Immerse yourself in Dive into Deep Learning, an engaging and accessible resource. Tailored for engineers, scientists, and students, it strikes a perfect balance between simplicity and technical rigor. You don’t need prior machine learning expertise; every concept is introduced anew. The appendix offers a math refresher, while practical runnable code instances foster intuitive understanding. Experience deep learning’s potential firsthand as you engage with this reader-friendly guide.
Table of Contents
- Introduction
- Preliminaries
- Linear Neural Networks for Regression
- Linear Neural Networks for Classification
- Multilayer Perceptrons
- Builders’ Guide
- Convolutional Neural Networks
- Modern Convolutional Neural Networks
- Recurrent Neural Networks
- Modern Recurrent Neural Networks
- Attention Mechanisms and Transformers
- Optimization Algorithms
- Computational Performance
- Computer Vision
- Natural Language Processing: Pretraining
- Natural Language Processing: Applications
- Reinforcement Learning
- Gaussian Processes
- Hyperparameter Optimization
- Generative Adversarial Networks
- Recommender Systems
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Author(s): Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola
Publisher: Cambridge University Press
Published: January 31, 2024
Format(s): Online
File size: –
Number of pages: 574
Download / View Link(s): Online
Publisher: Cambridge University Press
Published: January 31, 2024
Format(s): Online
File size: –
Number of pages: 574
Download / View Link(s): Online