| Title : | Fundamentals of deep learning : designing next-generation machine intelligence algorithms | | Material Type: | printed text | | Authors: | Nikhil Buduma, Author | | Publisher: | O'Reilly Media, Inc, USA | | Publication Date: | 2017 | | Pagination: | xii-283 p | | Layout: | ill | | Size: | 24 cm | | ISBN (or other code): | 978-1-491-92561-4 | | General note: | Includes bibliographical references and index. | | Languages : | English (eng) Original Language : English (eng) | | Descriptors: | Artificial intelligence
Deep learning
Machine learning
Neural networks (Computer science)
| | Class number: | 006.3 | | Abstract: | "Fundamentals of deep learning : designing next-generation machine intelligence algorithms" - 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
Understand the fundamentals of reinforcement learning | | Contents note: | The neural network; Training feed-forward neural networks; Implementing neural networks in TensorFlow; Beyond gradient descent; Convolutional neural networks; Embedding and representation learning; Models for sequence analysis; Memory augmented neural networks; Deep reinforcement learning. | | Record link: | https://library.seeu.edu.mk/index.php?lvl=notice_display&id=18469 |
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