Introduction to Deep Learning and Neural Networks with Python™ A Practical Guide
Auteurs : Gad Ahmed Fawzy, Jarmouni Fatima Ezzahra
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.
- 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
Date de parution : 11-2020
Ouvrage de 300 p.
15x22.8 cm
Thèmes d’Introduction to Deep Learning and Neural Networks with... :
Mots-clés :
Android; ANN; Architecture; backward pass; Backward pass; Bias; Code; Complete code; Constant; Development environment; Error calculation; Forward pass; Gradient; Gradients; Hidden layers; Hidden layers; Hidden layers; Inputs; Inputs assignment; Iteration; Kivy; Kivy app; MD dropdown menu; MDCheckbox; MDSpinner; Multiple training samples; Network architecture; Neural networks; Neurons; Outputs; Parameter; PyPy; Python 3; Python implementation; Python implementation; Scratch; SOP; Training network; Ubuntu virtual machine; Variable; Weight; Weights; Weights initialization