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A lightweight C library for artificial neural networks

• Flexible. Model construction by building a computational graph with operators. Support RNNs, weight sharing and multiple inputs/outputs.
• Efficient. Reasonably optimized matrix product and convolution. Support mini-batching and effective multi-threading. Sometimes faster than mainstream frameworks in their CPU-only mode.

How to use our public wav2vec2 dimensional emotion model

Keras-tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation（Unfinished）

Insight Face on TensorFlow

Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

From scratch implementation of a sparse mixture of experts language model inspired by Andrej Karpathy's makemore :)

Python on Zynq FPGA for Convolutional Neural Networks

Reinforced Recommendation toolkit built around pytorch 1.7

A collection of deep learning tutorials using Tensorflow and Python

Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

A PyTorch Implementation of End-to-End Models for Speech-to-Text

Powering AWS purpose-built machine learning chips. Blazing fast and cost effective, natively integrated into PyTorch and TensorFlow and integrated with your favorite AWS services

組織横断的にチームを組成し、機械学習による成長サイクルを実現する計画を立てるワークショップ

Sample applications built using AWS' Amazon Machine Learning.

Python package for graph neural networks in chemistry and biology

This repository lets you train neural networks models for performing end-to-end full-page handwriting recognition using the Apache MXNet deep learning frameworks on the IAM Dataset.

Everything you want to know about Google Cloud TPU

Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.

Extremely simple implement for CRNN by Tensorflow