Artificial Intelligence && Deep Learning

High energy: Facebook's AI guru LeCun imagines AI's next frontier

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.https://www.zdnet.com/google-amp/article/high-energy-facebooks-ai-guru-lecun-imagines-ais-next-frontier/?fbclid=IwAR1fnUHFVFeDyPNJI3SGZuSnvJrhJ0qrdOXMkaK14phkI5wcENDZixps5vw

100 Free Data Science Books

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.https://www.learndatasci.com/free-data-science-books/

Python Projects with Source Code - Practice Top Projects in Python - DataFlair

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.https://data-flair.training/blogs/python-projects-with-source-code/

Vid2Vid — Conditional GANs for Video-to-Video Synthesis

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.https://neurohive.io/en/state-of-the-art/vid2vid-conditional-gans-for-video-to-video-synthesis/

Automatically finding the best Neural Network for your GAN

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.https://towardsdatascience.com/automatically-finding-the-best-neural-network-for-your-gan-c0b97a5949f2

Python Implementation of Andrew Ng’s Machine Learning Course (Part 1)

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.https://medium.com/analytics-vidhya/python-implementation-of-andrew-ngs-machine-learning-course-part-1-6b8dd1c73d80

How to Deal with Imbalanced Data using SMOTE - Analytics Vidhya - Medium

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.https://medium.com/analytics-vidhya/balance-your-data-using-smote-98e4d79fcddb

Must-read papers on GNN

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.https://github.com/thunlp/GNNPapers

Review: FSRCNN (Super Resolution)

What Are Covered

1. Brief Review of SRCNNFSRCNN Network
2. ArchitectureExplanation of 1×1 Convolution Used in
3. Shrinking and Expanding
4. Explanation of Multiple 3×3 Convolutions in Non-Linear Mapping
5. Ablation Study
6. Results

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.https://towardsdatascience.com/review-fsrcnn-super-resolution-80ca2ee14da4

Review: G-RMI — Winner in 2016 COCO Detection (Object Detection)

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.https://towardsdatascience.com/review-g-rmi-winner-in-2016-coco-detection-object-detection-af3f2eaf87e4

The project is about predicting coronary heart disease by using three different ML algorithms

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https://blog.goodaudience.com/heart-disease-prediction-aa656f2db585

The 5 Feature Selection Algorithms every Data Scientist should know

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.https://towardsdatascience.com/the-5-feature-selection-algorithms-every-data-scientist-need-to-know-3a6b566efd2

Deepfakes, FaceGANS, and Synthetic Data: Welcome to the Reality Illusion of 2020

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.https://medium.com/swlh/deepfakes-facegans-and-the-rise-of-synthetic-data-welcome-to-2020-a54b88eecdf9

2D or 3D? A Simple Comparison of Convolutional Neural Networks for Automatic Segmentation of Cardiac Imaging

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.https://towardsdatascience.com/2d-or-3d-a-simple-comparison-of-convolutional-neural-networks-for-automatic-segmentation-of-625308f52aa7

A 2019 Guide to Object Detection

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.https://heartbeat.fritz.ai/a-2019-guide-to-object-detection-9509987954c3

Everything You Need to Know About Autoencoders in TensorFlow

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.https://towardsdatascience.com/everything-you-need-to-know-about-autoencoders-in-tensorflow-b6a63e8255f0

The Best Machine Learning Research of 2019 So Far - ODSC - Open Data Science - Medium

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.https://medium.com/@ODSC/the-best-machine-learning-research-of-2019-so-far-954120947794

Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library 1st Edition

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Who This Book Is For
This book contains descriptions, working code examples, and explanations of the
C++ computer vision tools contained in the OpenCV 3.x library. Thus, it should be
helpful to many different kinds of users:

Professionals and entrepreneurs
For practicing professionals who need to rapidly prototype or professionally
implement computer vision systems, the sample code provides a quick frame‐
work with which to start. Our descriptions of the algorithms can quickly teach or
remind the reader how they work. OpenCV 3.x sits on top of a hardware acceler‐
ation layer (HAL) so that implemented algorithms can run efficiently, seamlessly
taking advantage of a variety of hardware platforms.

Students....
Teachers....
Hobbyist....