Taking an approach that uses the latest developments in the Python ecosystem, youβll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. Weβll 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. Itβs okay if these terms seem overwhelming; weβll show you how to put them to work.
Weβll 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. Itβs after this that we start building out our 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, weβll explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. Weβll 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.