Real-World Python Deep Learning Projects [Video]
Deep Learning allows you to solve problems where traditional Machine Learning methods might perform poorly: detecting and extracting objects from images, extracting meaning from text, and predicting outcomes based on complex dependencies, to name a few. In this course you will learn how to use Deep Learning in practice by going through real-world examples.
You will start of by creating neural networks to predict the demand for airline travel in the future. Then, you'll run through a scenario where you have to identify negative tweets for a celebrity by using Convolutional Neural Networks (CNN's). Next you will create a neural network which will be able to identify smiles in your camera app. Finally, the last project will help you forecast a company's stock prices for the next day using Deep Learning.
By the end of this course, you will have a solid understanding of Deep Learning and the ability to build your own Deep Learning models.
The code bundle for this video course is available at - https://github.com/PacktPublishing/Real-World-Python-Deep-Learning-ProjectsStyle and Approach
This course will teach you Deep Learning using easy-to-understand, practical, and clear examples. Each Deep Learning use case is based on a real-world dataset.
|Course Length||3 hours 50 minutes|
|Date Of Publication||31 Oct 2018|
|Preparing Data for Sentiment Analysis|
|What Are Word Embeddings and Why They Are Important When Working with CNNs?|
|Building Your CNN Model for Text Classification|
|Training and Testing Your Model|
|Detecting Mean Tweets Using Your Model and What’s Next?|