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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.
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
A curated list of awesome Dash (plotly) resources
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
STUMPY is a powerful and scalable Python library for modern time series analysis
Modeltime unlocks time series forecast models and machine learning in one framework
Visual analysis and diagnostic tools to facilitate machine learning model selection.
Deep Learning Specialization by Andrew Ng, deeplearning.ai.
Deep Learning Specialization by Andrew Ng on Coursera.
Awesome free machine learning and AI courses with video lectures.
Rich is a Python library for rich text and beautiful formatting in the terminal.
Install and Run Python Applications in Isolated Environments
Code for "Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?"
Lab Materials for MIT 6.S191: Introduction to Deep Learning
A Code-First Introduction to NLP course
The Natural Language Decathlon: A Multitask Challenge for NLP
Neural network toolkit for sentence pair modeling.
Different types of autoencoders illustrated on MNIST using TensorFlow.
Example transcribing audio file (speech) to text with Google Cloud Speech API and Python
Speech recognition module for Python, supporting several engines and APIs, online and offline.
Collective communications library with various primitives for multi-machine training.
This is the code for "Capsule Networks: An Improvement to Convolutional Networks" by Siraj Raval on Youtube
A Keras implementation of CapsNet in NIPS2017 paper "Dynamic Routing Between Capsules". Now test error ＝ 0.34%.
A Tensorflow implementation of CapsNet(Capsules Net) in paper Dynamic Routing Between Capsules
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.