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/

ODSC is bringing you Blockbuster workshop in Quantitative Finance+ Data Science absolutely FREE. The workshop has three presenters from diverse domains coming together to deliver it to you on June 29th ..Hurry Up!!! Limited seats Only.
Pankaj is Quantitative Finance researcher for State Street who is also one CFA level 2 candidate
Abinash Panda is CEO and Founder of Prodios is the Founding member of the famous pgmpy package. He has also written two books for Pakt publications in Probabilistic Graphical Models and Markov Models
Usha Rengaraju is an expert in Quantitative Finance and Bayesian Networks.
The workshop will also be followed by the AMA session by Swiggy Data Science Leaders.
RSVP here : https://bit.ly/2IiAzGc
#datascience #odsc #openai #neuralnetworks #ml #deeplearning #analytics #machinelearning #ai #artificialintelligence
@Machine_learn

#Deep Learning Innovations and Their Convergence with Big Data
#DL #book
@Machine_learn

#Deep Learning Innovations and Their Convergence with Big Data
#DL #book
@Machine_learn