clustering techniques, including self-organizing maps, to organize unlabeled data based on similarity.
- [Time Series Forecasting](https://awesome-repositories.com/f/artificial-intelligence-ml/time-series-deep-learning-libraries/time-series-forecasting.md) — Implements recurrent neural networks for predicting future values based on historical temporal data sequences. ([source](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch10_rnn))
- [Unsupervised Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/unsupervised-learning.md) — Implements unsupervised learning algorithms for discovering hidden patterns and structures in unlabeled data.
- [Dense Embeddings](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-embeddings/dense-embeddings.md) — Generates dense vector representations that capture deep semantic meaning across multiple modalities.
- [Embedding Lookup Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-embeddings/dense-embeddings/token-embedding-layers/embedding-lookup-layers.md) — Provides neural network layers for retrieving dense vectors from learnable dictionaries using indices.

### Data & Databases

- [Embedding Generation](https://awesome-repositories.com/f/data-databases/vector-search/embedding-generation.md) — Provides processes for converting raw discrete data into high-dimensional vector representations. ([source](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch12_rank))

### Development Tools & Productivity

- [AI-Based Relevance Ranking](https://awesome-repositories.com/f/development-tools-productivity/search-ranking-algorithms/ai-based-relevance-ranking.md) — Implements AI-driven relevance ranking using learned model embeddings to prioritize results. ([source](https://github.com/BinRoot/TensorFlow-Book/blob/master/README.md))

### Graphics & Multimedia

- [General Data Clustering](https://awesome-repositories.com/f/graphics-multimedia/point-cloud-clustering/general-data-clustering.md) — Implements general data clustering to group unlabeled points using similarity metrics and self-organizing maps. ([source](https://github.com/BinRoot/TensorFlow-Book/blob/master/README.md))

### Scientific & Mathematical Computing

- [Forward-Pass Probability Calculations](https://awesome-repositories.com/f/scientific-mathematical-computing/numerical-mathematical-foundations/statistics-probability/probability-distributions/probability-outcome-calculation/forward-pass-probability-calculations.md) — Provides implementations of the forward algorithm to calculate the total probability of sequential observations. ([source](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch06_hmm))

### Part of an Awesome List

- [Deep Learning Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/deep-learning-frameworks.md) — Code examples accompanying a comprehensive guide to TensorFlow.
- [Learning and Reference](https://awesome-repositories.com/f/awesome-lists/ai/learning-and-reference.md) — Machine learning with TensorFlow book code.
- [Practical Learning Resources](https://awesome-repositories.com/f/awesome-lists/ai/practical-learning-resources.md) — Practical machine learning examples using TensorFlow.
- [Educational Tutorials](https://awesome-repositories.com/f/awesome-lists/learning/educational-tutorials.md) — Code examples accompanying a deep learning textbook.