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

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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
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@Machine_learn


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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

IINTRODUCTION TO DEEP COMPUTER VISION
John Olafenwa & Moses Olafenwa
#book #DL #Deep
@Machine_learn

IINTRODUCTION TO DEEP COMPUTER VISION
John Olafenwa & Moses Olafenwa
#book #DL #Deep
@Machine_learn

@Machine_learn
Introducing TensorFlow Graphics: Computer Graphics Meets Deep Learning

Github : https://github.com/tensorflow/graphics

Article: https://medium.com/tensorflow/introducing-tensorflow-graphics-computer-graphics-meets-deep-learning-c8e3877b7668

@Machine_learn
PyTorch image models, scripts, pretrained weights -- (SE)ResNet/ResNeXT, DPN, EfficientNet, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet, and more

https://github.com/rwightman/pytorch-image-models?fbclid=IwAR0QNx9Hui3Tucr04-yR5RlSXF9ApTNcXbMAilZrDnhFDiTy5QduNQQjgqA

@Machine_learn
How to Visualize Filters and Feature Maps in Convolutional Neural Networks #CNN #DL #Tools
https://machinelearningmastery.com/how-to-visualize-filters-and-feature-maps-in-convolutional-neural-networks/