A hands-on guide to deep learning that’s filled with intuitive explanations and engaging practical examples
Key Features
Designed to iteratively develop the skills of Python users who don’t have a data science background
Covers the key foundational concepts you’ll need to know when building deep learning systems
Full of step-by-step exercises and activities to help build the skills that you need for the real-world
Book Description
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 we train our first predictive model. We’ll explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. It’s okay if these terms seem overwhelming; we’ll show you how to put them to work.
We’ll build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. It’s after this that we start building out our keystone deep learning application, one that aims to predict
