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Introduction to Deep Learning and Neural Networks with Python: A Practical Guide

Introduction to Deep Learning and Neural Networks with Python A Practical Guide

Description

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

Book Details

  • Language: English
  • Published: 2020
  • ASIN: B08P75B9C4
  • Format: True EPUB

Download Links

Introduction to Deep Learning and Neural Networks with Python: A Practical Guide.zip (80.0 MB) | Mirror | Mirrors

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