Harveen Singh Chadha - 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

Machine learning gives computers the ability to learn like humans. It is becoming increasingly transformational to businesses in many forms, and a key skill to learn to prepare for the future digital economy.

As a beginner, you'll unlock a world of opportunities by learning the techniques you need to contribute to the domains of machine learning, deep learning, and modern data analysis using the latest cutting-edge tools.

The Applied TensorFlow and Keras Workshop begins by showing you how neural networks work. After you've understood the basics, you will train a few networks by altering their hyperparameters. To build on your skills, you'll learn how to select the most appropriate model to solve the problem in hand. While tackling advanced concepts, you'll discover how to assemble a deep learning system by bringing together all the essential elements necessary 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 this book, you'll have learned how to build a Bitcoin app that predicts future prices, and be able to build your own models for other projects.

If you are a data scientist or a machine learning and deep learning enthusiast, who is looking to design, train, and deploy TensorFlow and Keras models into real-world applications, then this workshop is for you. Knowledge of computer science and machine learning concepts and experience in analyzing data will help you to understand the topics explained in this book with ease.

Chapter 1, Introduction to Neural Networks and Deep Learning, gets us to select a TensorFlow-trained neural network using TensorBoard and trains the neural network by varying epochs and learning rates. This will give you hands-on experience of how to train a highly performant neural network and allow you to explore some of its limitations.

Chapter 2, Real-World Deep Learning: Predicting the Price of Bitcoin, teaches us how to assemble a complete deep learning system, from data input to prediction. The model created will serve as a benchmark from which we will be able to make improvements.

Chapter 3, Real-World Deep Learning: Evaluating the Bitcoin Model, focuses on how to evaluate a neural network model. Using hyperparameter tuning, we will improve the performance of the network. However, before altering any parameters, we need to measure how the model performs. By the end of this chapter, you will be able to evaluate a model using different functions and techniques.

Chapter 4,Productization, covers the handling of new data. We will create a model that is able to learn continuously from the patterns it's shown and make better predictions. We will use a web application as an example to demonstrate how to deploy deep learning models.

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