Fundamentals of Deep Learning
O'Reilly Media (Verlag)
978-1-4919-2561-4 (ISBN)
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With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that's paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.
Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you're familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.
- Examine the foundations of machine learning and neural networks
- Learn how to train feed-forward neural networks
- Use TensorFlow to implement your first neural network
- Manage problems that arise as you begin to make networks deeper
- Build neural networks that analyze complex images
- Perform effective dimensionality reduction using autoencoders
- Dive deep into sequence analysis to examine language
- Learn the fundamentals of reinforcement learning
Nikhil Buduma is a computer science student at MIT with deep interests in machine learning and the biomedical sciences. He is a two time gold medalist at the International Biology Olympiad, a student researcher, and a "hacker." He was selected as a finalist in the 2012 International BioGENEius Challenge for his research on the pertussis vaccine, and served as the lab manager of the Veregge Lab at San Jose State University at the age of 16. At age 19, he had a first author publication on using protist models for high throughput drug screening using flow cytometry. Nikhil also has a passion for education, regularly writing technical posts on his blog, teaching machine learning tutorials at hackathons, and recently, received the Young Innovator Award from the Gordon and Betty Moore Foundation for re-invisioning the traditional chemistry set using augmented reality.
Preface
Chapter 1 The Neural Network
Chapter 2 Training Feed-Forward Neural Networks
Chapter 3 Implementing Neural Networks in TensorFlow
Chapter 4 Beyond Gradient Descent
Chapter 5 Convolutional Neural Networks:
Chapter 6 Embedding and Representation Learning
Chapter 7 Models for Sequence Analysis
Chapter 8 Deep Reinforcement Learning
| Erscheint lt. Verlag | 7.7.2017 |
|---|---|
| Co-Autor | Nicholas Locascio |
| Verlagsort | Sebastopol |
| Sprache | englisch |
| Maße | 178 x 231 mm |
| Gewicht | 566 g |
| Einbandart | kartoniert |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Algorithmen • Deep learning • flow • Künstliche Intelligenz • machine intelligence • machine learning • Python • Tensor |
| ISBN-10 | 1-4919-2561-2 / 1491925612 |
| ISBN-13 | 978-1-4919-2561-4 / 9781491925614 |
| Zustand | Neuware |
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