TensorFlow

A collection of TensorFlow tutorials covering basics to advanced neural network architectures with Python code and notebooks.

A collection of simple tutorials introducing deep learning concepts using Google's TensorFlow framework.

A collection of beginner-friendly TensorFlow tutorials with accompanying YouTube videos covering deep learning fundamentals and advanced topics.

A comprehensive collection of TensorFlow tutorials and examples for beginners, covering both TF v1 and v2 with clear explanations.

A collection of beginner-friendly TensorFlow tutorials using Jupyter Notebook, covering deep learning fundamentals and practical applications.

A collection of TensorFlow practice exercises covering fundamental machine learning concepts from linear regression to CNNs.

Official TensorFlow Python wheels for Raspberry Pi, enabling machine learning on edge devices.

Human Activity Recognition using TensorFlow and LSTM RNNs on smartphone sensor data to classify six movement types.

A TensorFlow-based educational project for learning seq2seq RNNs through signal forecasting exercises.

TensorFlow implementation of Single Image Random Dot Stereograms for 3D data visualization in 2D images.

A comprehensive guide to TensorFlow 2.x covering fundamentals, best practices, and advanced topics for efficient machine learning development.

A TensorFlow project template with a well-designed folder structure and OOP design to accelerate deep learning development.

TensorFlow implementation of unsupervised cross-domain image generation for transferring images between domains like SVHN to MNIST.

TensorFlow implementation of an attention-based neural image caption generator that focuses on relevant image parts while generating words.

A TensorFlow implementation of neural style transfer for images and videos, blending content and artistic styles using convolutional neural networks.

Implementation of SRGAN for photo-realistic single image super-resolution using generative adversarial networks.

A high-level builder API for TensorFlow that enables fluent, chainable neural network construction.

A TensorFlow implementation of neural style transfer that transforms images by applying artistic styles from one image to another.

TensorFlow implementation of AlexNet with 3D convolutional layers for volumetric image recognition.

Annotated notes and summaries of the TensorFlow white paper, with SVG figures and links to documentation.

A TensorFlow implementation of the neural style transfer algorithm that applies artistic styles to images.

Generates realistic handwriting using LSTM Mixture Density Networks implemented in TensorFlow.

TensorFlow implementation of Neural Turing Machines with LSTM controllers, supporting multiple read/write heads.

A command-line utility that uses convolutional neural networks to search and filter videos based on objects and places that appear in them.

Neural machine translation between Shakespearean and modern English using TensorFlow.

A TensorFlow implementation of a neural conversational model (seq2seq) for building deep learning chatbots.

A minimal 200-line implementation of a sequence-to-sequence chatbot using TensorLayer and TensorFlow.

A minimal implementation of Deep Convolutional Generative Adversarial Networks (DCGAN) using TensorLayerX for generating realistic images.

TensorFlow implementation of GAN-CLS algorithm for generating images from text descriptions using adversarial networks.

An implementation of unsupervised image-to-image translation using Generative Adversarial Networks (GANs).

A TensorLayer re-implementation of CycleGAN with improvements like resize-convolution and instance normalization.

A deep learning model using generative adversarial networks for fast compressed sensing MRI reconstruction.

A neural network that automatically adds color to grayscale images using deep learning techniques.

TensorFlow implementation of weakly-supervised object localization using only image-level labels, without bounding box annotations.

A TensorFlow implementation of Dynamic Capacity Networks, which reduces computations by applying high-capacity networks to selected input patches.

TensorFlow and NumPy implementations of HMM Viterbi and forward/backward algorithms for sequence modeling.

Train neural networks with OpenStreetMap data and satellite imagery to classify roads and map features.

TensorFlow implementation of Deep Q-Networks (DQN) for human-level control in reinforcement learning environments.

A TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers, offering customizable neural layers.

A simple implementation of the DAGGER imitation learning algorithm for autonomous steering control in the Torcs racing simulator.

A reinforcement learning toolbox implementing TRPO and A3C algorithms for continuous action spaces.

A TensorFlow implementation of convolutional highway networks for deep learning experiments.

TensorFlow implementation of hierarchical attention networks for document classification using GRU cells and attention mechanisms.

A TensorFlow implementation of a convolutional neural network for sentence classification based on Yoon Kim's paper.

