neural-vqa - :grey_question: Visual Question Answering in Torch

•    Lua

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This is an experimental Torch implementation of the VIS + LSTM visual question answering model from the paper Exploring Models and Data for Image Question Answering by Mengye Ren, Ryan Kiros & Richard Zemel. Download the MSCOCO train+val images and VQA data using sh data/download_data.sh. Extract all the downloaded zip files inside the data folder.

torch deep-learning computer-vision natural-language-processing

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

•    Python

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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.

deep-learning deeplearning deep-neural-networks deep learning machine-learning machinelearning machine natural-language-processing natural-language-understanding natural-language natural-language-generation computer-vision python3

practical-machine-learning-with-python - Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system

•    Jupyter

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"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

machine-learning deep-learning text-analytics classification clustering natural-language-processing computer-vision data-science spacy nltk scikit-learn prophet time-series-analysis convolutional-neural-networks tensorflow keras statsmodels pandas deep-neural-networks

t81_558_deep_learning - Washington University (in St

•    Jupyter

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Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

neural-network machine-learning tensorflow keras deeplearning

zhihu - This repo contains the source code in my personal column (https://zhuanlan

•    Jupyter

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This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.

deep-learning tensorflow-examples convolutional-neural-networks recurrent-neural-networks autoencoder gan style-transfer natural-language-processing machine-translation

AliceMind - ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab

•    Python

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This repository provides pre-trained encoder-decoder models and its related optimization techniques developed by Alibaba's MinD (Machine IntelligeNce of Damo) Lab. StructVBERT (March 15, 2021): pre-trained models for vision-language understanding. We propose a new single-stream visual-linguistic pre-training scheme by leveraging multi-stage progressive pre-training and multi-task learning. StructVBERT obtained the 2020 VQA Challenge Runner-up award, and SOTA result on VQA 2020 public Test-standard benchmark (June 2020). "Talk Slides" (CVPR 2020 VQA Challenge Runner-up).

nlp natural-language-processing deep-learning bert

PaddleFL - Federated Deep Learning in PaddlePaddle

•    C++

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PaddleFL is an open source federated learning framework based on PaddlePaddle. Researchers can easily replicate and compare different federated learning algorithms with PaddleFL. Developers can also benefit from PaddleFL in that it is easy to deploy a federated learning system in large scale distributed clusters. In PaddleFL, several federated learning strategies will be provided with application in computer vision, natural language processing, recommendation and so on. Application of traditional machine learning training strategies such as Multi-task learning, Transfer Learning in Federated Learning settings will be provided. Based on PaddlePaddle's large scale distributed training and elastic scheduling of training job on Kubernetes, PaddleFL can be easily deployed based on full-stack open sourced software. Data is becoming more and more expensive nowadays, and sharing of raw data is very hard across organizations. Federated Learning aims to solve the problem of data isolation and secure sharing of data knowledge among organizations. The concept of federated learning is proposed by researchers in Google [1, 2, 3]. PaddleFL implements federated learning based on the PaddlePaddle framework. Application demonstrations in natural language processing, computer vision and recommendation will be provided in PaddleFL. PaddleFL supports the current two main federated learning strategies[4]: vertical federated learning and horizontal federated learning. Multi-tasking learning [7] and transfer learning [8] in federated learning will be developed and supported in PaddleFL in the future.

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

•    CSharp

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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.

machine-learning framework c-sharp nuget visual-studio statistics unity3d neural-network support-vector-machines computer-vision image-processing ffmpeg

sod - An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)

•    C

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SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.

computer-vision library deep-learning image-processing object-detection cpu real-time convolutional-neural-networks recurrent-neural-networks face-detection facial-landmarks machine-learning-algorithms image-recognition image-analysis vision-framework embedded detection iot-device iot

tensorlayer-tricks - How to use TensorLayer

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While research in Deep Learning continues to improve the world, we use a bunch of tricks to implement algorithms with TensorLayer day to day. Here are a summary of the tricks to use TensorLayer. If you find a trick that is particularly useful in practice, please open a Pull Request to add it to the document. If we find it to be reasonable and verified, we will merge it in.

