Deep Learning

Deep Learning for humans

Caffe: a fast open framework for deep learning.

Deep Learning Book Chinese Translation

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

Foundational deep learning lessons

The most cited deep learning papers

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

The fastai deep learning library, plus lessons and tutorials

PyTorch library aimed at accelerating 3D deep learning research

Books for machine learning, deep learning, math, NLP, CV, RL, etc

Full Stack Deep Learning Online Course

Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.

Paddle.js is an Web project for Baidu Paddle, which is an an open source deep learning framework designed to work on web browser. Load a pretrained paddle.js SavedModel or Paddle Hub module into the browser and run inference through Paddle.js. It could run on nearly every browser with WebGL support.

DeepFaceLab is the leading software for creating deepfakes.

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

A Deep-Learning Chess Engine with some awesome features

MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

A Deep-Learning Chess Engine with some awesome features

Machine Learning and Deep Learning lessons.

DeepLearning Models using Keras framework

Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP. Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning. We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn.

COVID-19: Face Mask Detector with OpenCV, Keras/TensorFlow, and Deep Learning

Developed using Pybullet, OpenCV and Deep Learning

Earthquake Responses prediction using Deep learning and Database

My Notes & Templates on Deep Learning/Apache Spark; Used for building code faster

ktrain is a Python library that makes deep learning and AI more accessible and easier to apply

Collection of papers in machine learning, deep learning and neuroscience

GPU accelerated deep learning inference applications using TensorflowLite GPUDelegate / TensorRT