English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 11h 09m | 1.58 GB

The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my 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. All the materials for this course are FREE.

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. I show you 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. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.

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!

After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks – slightly modified architectures and what they are used for.

What you’ll learn

- Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)
- Learn how a neural network is built from basic building blocks (the neuron)
- Code a neural network from scratch in Python and numpy
- Code a neural network using Google’s TensorFlow
- Describe different types of neural networks and the different types of problems they are used for
- Derive the backpropagation rule from first principles
- Create a neural network with an output that has K > 2 classes using softmax
- Describe the various terms related to neural networks, such as “activation”, “backpropagation” and “feedforward”
- Install TensorFlow

## Table of Contents

**Welcome**

1 Introduction and Outline

2 Where to get the code

3 How to Succeed in this Course

**Review**

4 Review Section Introduction

5 What does machine learning do

6 Neuron Predictions

7 Neuron Training

8 Deep Learning Readiness Test

9 Review Section Summary

**Preliminaries From Neurons to Neural Networks**

10 Neural Networks with No Math

11 Introduction to the E-Commerce Course Project

**Classifying more than 2 things at a time**

12 Prediction Section Introduction and Outline

13 From Logistic Regression to Neural Networks

14 Interpreting the Weights of a Neural Network

15 Softmax

16 Sigmoid vs. Softmax

17 Feedforward in Slow-Mo (part 1)

18 Feedforward in Slow-Mo (part 2)

19 Where to get the code for this course

20 Softmax in Code

21 Building an entire feedforward neural network in Python

22 E-Commerce Course Project Pre-Processing the Data

23 E-Commerce Course Project Making Predictions

24 Prediction Quizzes

25 Prediction Section Summary

26 Suggestion Box

**Training a neural network**

27 Training Section Introduction and Outline

28 What do all these symbols and letters mean

29 What does it mean to train a neural network

30 How to Brace Yourself to Learn Backpropagation

31 Categorical Cross-Entropy Loss Function

32 Training Logistic Regression with Softmax (part 1)

33 Training Logistic Regression with Softmax (part 2)

34 Backpropagation (part 1)

35 Backpropagation (part 2)

36 Backpropagation in code

37 Backpropagation (part 3)

38 The WRONG Way to Learn Backpropagation

39 E-Commerce Course Project Training Logistic Regression with Softmax

40 E-Commerce Course Project Training a Neural Network

41 Training Quiz

42 Training Section Summary

**Practical Machine Learning**

43 Practical Issues Section Introduction and Outline

44 Donut and XOR Review

45 Donut and XOR Revisited

46 Neural Networks for Regression

47 Common nonlinearities and their derivatives

48 Practical Considerations for Choosing Activation Functions

49 Hyperparameters and Cross-Validation

50 Manually Choosing Learning Rate and Regularization Penalty

51 Why Divide by Square Root of D

52 Practical Issues Section Summary

**TensorFlow, exercises, practice, and what to learn next**

53 TensorFlow plug-and-play example

54 Visualizing what a neural network has learned using TensorFlow Playground

55 Where to go from here

56 You know more than you think you know

57 How to get good at deep learning + exercises

58 Deep neural networks in just 3 lines of code with Sci-Kit Learn

**Project Facial Expression Recognition**

59 Facial Expression Recognition Project Introduction

60 Facial Expression Recognition Problem Description

61 The class imbalance problem

62 Utilities walkthrough

63 Facial Expression Recognition in Code (Binary Sigmoid)

64 Facial Expression Recognition in Code (Logistic Regression Softmax)

65 Facial Expression Recognition in Code (ANN Softmax)

66 Facial Expression Recognition Project Summary

**Backpropagation Supplementary Lectures**

67 Backpropagation Supplementary Lectures Introduction

68 Why Learn the Ins and Outs of Backpropagation

69 Gradient Descent Tutorial

70 Help with Softmax Derivative

71 Backpropagation with Softmax Troubleshooting

**Higher-Level Discussion**

72 What’s the difference between neural networks and deep learning

73 Who should take this course in 2020 and beyond

74 Who should learn backpropagation in 2020 and beyond

75 Where does this course fit into your deep learning studies

**Setting Up Your Environment (FAQ by Student Request)**

76 Windows-Focused Environment Setup 2018

77 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

**Extra Help With Python Coding for Beginners (FAQ by Student Request)**

78 How to Uncompress a .tar.gz file

79 How to Code by Yourself (part 1)

80 How to Code by Yourself (part 2)

81 Proof that using Jupyter Notebook is the same as not using it

82 Python 2 vs Python 3

**Effective Learning Strategies for Machine Learning (FAQ by Student Request)**

83 How to Succeed in this Course (Long Version)

84 Is this for Beginners or Experts Academic or Practical Fast or slow-paced

85 Where does this course fit into your deep learning studies (Old Version)

86 Machine Learning and AI Prerequisite Roadmap (pt 1)

87 Machine Learning and AI Prerequisite Roadmap (pt 2)

**Appendix FAQ Finale**

88 What is the Appendix

89 BONUS Where to get Udemy coupons and FREE deep learning material

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