English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 50m | 798 MB
Identify mean tweets, detect smiles in your camera app, forecast stock prices, and more using Neural Networks
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.
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.
What You Will Learn
- Build a solid understanding of common problems can you solve with Deep Learning
- Use different Deep Learning algorithms to solve specific types of problem and learn their strengths and weaknesses,
- Develop a clear understanding of how Deep Learning tools work and what you need to know to use them in practice
- Discover the practical pros and cons of using Deep Learning
- Save time by learning practical Deep Learning methods that you can immediately apply to real-world problems.
Table of Contents
Exploring Essential Deep Learning Terms and Tools
1 The Course Overview
2 What Types of Problems Can You Solve Using Deep Learning
3 Installing Essential DL Tools
Predicting Demand for Airline Travel
4 Based on Past Data, Predicting the Number of Airline Passengers
5 Getting and Preparing Airline Data
6 Building Your Multilayer Perceptron Model
7 Training and Testing Your Model
8 Making Predictions and What’s Next
Identifying Mean Tweets
9 End Goal – Label a Given Tweet (Short Text) as Negative or Positive
10 Dataset Overview
11 Preparing Data for Sentiment Analysis
12 What Are Word Embeddings and Why They Are Important When Working with CNNs
13 Building Your CNN Model for Text Classification
14 Training and Testing Your Model
15 Detecting Mean Tweets Using Your Model and What’s Next
Detecting Smiles in Your Camera App
16 Detect Whether an Image Contains a Smile with High Accuracy
17 Getting and Preparing Data for Smile Detection
18 Building Your CNN Model for Smile Detection.
19 Training and Testing Your Model
20 Detecting Smiles with Your Model and What’s Next
Predicting Stock Prices Using LSTM
21 Predict the Closing Stock Price of a Given Company for the Next Day
22 Getting and Preparing Stock Prices Data
23 Building Your LSTM Model for Price Prediction
24 Training and Testing Your Model
25 Detecting Closing Stock Price with Your Model and What’s Next
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