Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
 
Use Keras if you need deep learning that 
<ul>
<li>Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).</li>
<li>Supports both convolutional networks and recurrent networks, as well as combinations of the two.</li>
<li>Runs seamlessly on CPU and GPU.</li>
</ul>

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

keras - Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.

Project DeepSpeech is an open source Speech-To-Text engine. It uses a model trained by machine learning techniques, based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow project to make the implementation easier.

DeepSpeech - A TensorFlow implementation of Baidu's DeepSpeech architecture

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Kubeflow is a platform for data scientists who want to build and experiment with ML pipelines. Kubeflow is also for ML engineers and operational teams who want to deploy ML systems to various environments for development, testing, and production-level serving.

kubeflow - Machine Learning Toolkit for Kubernetes

Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving. Ray is a unified way to scale Python and AI applications from a single node to a cluster.
 
Ray AI Runtime (AIR) is an open-source toolkit for building ML applications. It provides libraries for distributed data processing, model training, tuning, reinforcement learning, model serving, and more.
 
Ray Core provides a simple and flexible API for building and running your distributed applications. You can often parallelize single machine code with little to zero code changes.
 
With a Ray cluster you can deploy your workloads on AWS, GCP, Azure or on premise. You can also use Ray cluster managers to run Ray on your existing Kubernetes, YARN, or Slurm clusters.

Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving. Ray is a unified way to scale Python and AI applications from a single node to a cluster.


Ray - A unified framework for scaling AI and Python applications

RedisAI is a Redis module for executing Deep Learning/Machine Learning models and managing their data. Its purpose is being a "workhorse" for model serving, by providing out-of-the-box support for popular DL/ML frameworks and unparalleled performance. RedisAI both maximizes computation throughput and reduces latency by adhering to the principle of data locality , as well as simplifies the deployment and serving of graphs by leveraging on Redis' production-proven infrastructure.

RedisAI - A Redis module for serving tensors and executing deep learning graphs

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

AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on recent AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models.

AdaNet - Fast and flexible AutoML with learning guarantees

Guesslang detects the programming language of a given source code. It supports more than 50 programming languages and detects the correct programming language with more than 90% accuracy. It is based on Tensorflow machine learning framework that have been trained with over a million source code files.





Guesslang - Detects the programming language of a given source code

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