PyTorch is a deep learning framework that puts Python first. It is a python package that provides Tensor computation (like numpy) with strong GPU acceleration, Deep Neural Networks built on a tape-based autograd system. You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed. 
 
Usually one uses PyTorch either as a replacement for numpy to use the power of GPUs, a deep learning research platform that provides maximum flexibility and speed. PyTorch provides Tensors that can live either on the CPU or the GPU, and accelerate compute by a huge amount.

PyTorch is a deep learning framework that puts Python first. It is a python package that provides Tensor computation (like numpy) with strong GPU acceleration, Deep Neural Networks built on a tape-based autograd system. You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed. 

PyTorch - Tensors and Dynamic neural networks in Python  with strong GPU acceleration

SentencePiece is an unsupervised text tokenizer and detokenizer mainly for Neural Network-based text generation systems where the vocabulary size is predetermined prior to the neural model training. SentencePiece implements subword units (e.g., byte-pair-encoding (BPE) and unigram language model with the extension of direct training from raw sentences. SentencePiece allows us to make a purely end-to-end system that does not depend on language-specific pre postprocessing.
 
SentencePiece is a re-implementation of sub-word units, an effective way to alleviate the open vocabulary problems in neural machine translation. Neural Machine Translation models typically operate with a fixed vocabulary. Unlike most unsupervised word segmentation algorithms, which assume an infinite vocabulary, SentencePiece trains the segmentation model such that the final vocabulary size is fixed, e.g., 8k, 16k, or 32k.

SentencePiece is an unsupervised text tokenizer and detokenizer mainly for Neural Network-based text generation systems where the vocabulary size is predetermined prior to the neural model training. SentencePiece implements subword units (e.g., byte-pair-encoding (BPE) and unigram language model with the extension of direct training from raw sentences. SentencePiece allows us to make a purely end-to-end system that does not depend on language-specific pre  postprocessing.

SentencePiece - Unsupervised text tokenizer for Neural Network-based text generation

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.
 
After merging with the AForge.NET project, the framework now offers a unified API for learning/training machine learning models that is both easy to use and extensible. 
 
It supports Vector Machines , Logistic Regression , Decision Trees , Neural Networks , Deep Learning (Deep Neural Networks), Levenberg-Marquardt with Bayesian Regularization , Restricted Boltzmann Machines , Sequence classification , Hidden Markov Classifiers and Hidden Conditional Random Fields, Multiple linear regression , Multivariate linear regression , polynomial regression , logarithmic regression.
 
 Clustering algorithms like K-Means , K-Modes , Mean-Shift , Gaussian Mixture Models , Binary Split, Image processing, Load, parse, save, filter and transform audio signals, Real-time face detection and tracking , as well as general methods for detecting , tracking and transforming objects in image streams and lot more.

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET

Gorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.
 
Gorgonia can perform automatic differentiation, symbolic differentiation, gradient descent optimizations, numerical stabilization, distributed computing. 
 
The primary goal for Gorgonia is to be a highly performant machine learning/graph computation-based library that can scale across multiple machines. It should bring the appeal of Go (simple compilation and deployment process) to the ML world. It's a long way from there currently, however, the baby steps are already there.

Gorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.



Gorgonia - Library that helps facilitate machine learning in Go

Qdrant ( quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more.
 
The neural search uses semantic embeddings instead of keywords and works best with short texts. With Qdrant and a pre-trained neural network, you can build and deploy semantic neural search on your data in minutes.

Qdrant ( quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more.

Qdrant - Neural Search Engine, Vector Similarity Search Engine with extended filtering support

Deeplearning4J is an open source, distributed neural net library written in Java and Scala. It integrates with Hadoop and Spark and runs on several backends that enable use of CPUs and GPUs. It provides versatile n-dimensional array class for Java and Scala. 
 
It is best suited for Face/image recognition, Voice search, Speech-to-text (transcription), Spam filtering (anomaly detection), Fraud detection, Recommender Systems (CRM, adtech, churn prevention), Regression.

Deeplearning4J is an open source, distributed neural net library written in Java and Scala. It integrates with Hadoop and Spark and runs on several backends that enable use of CPUs and GPUs. It provides versatile n-dimensional array class for Java and Scala. 

Deeplearning4J - Neural Net Platform in Java and Scala

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more. 

Machine-Learning / Deep-Learning / AI + Web3 -Tutorials

The OpenCog Framework is a platform to build and share artificial intelligence programs. It includes components for procedural and declarative knowledge representation (AtomSpace), task scheduling (CogServer), AI algorithm containers (MindAgents), connectors to instant messaging and virtual world systems, and other components. MindAgents and other add-ons explore a wide variety of AI techniques including evolutionary program learning (MOSES), natural language processing, and others.

OpenCog - Framework to build Artificial Intelligence Programs

Kur is a system for quickly building and applying state-of-the-art deep learning models to new and exciting problems. Kur was designed to appeal to the entire machine learning community, from novices to veterans. It uses specification files that are simple to read and author, meaning that you can get started building sophisticated models without ever needing to code. Even so, Kur exposes a friendly and extensible API to support advanced deep learning architectures or workflows. 

Kur - Descriptive Deep Learning

Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top of NumPy. Its features include tight integration with NumPy, transparent use of a GPU, dynamic C code generation and lot more.

Theano - Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.

NowTrade is an algorithmic trading library with a focus on creating powerful strategies using easily-readable and simple Python code. With the help of NowTrade, full blown stock/currency trading strategies, harnessing the power of machine learning, can be implemented with few lines of code. NowTrade strategies are not event driven like most other algorithmic trading libraries available. The strategies are implemented in a sequential manner (one line at a time) without worrying about events, callbacks, or object overloading.

NowTrade - Algorithmic trading library with a focus on creating powerful strategies

Dannjs is an easy-to-use neural network library for Javascript. Since this library is small and easy to learn, it acts as a playground for experimentation with deep neural networks. It is also fast to implement which makes it a great choice for various applications. 

Dannjs - Deep Neural Network Library for JavaScript

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PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration