We’d like to thank the participating publishers who have created custom discount codes for online order and are sending books for the Book Display at MLconf NYC 2019. At the close of the event, we’ll host a book giveaway from a small collection of various ML books and textbooks across several publishers. Attendees who tweet the most interesting and unique tweets, using the @mlconf or #mlcny. Book winners will be tweeted at by the @mlconf handle on 3/29/2019. Must be present to win.
The 40% off discount code on the following books (for all attendees): ctwmlconfny19
- Real-World Machine Learning by Henrik Brink, Joseph W. Richards, and Mark Fetherolf
- AWS Machine Learning in Motion (live video course) by Kesha Williams
- Machine Learning with TensorFlow by Nishant Shukla
- Machine Learning Systems by Jeff Smith
- Machine Learning for Mere Mortals (live video course) by Nicholas Chase
- Deep Reinforcement Learning by Alexander Zai and Brandon Brown
- Deep Learning and the Game of Go by Max Pumperla and Kevin Ferguson
- Deep Learning with Python by François Chollet
- Deep Learning with R by François Chollet with J. J. Allaire
- Grokking Deep Reinforcement Learning by Miguel Morales
- Reinforcement Learning in Motion (live video course) by Phil Tabor
**Email books@mlconf.com for a chance to win a free eBook Grokking Deep Learning for Computer Vision by Mohamed Elgendy Full Catalog, here.
The books are 20% off and the discount is good until April 30, 2019. Check out classics such as:
- Statistical Methods for Recommender Systems by Agarwal/Chen
- Network Science by Barabási/Pósfai
- Bayesian Reasoning and Machine Learning by Barber
- Scaling up Machine Learning, Parallel and Distributed Approaches By Bekkerman/Bilenko/Langford
- Financial Analytics with R Building a Laptop Laboratory for Data Science by Bennett/Hugen
- Geometric and Topological Inference by Boissonnat/Chazal/Yvinec
- Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares by Boyd/Vandenberghe
- A First Course in Statistical Programming with R by Braun/Murdoch
- Data-Driven Science and Engineering Machine Learning, Dynamical Systems, and Control by Brunton/Kutz
- Big Crisis Data Social Media in Disasters and Time-Critical Situations by Castillo
