Key Papers in Deep Reinforcement Learning
#deep_learning
#Reinforcement_learning
______




@Machine_learn



_______
https://spinningup.openai.com/en/latest/spinningup/keypapers.html

Estimators, Loss Functions, Optimizers —Core of ML Algorithms
____



@Machine_learn

____
https://towardsdatascience.com/estimators-loss-functions-optimizers-core-of-ml-algorithms-d603f6b0161a]

#Designing Machine Learning Systems with Python by David Julian
#Book @Machine_learn

#Designing Machine Learning Systems with Python by David Julian
#Book @Machine_learn

TensorWatch: a debugging and visualization tool designed for deep learning
#TensorWatch
#tool #deep_learning
____


@Machine_learn

____
https://github.com/microsoft/tensorwatch

@Machine_learn #Article_code

Generating Game of Thrones Characters Using StyleGAN
article: https://blog.nanonets.com/stylegan-got/

gitHub repo: https://github.com/iyaja/stylegan-encoder

KB – Neural Data Mining with Python sources — Roberto Bello (en) 2013
#middle #book
@Machine_learn

KB – Neural Data Mining with Python sources — Roberto Bello (en) 2013
#middle #book
@Machine_learn

A Manager’s Guide to Data Warehousing — Laura L. Reeves (en) 2009
#book #beginner @Machine_learn

A Manager’s Guide to Data Warehousing — Laura L. Reeves (en) 2009
#book #beginner @Machine_learn

How to Develop a Deep CNN to Classify Satellite Photos of the Amazon Rainforest
____



@Machine_learn


______

https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-satellite-photos-of-the-amazon-rainforest/

@Machine_learn
MNIST reborn, restored and expanded.
Now with an extra 50,000 training samples.

If you used the original #MNIST test set more than a few times, chances are your models #overfit the test set. Time to test them on those extra samples.

Now you will use #QMNIST instead of #MNIST
Detailed explanation at #paper: 👇

https://arxiv.org/pdf/1905.10498.pdf

and it's #implementation and some results by using #pytorch: 👇

https://github.com/facebookresearch/qmnist

#50 AI Influencers to follow on #twitter
@Machine_learn

Chapter 1: Making Paper Cryptography Tools
Chapter 2: Programming in the Interactive Shell
Chapter 3: Strings and Writing Programs
Chapter 4: The Reverse Cipher
Chapter 5: The Caesar Cipher
Chapter 6: Hacking the Caesar Cipher with Brute-Force
Chapter 7: Encrypting with the Transposition Cipher
Chapter 8: Decrypting with the Transposition Cipher
Chapter 9: Programming a Program to Test Your Program
Chapter 10: Encrypting and Decrypting Files
Chapter 11: Detecting English Programmatically
Chapter 12: Hacking the Transposition Cipher
Chapter 13: A Modular Arithmetic Module for the Affine Cipher
Chapter 14: Programming the Affine Cipher
Chapter 15: Hacking the Affine Cipher
Chapter 16: Programming the Simple Substitution Cipher
Chapter 17: Hacking the Simple Substitution Cipher
Chapter 18: Programming the Vigenère Cipher
Chapter 19: Frequency Analysis
Chapter 20: Hacking the Vigenère Cipher
@Machine_learn #book #python

Chapter 21: The One-Time Pad Cipher
Chapter 22: Finding and Generating Prime Numbers
Chapter 23: Generating Keys for the Public Key Cipher
Chapter 24: Programming the Public Key
@Machine_learn #book #python

How to Perform Object Detection in Photographs Using Mask R-CNN with Keras

@Machine_learn
https://machinelearningmastery.com/how-to-perform-object-detection-in-photographs-with-mask-r-cnn-in-keras/

Bottle Documentation — Marcel Hellkamp (en) 2019 #book
#middle #python #web_framework
@Machine_learn

Bottle Documentation — Marcel Hellkamp (en) 2019 #book
#middle #python #web_framework
@Machine_learn

#Multi-Sample Dropout for Accelerated Training
and Better Generalization
#Paper @Machine_learn

#Multi-Sample Dropout for Accelerated Training
and Better Generalization
#Paper @Machine_learn