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Real-World Python Deep Learning Projects

Video Description

Identify mean tweets, detect smiles in your camera app, forecast stock prices, and more using Neural Networks

About This Video

  • Explore the practical essence of Deep Learning in a relatively short amount of time by working on practical, real-world use cases.
  • Learn which classes of problem Deep Learning is most effective in solving
  • Work with the best tools to get started with Deep Learning in your real-life projects

In Detail

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-Projects

Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Table of Contents

  1. Chapter 1 : Exploring Essential Deep Learning Terms and Tools
    1. The Course Overview 00:05:19
    2. What Types of Problems Can You Solve Using Deep Learning? 00:04:34
    3. Installing Essential DL Tools 00:08:17
  2. Chapter 2 : Predicting Demand for Airline Travel
    1. Based on Past Data, Predicting the Number of Airline Passengers 00:02:37
    2. Getting and Preparing Airline Data 00:08:26
    3. Building Your Multilayer Perceptron Model 00:08:16
    4. Training and Testing Your Model 00:23:56
    5. Making Predictions and What's Next? 00:08:48
  3. Chapter 3 : Identifying Mean Tweets
    1. End Goal – Label a Given Tweet (Short Text) as Negative or Positive 00:02:27
    2. Dataset Overview 00:05:15
    3. Preparing Data for Sentiment Analysis 00:15:01
    4. What Are Word Embeddings and Why They Are Important When Working with CNNs? 00:07:42
    5. Building Your CNN Model for Text Classification 00:11:54
    6. Training and Testing Your Model 00:12:18
    7. Detecting Mean Tweets Using Your Model and What’s Next? 00:15:55
  4. Chapter 4 : Detecting Smiles in Your Camera App
    1. Detect Whether an Image Contains a Smile with High Accuracy 00:01:52
    2. Getting and Preparing Data for Smile Detection 00:13:37
    3. Building Your CNN Model for Smile Detection. 00:16:52
    4. Training and Testing Your Model 00:07:12
    5. Detecting Smiles with Your Model and What’s Next? 00:16:00
  5. Chapter 5 : Predicting Stock Prices Using LSTM
    1. Predict the Closing Stock Price of a Given Company for the Next Day 00:01:29
    2. Getting and Preparing Stock Prices Data 00:09:57
    3. Building Your LSTM Model for Price Prediction 00:07:16
    4. Training and Testing Your Model 00:04:51
    5. Detecting Closing Stock Price with Your Model and What’s Next? 00:10:53