Deep Learning With Python And Pytorch

Last updated 12/2018
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.83 GB | Duration: 6h 21m

Step into the world of Python and PyTorch to build useful and effective deep learning models for images, text, and more

What you'll learn
Create neural networks to predict the demand for airline travel in the future
Identify negative tweets on Twitter by using Convolutional Neural Networks (CNN's)
Create a neural network which will identify smiles in your camera app
Forecast a company's stock prices for the next day using deep learning models
Learn intuitive ways to build neural networks using the PyTorch API
Master PyTorch's unique features gradually as you work through projects that make PyTorch perfect for rapid prototyping
Debug your PyTorch code using standard Python tools, so you can easily fix bugs

Requirements
Basic understanding of deep learning and Python programming is assumed.

Description
Are you ready to go on a journey into the world of deep learning with the powerful Python and PyTorch? This course will be your guide through the joys and dangers of this new wave of machine learning. Python is quickly becoming the technology of choice for deep learning and machine learning, because of its ease to develop powerful neural networks and intelligent machine learning applications. Like Python, PyTorch has a clean and simple API, which makes building neural networks faster and easier. It's also modular, and that makes debugging your code a breeze. This comprehensive 2-in-1 course will teach you deep learning with Python and PyTorch in an easy-to-understand, practical manner with the help of use cases based on real-world datasets. To begin with, you will create neural networks and deep learning models to predict data and to solve some problems based on the scenarios in the use cases. Next, you will learn how to use Convolutional Neural Networks (CNNs) to classify images, Recurrent Neural Networks (RNNs) to detect languages, and then translate them using Long-Term-Short Memory (LTSM). Finally, you will learn to create Deep Neural Network (DNN) to paint unique images.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.In the first course, Real-World Python Deep Learning Projects, you will start of by creating neural networks to predict the demand for airline travel in the future. You will then 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. In the second course, Deep Learning Adventures with PyTorch, you will start by using Convolutional Neural Networks (CNNs) to classify images; Recurrent Neural Networks (RNNs) to detect languages; and then translate them using Long-Term-Short Memory (LTSM). Finally, you will channel your inner Picasso by using Deep Neural Network (DNN) to paint unique images.By the end of this course, you will be ready to use Python and PyTorch proficiently in your real-world deep learning projects.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Jakub Konczyk has enjoyed programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share them with others. He first discovered Machine Learning when he was trying to predict real estate prices in one of the early stage startups he was involved in. He failed miserably. Then he discovered a much more practical way to learn Machine Learning, which he would like to share with you in this course. It boils down to the Keep it simple! mantra.

Overview
Section 1: Real-World Python Deep Learning Projects

Lecture 1 The Course Overview

Lecture 2 What Types of Problems Can You Solve Using Deep Learning?

Lecture 3 Installing Essential DL Tools

Lecture 4 Based on Past Data, Predicting the Number of Airline Passengers

Lecture 5 Getting and Preparing Airline Data

Lecture 6 Building Your Multilayer Perceptron Model

Lecture 7 Training and Testing Your Model

Lecture 8 Making Predictions and What's Next?

Lecture 9 End Goal – Label a Given Tweet (Short Text) as Negative or Positive

Lecture 10 Dataset Overview

Lecture 11 Preparing Data for Sentiment Analysis

Lecture 12 What Are Word Embeddings and Why They Are Important When Working with CNNs?

Lecture 13 Building Your CNN Model for Text Classification

Lecture 14 Training and Testing Your Model

Lecture 15 Detecting Mean Tweets Using Your Model and What's Next?

Lecture 16 Detect Whether an Image Contains a Smile with High Accuracy

Lecture 17 Getting and Preparing Data for Smile Detection

Lecture 18 Building Your CNN Model for Smile Detection.

Lecture 19 Training and Testing Your Model

Lecture 20 Detecting Smiles with Your Model and What's Next?

Lecture 21 Predict the Closing Stock Price of a Given Company for the Next Day

Lecture 22 Getting and Preparing Stock Prices Data

Lecture 23 Building Your LSTM Model for Price Prediction

Lecture 24 Training and Testing Your Model

Lecture 25 Detecting Closing Stock Price with Your Model and What's Next?

Section 2: Deep Learning Adventures with PyTorch

Lecture 26 The Course Overview

Lecture 27 What Makes PyTorch Special?

Lecture 28 Installing PyTorch

Lecture 29 Problem: Detect a Specific Type of Object in an Image

Lecture 30 Quick Win: Using a Pretrained AlexNet Model for Beaver Detection

Lecture 31 Getting and Preparing Image Data

Lecture 32 Building, Training, and Testing Your Model

Lecture 33 Using Your Model to Detect Beavers and What's Next?

Lecture 34 Problem: Recognize the Language of a Specific Text

Lecture 35 Understanding and Preparing Language Data

Lecture 36 Building, Training, and Testing Your Model for Language Detection

Lecture 37 Using Your Model to Detect Languages and What's Next?

Lecture 38 Problem: Translate a Specific Text from One Language to Another

Lecture 39 Understanding and Preparing Dataset for Language Translation

Lecture 40 Building, Training, and Testing Your Models for Language Translation

Lecture 41 Using Your Models for Language Translation

Lecture 42 Problem: Extract Key Style Features from One Image and Use It on Another One

Lecture 43 Preparing Images for Style Transfer

Lecture 44 Building and Training Style Transfer Model

This course is for Python programmers, Data Science professionals who would like to practically implement Python and PyTorch and exploit its unique features in their deep learning projects.


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