Deep Learning with Python: The Ultimate Guide to Understand Artificial Neural Network Using Deep Learning Techniques with Pure Python - Paperback >
/ Deep Learning with Python: The Ultimate Guide to Understand Artificial Neural Network Using Deep Learning Techniques with Pure Python - Paperback

Deep Learning with Python: The Ultimate Guide to Understand Artificial Neural Network Using Deep Learning Techniques with Pure Python - Paperback

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by Jacob Bird (Author)

You're looking for a complete Artificial Neural Network (ANN) guidethat teaches you everything you need to create a Neural Network model in Python,

right?

You've found the right Neural Networks book

This book will get you started in building your FIRST artificial neural network using deep learning techniques.

You should read this book if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.

This book provides you with many practical examples so that you can really see how deep learning can be used on anything.

After completing this course you will be able to:

  • Identify the business problem which can be solved using Neural network Models.

  • Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.

  • Create Neural network models in Python using Keras and Tensorflow libraries and analyze their results.

  • Confidently practice, discuss and understand Deep Learning concepts

By the end of this book, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.

All the materials needed to practice your lessons are FREE.

Go ahead and click the Buy now with 1-Click (R) button

Number of Pages: 138
Dimensions: 0.3 x 9.02 x 5.98 IN
Publication Date: April 03, 2020
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