If you’re a Python programmer stepping out into the world of data science, this is the right-way to get started. It is also ideal for experienced developers, analysts, or data scientists, who want to work with TensorFlow and Keras. We assume that you are familiar with Python, web application development, Docker commands, and concepts of linear algebra, probability, and statistics.
Applied Deep Learning with Python
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Applied Deep Learning with Python
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Overview of this book
Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before you train your first predictive model. You’ll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics.
After covering classification, you’ll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you’ll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You’ll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you’ll delve into model optimization and evaluation. You’ll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results.
By the end of this book, you’ll be equipped with the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.
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Current Title:
Applied Deep Learning with Python
Table of Contents (9 chapters)
Preface
Free Chapter
Jupyter Fundamentals
Data Cleaning and Advanced Machine Learning
Web Scraping and Interactive Visualizations
Introduction to Neural Networks and Deep Learning
Model Architecture
Model Evaluation and Optimization
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