Introduction to Deep Learning and Neural Networks with Python(TM): A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and Python(TM) 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(TM) 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(TM) functions and features for neuroscientists - Uses a careful tutorial approach to describe implementation of neural networks in Python(TM) - Features math and code examples (via companion website) with helpful instructions for easy implementation
Autorentext
Dr. Gad is a data neuroscientist who is passionate about artificial intelligence, machine learning, deep learning, computer vision, and Python with over 7 projects in the fields. He is a researcher at both the University of Ottawa, Canada and Menoufia University, Egypt and also serves in a teaching capacity as an Assistant Lecturer. He has contributed to over 80 original articles and additional tutorials in addition to his previous 3 books. He hopes to continue adding value to the neural data science community by sharing his writings, recorded tutorials, and consultation with new trainees in the field.
Inhalt
1. Preparing the Development Environment
2. Introduction to ANN
3. ANN with 1 Input and 1 Output
4. Working with Any Number of Inputs
5. Working with Hidden Layers
6. Using Any Number of Hidden Neurons
7. ANN with 2 Hidden Layers
8. ANN with 3 Hidden Layers
9. Any Number of Hidden Layers
10. Generic ANN
11. Speeding Neural Network using Cython and PyPy
12. Deploying Neural Network to Mobile Devices