tensorlayer tensorflow deep-learning machine-learning data-science neural-network reinforcement-learning neural-networks tensorflow-tutorials tensorflow-models computer-vision tensorflow-framework tensorflow-library tflearn keras tensorboard nlp natural-language-processing lasagne tensorflow-experiments

spaCy - 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython

•    Python

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spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pre-trained statistical models and word vectors, and currently supports tokenization for 20+ languages. It features the fastest syntactic parser in the world, convolutional neural network models for tagging, parsing and named entity recognition and easy deep learning integration. It's commercial open-source software, released under the MIT license. 💫 Version 2.0 out now! Check out the new features here.

natural-language-processing data-science big-data machine-learning cython nlp artificial-intelligence ai spacy nlp-library neural-network neural-networks deep-learning

lectures - Oxford Deep NLP 2017 course

•    

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This repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions. These topics are organised into three high level themes forming a progression from understanding the use of neural networks for sequential language modelling, to understanding their use as conditional language models for transduction tasks, and finally to approaches employing these techniques in combination with other mechanisms for advanced applications. Throughout the course the practical implementation of such models on CPU and GPU hardware is also discussed.

deep-learning machine-learning natural-language-processing nlp oxford

knockknock - 🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code

•    Python

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A small library to get a notification when your training is complete or when it crashes during the process with two additional lines of code. When training deep learning models, it is common to use early stopping. Apart from a rough estimate, it is difficult to predict when the training will finish. Thus, it can be interesting to set up automatic notifications for your training. It is also interesting to be notified when your training crashes in the middle of the process for unexpected reasons.

nlp machine-learning natural-language-processing computer-vision deep-learning cv neural-networks train python36 nlproc

nlp-architect - A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks

•    Python

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NLP Architect is an open source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding Neural Networks. NLP Architect is an NLP library designed to be flexible, easy to extend, allow for easy and rapid integration of NLP models in applications and to showcase optimized models.

nlp deep-learning tensorflow nlu transformers pytorch deeplearning quantization bert dynet

ai-deadlines - :alarm_clock: AI conference deadline countdowns

•    HTML

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Countdown timers to keep track of a bunch of CV/NLP/ML/AI conference deadlines.

deep-learning deep-neural-networks machine-learning computer-vision artificial-intelligence natural-language-processing robotics

spago - Self-contained Machine Learning and Natural Language Processing library in Go

•    Go

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A Machine Learning library written in pure Go designed to support relevant neural architectures in Natural Language Processing. spaGO is self-contained, in that it uses its own lightweight computational graph framework for both training and inference, easy to understand from start to finish.

nlp machine-learning natural-language-processing deep-learning neural-network automatic-differentiation artificial-intelligence recurrent-networks lstm computation-graph question-answering bart automatic-translation deeplearning language-model bert transformer-architecture bert-as-service named-entities-recognition

AutoGluon - AutoML for Text, Image, and Tabular Data

•    Python

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AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, and tabular data.

data-science machine-learning natural-language-processing computer-vision deep-learning mxnet scikit-learn tabular-data pytorch image-classification ensemble-learning object-detection

catalyst - Accelerated deep learning R&D

•    Python

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Accelerated deep learning R&D

infrastructure machine-learning natural-language-processing information-retrieval research reinforcement-learning computer-vision deep-learning text-classification distributed-computing image-processing pytorch image-classification metric-learning recommender-system object-detection image-segmentation reproducibility text-segmentation

awesome-ai-awesomeness - A curated list of awesome awesomeness about artificial intelligence

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A curated list of awesome awesomeness about artificial intelligence(AI). If you want to contribute to this list (please do), send me a pull request.

natural-language-processing computer-vision deep-learning artificial-intelligence neural-networks artificial-neural-networks machine-l

Deep-Learning-101 - Deep Learning Tutorials

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#Deep Learning Tutorials These tutorials are for deep learning beginners which have been used in a six week Deep Learning and Computer Vision course. Hope these to be helpful for understanding what deep learning is and how it can be applied to various fields including computer vision, robotics, natural language processings, and so forth.