The following videos were recording during the April 19 Panel event. You may wish to reference them in preparation of the weekend ML event.
VC Panel moderated by Jay Bartot. Panelists: Tim Porter, Mike Miller, Pradeep Rathinam & Ankur Teredesai
WATCH VIDEOThe list below contains a wide variety of machine learning educational resources aimed at data scientists, software engineers, and even non-technical business and product people. Take some time to peruse the books, articles and videos that appear most appropriate for your background. The more prepared you are coming into the event, the more you'll get out of it!
"...If data-ism is today's philosophy, this book is its bible..."
For all participants
Highly visual YouTube video explaining fundamentals of Deep Learning.
For all participants
"...this microsite is intended to help newcomers (both non-technical and technical) begin exploring what's possible with AI"
Non-Technical
Blog articles with resources for non-technical folks about ML/AI.
Non-Technical
"...This (KDNuggets) post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn."
For developers, non-technical
"...Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering. The word learning in the term stems from the ability to learn from data..."
For developers, non-technical
A cool and interactive visualization that explains some fundamentals behind machine learning.
For developers, non-technical
KDNuggets article by best selling author Sebastian Raschka. Pointers to videos and other resources for an introductory high-level overview of ML and data science.
For developers, non-technical
Best selling book, authored by our friend Joel Grus, shows how many of the most fundamental data science tools and algorithms work by implementing them from scratch. Great book for software engineers looking to get into data science.
For developers
Great curated list of FREE, online and foundational machine learning books
For developers & data scientists
Top 8 Free Must-Read Books on Deep Learning
For developers & data scientists
A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding
For developers
"Master machine learning by using it on real life applications, even if you’re starting from scratch." Great site for software engineers who want to understand machine learning through code.
For developers
"This book has been written in layman’s terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application."
For developers, non-technical
Building powerful image classification models using very little data
For data scientists
Part two in a three-part series on building a complete end-to-end image classification + deep learning application from PyImageSearch
For developers, data scientists
We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.
For data scientists
Although a bit dated now, great blog post Andrej Karpathy on the power and possibilities of RNNs and LSTMs.
For data scientists
Keras is a high-level neural network API, helping lead the way to the commoditization of deep learning and artificial intelligence.
For developers, data scientists
Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples
For developers, data scientists
