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Data Science: Deep Learning in Python

Видеоуроки





Разместил: daromir

5-07-2017, 17:33

Просмотров: 283





Data Science: Deep Learning in Python


Название: Data Science: Deep Learning in Python
Год: 2017
Жанр: Programming / Design / Marketing / Video course
Автор курса: Lazy Programmer Inc.
Уровень курса: Intermediate
Продолжительность: 06:15:14
Язык: English
Формат: mp4
Размер: 722,4 Мб

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following the previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy.

We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. You'll see how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

Next, we implement a neural network using Google's new TensorFlow library.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!

Table of contents:


What is a neural network? 27:27

Introduction and Outline 03:45
Where does this course fit into your deep learning studies? 04:57
Deep Learning Readiness Test 05:33
Neural Networks with No Math 04:20
Introduction to the E-Commerce Course Project 08:52

Classifying more than 2 things at a time 01:19:58

Prediction: Section Introduction and Outline 05:39
From Logistic Regression to Neural Networks 05:12
Interpreting the Weights of a Neural Network 08:05
Softmax 02:54
Sigmoid vs. Softmax 01:30
Feedforward in Slow-Mo (part 1) 19:42
Feedforward in Slow-Mo (part 2) 10:55
Where to get the code for this course 01:30
Softmax in Code 03:39
Building an entire feedforward neural network in Python 06:23
E-Commerce Course Project: Pre-Processing the Data 05:24
E-Commerce Course Project: Making Predictions 03:55
Prediction Quizzes 03:25
Prediction: Section Summary 01:45

Training a neural network 01:23:46

Training: Section Introduction and Outline 02:49
What do all these symbols and letters mean? 09:45
What does it mean to "train" a neural network? 06:15
Backpropagation Intro 11:53
Backpropagation - what does the weight update depend on? 04:47
Backpropagation - recursiveness 04:37
Backpropagation in code 17:07
The WRONG Way to Learn Backpropagation 03:52
E-Commerce Course Project: Training Logistic Regression with Softmax 08:11
E-Commerce Course Project: Training a Neural Network 06:19
Training Quiz 05:30
Training: Section Summary 02:41

Practical Machine Learning 23:04

Practical Issues: Section Introduction and Outline 01:43
Donut and XOR Review 01:06
Donut and XOR Revisited 04:21
Common nonlinearities and their derivatives 01:26
Hyperparameters and Cross-Validation 04:10
Manually Choosing Learning Rate and Regularization Penalty 04:08
Practical Issues: Section Summary 06:10

TensorFlow, exercises, practice, and what to learn next
41:35

TensorFlow plug-and-play example 07:31
Visualizing what a neural network has learned using TensorFlow Playground 11:35
Where to go from here 03:41
You know more than you think you know 04:52
How to get good at deep learning + exercises 05:07
Deep neural networks in just 3 lines of code with Sci-Kit Learn 08:49

Project: Facial Expression Recognition 56:01

Facial Expression Recognition Problem Description 12:21
The class imbalance problem 06:01
Utilities walkthrough 05:45
Facial Expression Recognition in Code (Binary / Sigmoid) 12:13
Facial Expression Recognition in Code (Logistic Regression Softmax) 08:57
Facial Expression Recognition in Code (ANN Softmax) 10:44

Appendix 01:03:23

Gradient Descent Tutorial 04:30
Help with Softmax Derivative 04:09
Backpropagation with Softmax Troubleshooting 11:55
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:32
How to Code by Yourself (part 1) 15:54
How to Code by Yourself (part 2) 09:23










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