Useful links

Name	Main Libs	Level	Source	Comments	Language
Machine Learning for Beginners: An Introduction to Neural Networks	Numpy	Beginner	Internet	---	ENG
Machine Learning Specialization	Tensorflow, XGBoost	Beginner	Coursera	---	ENG
Complete Guide to Adam Optimization	---	---	TowardsDataScience	---	ENG
MNIST digits classification with TensorFlow	Tensorflow	---	Medium	---	ENG
Учебник по машинному обучению	---	Beginner	Yandex	---	RUS
Tricking Neural Networks: Create your own Adversarial Examples	---	Pro	Medium	Using noise to crack neural network	ENG
Создание простой нейронной сети на Python	NumPy, Scikit-learn, Matplotlib	Beginner	Medium	---	ENG
Generative adversarial networks	Tensorflow	Medium	Habr	---	RUS
A Beginner's Guide to Generative Adversarial Networks (GANs)	Tensorflow	Medium	Internet	There are a lot of different papers in comment of this site	ENG
GANs from Scratch 1: A deep introduction. With code in PyTorch and TensorFlow	Pytorch	Beginner (in GANs)	Medium	---	ENG
A Survey of Visual Attention Mechanisms in Deep Learning	Only math	Pro	Medium	---	ENG
Attention mechanism	Only math	Pro	Medium	---	ENG
Нейросеть — обучение без учителя. Метод Policy Gradient	PyTorch	Medium	Habr	Пример игры где ракетка пытается поймать кубики падающие сверху	RUS
Neural Architecture Search (NAS): basic principles and different approaches	Pytorch	Pro+	Internet	neural network intelligence (nni) package by Microsoft	ENG
Reinforcement Learning Explained Visually (Part 4): Q Learning, step-by-step	Only math	Pro	TowardsDataScience	There are lots of parts in this article	ENG
Backpropagation in a convolutional layer	Only math	Pro	TowardsDataScience	---	ENG
Introduction to 1D Convolutional Neural Networks in Keras for Time Sequences	Tensorflow	Beginner	Internet	Очень хорошо объяснено, как работает 1D Convolutional Layers	ENG
Attention for time series forecasting and classification	---	Medium	Medium	---	ENG
Transformers Explained Visually (Part 3): Multi-head Attention, deep dive	---	Medium	TowardsDataScience	---	ENG
Why multi-head self attention works: math, intuitions and 10+1 hidden insights	---	Pro	Internet	---	ENG