A TensorFlow implementation of End-To-End Memory Networks with a scikit-learn-like interface for bAbI tasks.

TensorFlow implementation of character-aware neural language models using CNN, highway networks, and LSTM.

A TensorFlow implementation of YOLO for real-time object detection, supporting weight conversion, training, and mobile deployment.

A TensorFlow implementation of DeepMind's WaveNet neural network for generating raw audio waveforms.

A TensorFlow implementation of the Mnemonic Descent Method for end-to-end face alignment.

TensorFlow implementation of a generative adversarial network for video generation with scene dynamics.

A U-Net implementation for brain tumor segmentation using the BRATS 2017 dataset with data augmentation and dice loss.

A TensorFlow/TensorLayer implementation of Spatial Transformer Networks for learning image transformations like scaling, cropping, and rotation.

A TensorFlow implementation of hierarchical attentive recurrent neural networks for single object tracking in videos.

Graph embedding framework implementing TransE, TransH, TransR, TransD, and TransSparse models for knowledge graph representation learning.

A TensorFlow implementation of Attend, Infer, Repeat (AIR), a generative model for fast scene understanding by reconstructing objects sequentially.

A TensorFlow implementation of fastText for embedding-based text classification with support for character ngrams and distributed training.

Classify music genre from a 10-second audio stream using a convolutional neural network trained on mel-frequency spectrograms.

A composable, modular, and scalable machine learning toolkit for building AI platforms on Kubernetes.

High-level TensorFlow network definitions with pre-trained weights for easy integration into existing ML workflows.

A Keras implementation of Ladder Networks for semi-supervised learning, achieving 98% accuracy on MNIST with only 100 labeled examples.

A general-purpose U-Net implementation in Keras for image segmentation tasks.

A collection of classical machine learning models implemented with TensorFlow 2 and Keras, featuring TensorBoard visualizations.

TensorFlow implementation of YOLO for real-time object detection using pretrained YOLO_small, YOLO_tiny, and YOLO_face models.

Real-time object detection on Android using YOLO with TensorFlow, detecting 20 object classes from the Pascal VOC dataset.

A research project exploring machine learning for generating music, images, and art using deep learning and reinforcement learning.

A TensorFlow library implementing constrained and interpretable lattice-based models with shape constraints like monotonicity and convexity.

A Ruby API for TensorFlow, enabling machine learning and deep learning within Ruby applications.

A modular deep learning library providing a higher-level API for TensorFlow to speed up experimentation.

A TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers with customizable neural layers.

TensorFlow binding for Apache Spark DataFrames, enabling TensorFlow program execution on Spark data.

A modular TensorFlow library for applied reinforcement learning with a focus on flexible design and practical usability.

Enables distributed TensorFlow training and inferencing on Apache Spark and Hadoop clusters with minimal code changes.

Convert Caffe deep learning models to TensorFlow format for deployment and inference.

Run trained Keras models directly in the browser with GPU acceleration via WebGL.

A simple interface for converting ROOT nTuples to NumPy arrays and using them in TensorFlow for neural network training.

A TensorFlow 2 library providing simple, composable abstractions for machine learning research via the snt.Module concept.

A high-performance neural network training interface for TensorFlow focused on speed, flexibility, and reproducible research.

A framework for encrypted machine learning in TensorFlow using secure multi-party computation and homomorphic encryption.

Convert PyTorch models to Keras (TensorFlow backend) for deployment and interoperability.

Convert deep neural network models from Apache MXNet Gluon to Keras format for interoperability between frameworks.

A Python library for machine learning on graphs and networks, offering state-of-the-art algorithms for tasks like node classification and link prediction.

A collection of plug-and-play TensorFlow/Keras deep learning models and architectures for easy integration.

A unified deep learning and reinforcement learning framework supporting multiple backends and hardware platforms.

A lightweight, high-performance C++ wrapper for TensorFlow that simplifies development with a modern API.

A collection of libraries to optimize AI model performance through inference, infrastructure, and fine-tuning techniques.

A web-based IDE for machine learning and data science with pre-installed libraries and tools, deployable via Docker.

A single-script tool to set up and maintain TensorFlow machine learning projects with environment management and linting.