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Book Overview & Buying
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Table Of Contents
Python for Deep Learning — Build Neural Networks in Python
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Python for Deep Learning — Build Neural Networks in Python
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Overview of this book
Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it's no secret that Python’s best application is in deep learning and artificial intelligence (AI) tasks.
You’ll start with an introduction to deep learning where you’ll focus on the fundamentals of the deep learning theory and find out how to use deep learning in Python, before moving on to artificial neural networks (ANNs). You’ll learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence. Next, you’ll discover how to make predictions using linear regression, polynomial regression, and multivariate regression, and build artificial neural networks with TensorFlow and Keras. The video also covers convolutional neural networks (CNNs) at length and goes through the different components such as convolution layer, pooling layer, and fully connected layer. Finally, you’ll wrap up CNN implementation in Python.
By the end of this course, you’ll be able to use the concepts of deep learning to build neural networks in Python like a professional.
Table of Contents (10 chapters)
Introduction to Deep Learning
Artificial Neural Networks (ANN)
Propagation of Information in ANNs
Neural Network Architectures
Activation Functions
Gradient Descent Algorithm
Summary - Overview of Neural Networks
Implementation of ANN in Python
Convolutional Neural Networks (CNN)
Implementation of CNN in Python
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