Dark Sebastian. Deep Learning With Python: A Fundamentals Guide To Understanding Machine Learning and Artificial Intelligence With Scikit-Learn, TensorFlow, and Keras

Amazon Digital Services LLC, 2018. — 98 p. — ASIN: B07K2Q6DXH.Deep learning is a process that widens the range of most artificial intelligence problems like speech recognition, image classification, question answering, optical character recognition, and transforming text to speech. It is true that deep learning is a complex subject to learn and understand, but it is not difficult for most machine learning algorithms. If you want to learn how to build a deep learning model in Python, you have come to the right place.Over the course of this book, you will gather information on what deep learning is and how it ties in to machine learning and artificial intelligence. You will also learn about the different neural network architectures that designers use to program a deep learning model. The book provides information on how you can build a deep learning model in Python using Keras. The examples in this book use datasets that are available online. You can download the data from the links mentioned in the book and use these datasets to test your model.
By the time you reach the end of the book, you will have built a solid foundation of building models. You will also learn to build deep learning models for your projects.In this Guide, you will discover...What Machine Learning and Deep Learning Is And How You Can Use It To Change The World
How The Field Can Be Broken Down And Learned In A Manageable Way
Various Applications and Potential of Deep Learning That You Can Utilize - That You May Never Have Even Imagined
Supervised And Unsupervised Learning - And Breaking It Down Step By Step
How You Can Create And Train Deep Learning Models
Where and How To Install the Best Programs So You Can Get Started Today
Sample Codes And Datasets To Practice Along With
And much more!Chapter One: What is Machine Learning?
Chapter Two: An Introduction to Neural Networks
Chapter Three: An Introduction to Deep Learning
Chapter Four: Supervised Learning
Chapter Five: Unsupervised Learning
Chapter Six: How to Create and Train Deep Learning Models
Chapter Seven: Applications of Deep Learning
Chapter Eight: An Introduction to Python
Chapter Nine: Python Environment for Deep Learning
Chapter Ten: How to Develop a Neural Network in Python using Keras
Chapter Eleven: How to Evaluate the Performance of a Deep Learning Model
Chapter Twelve: How to Save and Load Deep Learning Models
Chapter Thirteen: Reducing Dropouts in Deep Learning ModelsSources