If you want to excel in ML and deep learning, you need to know more than how to implement the algorithms—you need to know them inside-out. This book delves into selected algorithms and teaches you how to build your own from scratch.
From Deep Learning for Natural Language Processing by Stephan Raaijmakers
This article introduces you to working with BERT.
From Automated Machine Learning in Action by Qingquan Song, Haifeng Jin, and Xia Hu
This article covers
• Defining and introducing the fundamental concepts of machine learning
• Describing the motivation for and high-level concepts of automated machine learning
In case you missed it, here is Miguel Morales’ live Twitch coding stream recap. For more, check out the book: Grokking Deep Reinforcement Learning. For more live coding streams, subscribe to Manning’s Twitch channel here: https://www.twitch.tv/manningpublications
In case you missed it, here is Peter Elger and Eoin Chanaghy’s live Twitch coding stream recap. For more, check out the book: AI as a Service. For more live coding streams, subscribe to Manning’s Twitch channel here: https://www.twitch.tv/manningpublications
In this video, machine learning expert Eli Stevens showcases how to use open-source libraries that are available in the PyTorch ecosystem to cut down the amount of the code that you want to write.
In this video, we join deep learning advocate Mark Ryan on his tour into training the model and running experiments.
Learn the most important tools in the repertoire of a data scientist and machine learning practitioner – Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Latent Semantic Analysis (LSA) – with the help of Krishnendu Chaudhury.
Deep dive with Carl Osipov into understanding automatic differentiation used by PyTorch autograd for deep learning
Kevin Ferguson, co-author of Deep Learning and the Game of Go, was our latest Data Speaker Series guest. He talked about how AlphaGo Zero combines tree search and reinforcement learning in a novel way.