Must-read tutorial to learn sequence modeling from 'Analytics Vidhya' team and detailed explanation by real examples

Ultimate learning path of Deep Learning for 2019 in infographics. Image credited from Analytics Vidhya

Prediction based algorithms in infographics. Type, name, description, advantages and disadvantages

This is a super cool resource: Papers With Code now includes 950+ ML tasks, 500+ evaluation tables (including SOTA results) and 8500+ papers with code. Probably the largest collection of NLP tasks I've seen including 140+ tasks and 100 datasets.

Impractical PythonProjects by Lee Vaughan 2018

Github repository:
https://github.com/rlvaugh/Impractical_Python_Projects

Deep Learning Drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these selected and exciting lectures!!

GitHub by Marimuthu Kalimuthu

You are deep learning enthusiast and Covolutions are unseperable part of your projects. In this tutorial given comprehensive guideline all about convolutions:

-> Convolution v.s. Cross-correlation
-> Convolution in Deep Learning (single channel version, multi-channel version)
-> 3D Convolution
-> 1 x 1 Convolution
-> Convolution Arithmetic
-> Transposed Convolution (Deconvolution, checkerboard artifacts)
-> Dilated Convolution (Atrous Convolution)
-> Separable Convolution (Spatially Separable Convolution, Depthwise Convolution)
-> Flattened Convolution
-> Grouped Convolution
-> Shuffled Grouped Convolution
-> Pointwise Grouped Convolution

Rules of Machine Learning:
Best Practices for ML Engineering by Martin Zinkevich best practices in ML from around Google 👆

Computer Vision news magazine RSIP vision. February 2019. CV Application, Challenges, Projects

Introduction to Deep learning with flavor of Natural Language Processing(NLP)
Course (Tokyo Institue of Technology) materials, demos and implementations are available. Enjoy with DL. Happy learning