Important Links

TensorFlow Crash Course

Google’s fast-paced, practical introduction to machine learning
[40+ exercises, 25 lessons, 15 hours]

Foundations of Machine Learning

Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning.

Machine Learning Cheatsheet

Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.

Facebook Field Guide to Machine Learning video series.

The Facebook Field Guide to Machine Learning is a six-part video series developed by the Facebook ads machine learning team.

ML Audio

Machine Learning Guide Twimlai
This series aims to teach you the high level fundamentals of machine learning from A to Z. I’ll teach you the basic intuition, algorithms, and math.

Reinforcement Learning Intro

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

AI a Modern Approach

Artificial Intelligence: A Modern Approach
(Third edition) by Stuart Russell and Peter Norvig
The leading textbook in Artificial Intelligence.
Used in over 1300 universities in over 110 countries.

Berkeley cs294: Deep Reinforcement Learning

CS 294-112 at UC Berkeley – Deep Reinforcement Learning

 

VIA Group Public Databases
Documented image databases are essential for the development of quantitative image analysis tools especially for tasks of computer-aided diagnosis (CAD). In collaboration with the I-ELCAP group we have established two public image databases that contain lung CT images in the DICOM format together with documentation of abnormalities by radiologists. Please access the links below for more details:

http://www.via.cornell.edu/databases/

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Kaggle

  1. Resize images (nuclei segmentation)
  2. https://www.kaggle.com/competitions

A compiled list of kaggle competitions and their winning solutions for classification problems.

  1. https://github.com/ShuaiW/kaggle-classification

Machinelearningmastery

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