Introduction to Deep Learning and Neural Networks with Python™
Introduction to Deep Learning and Neural Networks with Python™: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and Python™ code examples to clarify neural network calculations, by book’s end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and Python™ examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network.
- Examines the practical side of deep learning and neural networks
- Provides a problem-based approach to building artificial neural networks using real data
- Describes Python™ functions and features for neuroscientists
- Uses a careful tutorial approach to describe implementation of neural networks in Python™
- Features math and code examples (via companion website) with helpful instructions for easy implementation
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UPC | 9780323909341 |
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Author | Gad, Ahmed Fawzy, Jarmouni, Fatima Ezzahra |
Pages | 300 |
Language | English |
Format | |
Publisher | Academic Press |
SKU | 9780323909341 |
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