The Applied TensorFlow and Keras Workshop: Develop your practical skills by: working through a real-world project and build your own Bitcoin price prediction tracker
by: Harveen Singh Chadha and Luis Capelo
Print Length 页数: 174 pages
Publisher finelybook 出版社: Packt Publishing; 2nd Revised edition edition (30 July 2020)
Language 语言: English
ISBN-10: 1800201214
ISBN-13: 9781800201217
Book Description
By finelybook
Cut through the noise and get real results with a step-by: -step project-based approach to machine learning with TensorFlow and Keras
Machine learning gives computers the ability to learn. With each passing day,machine learning is becoming increasingly transformational to businesses and is perhaps being used in many more places than one would expect. The Applied TensorFlow and Keras Workshop offers you the practical knowledge and techniques you need to create and contribute to machine learning,deep learning,and modern data analysis using the latest cutting-edge tools.
The workshop begins with showing you how neural networks work. After you understand the basics,you will train a few networks by: altering their hyperparameters. As you progress,you’ll learn how to select the most appropriate model to solve the problem in hand. While learning the advance concepts,you’ll assemble a deep learning system by: bringing together all the essential pieces for building a basic deep learning system: data,model,and prediction. Finally,you’ll explore ways to evaluate the performance of your model and improve it,using techniques such as model evaluation and hyperparameter optimization.
By the end of the book,you’ll have learned how to build a Bitcoin application that predicts the future price and gained the knowledge and confidence to build your own models for other projects.
What you will learn
Get familiar with the components of a neural network
Understand the types of problems that can be solved using neural networks
Explore different ways to select the right architecture for your model
Make predictions with a trained model using TensorBoard
Discover the components of Keras and ways to leverage its features in your model
Explore ways to deal with new data by: learning ways to retrain your model
