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.

About the Author
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. Fatima Ezzahra Jarmouni is an M.Sc. junior data scientist interested in statistics, data science, machine learning, and deep learning. Currently enrolled in a PhD program in machine learning at ENSIAS. She codes with Python and has experience in Python data science libraries including NumPy, Scikit-Learn, TensorFlow, and Keras.


Book Information
ISBN 9780323909334
Author Ahmed Fawzy Gad
Format Paperback
Page Count 300
Imprint Academic Press Inc
Publisher Elsevier Science & Technology
Weight(grams) 480g