Tensorflow
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ROS package implementing a deep reinforcement learning algorithm for dynamic obstacle avoidance in ground robots.
Generates realistic handwriting using LSTM Mixture Density Networks implemented in TensorFlow.
TensorFlow port for AMD GPUs via ROCm, enabling machine learning on Radeon hardware.
A TensorFlow library for training, serving, and interpreting decision forest models like Random Forests and Gradient Boosted Trees.
Real-time object detection on Android using YOLO with TensorFlow, detecting 20 object classes from the Pascal VOC dataset.
A modular deep reinforcement learning framework for portfolio management, enabling algorithmic stock trading with DQN and DDPG agents.
A collection of TensorFlow practice exercises covering fundamental machine learning concepts from linear regression to CNNs.
Classify music genre from a 10-second audio stream using a convolutional neural network trained on mel-frequency spectrograms.
TensorFlow implementation of GAN-CLS algorithm for generating images from text descriptions using adversarial networks.
A quantization extension for Keras that provides drop-in replacement layers for creating quantized deep learning models in TensorFlow.
TensorFlow implementation of R-Net for machine reading comprehension on the SQuAD dataset.
A convolutional neural network model for real-time road-object segmentation from 3D LiDAR point clouds.
A TensorFlow-based image recognition system for captchas that works without image segmentation.
A web application that uses a CNN model to recognize handwritten Chinese characters from an online drawing canvas.
A U-Net implementation for brain tumor segmentation using the BRATS 2017 dataset with data augmentation and dice loss.
A unified deep learning and reinforcement learning framework supporting multiple backends and hardware platforms.
A TensorFlow library implementing constrained and interpretable lattice-based models with shape constraints like monotonicity and convexity.
A standalone reimplementation of TensorFlow for Ruby, supporting pure Ruby and OpenCL backends for machine learning.
A book teaching practical patterns for building scalable and reliable distributed machine learning systems using Kubernetes, TensorFlow, Kubeflow, and Argo Workflows.
A TensorFlow implementation of the neural style transfer algorithm that applies artistic styles to images.
A deep learning library for single-cell analysis of biological images, specializing in cell segmentation and tracking.
A deep learning toolkit for predicting regulatory activity, 3D genome folding, and mRNA half-life from DNA/RNA sequences.
An Android example project demonstrating TensorFlow integration for handwritten digit recognition using the MNIST dataset.
A toolkit that streamlines and automates the generation of model cards for machine learning models.
An AI-powered captcha solver using SimGAN to generate synthetic training data without manual labeling.
Annotated notes and summaries of the TensorFlow white paper, with SVG figures and links to documentation.
Deep learning models for crop yield prediction using remote sensing data, with CNN/LSTM and Gaussian Process approaches.
A practical demo using LSTM neural networks with TensorFlow to predict lottery numbers.
Interactive visualization tool for understanding Capsule Network (CapsNet) layers and their internal workings.
A Ruby gem providing TensorFlow bindings for basic tensor operations and machine learning tasks.
A collection of open-source machine learning and quantitative analysis models implemented in TensorFlow and PyTorch.
A neural networks toolbox for medical image analysis, providing specialized layers, models, and utilities for TensorFlow/Keras.
An easy-to-use C# deep learning library with support for multiple backends including TensorFlow, PyTorch, and CUDA/OpenCL.
A real-time RGB-based pipeline for object detection and 6D pose estimation using a denoising autoencoder trained on simulated 3D views.
TensorFlow implementation of weakly-supervised object localization using only image-level labels, without bounding box annotations.
Deploy TensorFlow graphs for fast evaluation and export to environments without TensorFlow, using NumPy.
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