Deep Learning from Scratch
Seth Weidman is a data scientist who has spent years applying and teaching machine learning principles. He began his career as Trunk Club's first data scientist, where he constructed lead scoring models and recommender systems, and now works at Facebook, where he produces machine learning models for their infrastructure team.
Deep learning has become vital for machine learning practitioners and even many software engineers since the resurrection of neural networks in the 2010s. Deep Learning from Scratch gives a thorough introduction to machine learning for data scientists and software engineers with prior experience. You'll begin with fundamentals and go fast to the intricacies of crucial advanced designs, implementing everything from the ground up along the way.
Author Seth Weidman explains how neural networks work from the ground up. From the ground up, you'll learn how to use multilayer neural networks, convolutional neural networks, and recurrent neural networks. You'll be well-prepared for future deep learning projects if you have a solid understanding of how neural networks work mathematically, computationally, and conceptually.
Deep Learning from Scratch includes:
- Extremely clear and detailed mental models for comprehending neural networks, complemented by working code samples and mathematical explanations.
- Methods for building multilayer neural networks from the ground up using a simple object-oriented framework.
- Convolutional and recurrent neural networks have working implementations and good descriptions.
- The popular PyTorch framework is used to implement these neural network features.
Author: Seth Weidman
Link to buy: https://www.amazon.com/dp/1492041416
Ratings: 4.3 out of 5 stars (from 76 reviews)
Best Sellers Rank: #392,792 in Books
#111 in Machine Theory (Books)
#123 in Computer Neural Networks
#161 in Artificial Intelligence (Books)
