MP4 | Video: AVC 1920x1080 | Audio: AAC 48KHz 2ch | Duration: 3 hours 50 minutes | English | 798 MB
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.
Style 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.
Table of Contents
EXPLORING ESSENTIAL DEEP LEARNING TERMS AND TOOLS
PREDICTING DEMAND FOR AIRLINE TRAVEL
IDENTIFYING MEAN TWEETS
DETECTING SMILES IN YOUR CAMERA APP
PREDICTING STOCK PRICES USING LSTM