tensorwatch - Debugging, monitoring and visualization for Deep Learning and Reinforcement Learning

  •        88

TensorWatch is a debugging and visualization tool designed for deep learning and reinforcement learning. It fully leverages Jupyter Notebook to show real time visualizations and offers unique capabilities to query the live training process without having to sprinkle logging statements all over. You can also use TensorWatch to build your own UIs and dashboards. In addition, TensorWatch leverages several excellent libraries for visualizing model graph, review model statistics, explain prediction and so on. TensorWatch is under heavy development with a goal of providing a research platform for debugging machine learning in one easy to use, extensible and hackable package.




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ml-agents - Unity Machine Learning Agents

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ai-resources - Selection of resources to learn Artificial Intelligence / Machine Learning / Statistical Inference / Deep Learning / Reinforcement Learning


Update April 2017: It’s been almost a year since I posted this list of resources, and over the year there’s been an explosion of articles, videos, books, tutorials etc on the subject — even an explosion of ‘lists of resources’ such as this one. It’s impossible for me to keep this up to date. However, the one resource I would like to add is https://ml4a.github.io/ (https://github.com/ml4a) led by Gene Kogan. It’s specifically aimed at artists and the creative coding community. This is a very incomplete and subjective selection of resources to learn about the algorithms and maths of Artificial Intelligence (AI) / Machine Learning (ML) / Statistical Inference (SI) / Deep Learning (DL) / Reinforcement Learning (RL). It is aimed at beginners (those without Computer Science background and not knowing anything about these subjects) and hopes to take them to quite advanced levels (able to read and understand DL papers). It is not an exhaustive list and only contains some of the learning materials that I have personally completed so that I can include brief personal comments on them. It is also by no means the best path to follow (nowadays most MOOCs have full paths all the way from basic statistics and linear algebra to ML/DL). But this is the path I took and in a sense it's a partial documentation of my personal journey into DL (actually I bounced around all of these back and forth like crazy). As someone who has no formal background in Computer Science (but has been programming for many years), the language, notation and concepts of ML/SI/DL and even CS was completely alien to me, and the learning curve was not only steep, but vertical, treacherous and slippery like ice.

polyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes

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Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.

tensorlayer - Deep Learning and Reinforcement Learning Library for Developers and Scientists

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node-tensorflow - Node.js + TensorFlow

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AirSim - Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research

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Machine-Learning-Tutorials - machine learning and deep learning tutorials, articles and other resources


This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list. If you want to contribute to this list, please read Contributing Guidelines.

ludwig - Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code

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Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. All you need to provide is a CSV file containing your data, a list of columns to use as inputs, and a list of columns to use as outputs, Ludwig will do the rest. Simple commands can be used to train models both locally and in a distributed way, and to use them to predict on new data.

ImageAI - A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

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A python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.

deep-neuroevolution - Deep Neuroevolution

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Our code is based off of code from OpenAI, who we thank. The original code and related paper from OpenAI can be found here. The repo has been modified to run both ES and our algorithms, including our Deep Genetic Algorithm (DeepGA) locally and on AWS. The folder ./visual_inspector contains implementations of VINE, i.e., Visual Inspector for NeuroEvolution, an interactive data visualization tool for neuroevolution. Refer to README.md in that folder for further instructions on running and customizing your visualization. An article describing this visualization tool can be found here.

pycm - Multi-class confusion matrix library in Python

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PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. threshold is added in version 0.9 for real value prediction.



Here we have a Deep Learning system image (AWS AMI) custom built for the p2.xlarge system, which at ~$1 per hour is a GREAT price for a machine as powerful as it is. I've had my share of difficulties with building various modules/packages/drivers personally, so I thought I would share this for any fellow ml/dl/ai researchers/professionals who feel they might benefit from not having to go through the lengthy process of debugging/hacking that I did. I wanted to build a flexible system that would allow myself and others to seamlessly explore whatever machine learning / deep learning system I wanted to, while side-stepping the plethora of dependency conflict issues / custom build issues that inevitably seem to arise each time I try a new package. To that end, I wanted to start off with a clean system, and do a simul-build of all of the latest, bleeding-edge packages around. I'm sure I inevitably missed a few things here and there, but feel free to shoot me an email at mark.woods89 (at) gmail (dot) com if theres something you'd like to see added to this image.

ViZDoom - Doom-based AI Research Platform for Reinforcement Learning from Raw Visual Information

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ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is primarily intended for research in machine visual learning, and deep reinforcement learning, in particular. ViZDoom is based on ZDoom to provide the game mechanics.

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