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

Watson Studio Desktop is now free for academia. All products in your charge for free:
* Watson Studio Cloud
* Watson Studio Local
* Watson studio Desktop

Just visit, register as a student or faculty, varify your account, install and enjoy with service.
You'll get detailed information in below medium link

Next decade of technologies and fields by interpretation of Business insider:

1 #AI
2 #IoT⌚️
3 #blockchain ⛓
4 3D print 🖨
5 mobile📱
6 autonomous cars 🚗
7 mobile internet 💻
8 robotics🤖
9 VR/AR 👓
10 wireless power🔌
11 quantum computing 🖥
12 5G 📡
13 voice assistant🎙
14 cybersecurity🔒 #MWC19

Video is credited from Tech Insider 👇

Natural Language Processing Tutorial for Deep Learning Researchers using Tensorflow and Pytorch

Most of the models in NLP were implemented with less than 100 lines of code.(except comments or blank lines)