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Learning Path

Applied Deep Learning with Python

Time to complete: 7h 16m

Published byPackt Publishing

CreatedNovember 2018

Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

In Detail

Taking an approach that uses the latest developments in the Python ecosystem, Applied Deep Learning with Python begins by guiding you through the Jupyter ecosystem, key visualization libraries, and powerful data sanitization techniques before you train our first predictive model. You'll explore a variety of approaches to classification, such as support vector networks, random decision forests, and k-nearest neighbors to build out your understanding before you move into a more complex territory. It's okay if these terms seem overwhelming; you'll learn how to put them to work.

You'll build upon the classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. Then, you'll start building out your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data.

By guiding you through a trained neural network, this Learning Path explores common deep learning network architectures (convolutional, recurrent, generative adversarial) and branches out into deep reinforcement learning before you dive into model optimization and evaluation. You'll do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.

Prerequisites:If you're a Python programmer stepping into the world of data science, Applied Deep Learning with Python is the ideal way to get started. You will grasp the concepts of this Learning Path better if you have some background in Python programming.

Resources: Code downloads and errata:


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