**Deep Learning with TensorFlow 2.0 [2019]**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 6 Hours | 1.97 GB

Build Deep Learning Algorithms with TensorFlow 2.0, Dive into Neural Networks and Apply Your Skills in a Business Case

Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what is that one special thing they have in common?

They are all masters of deep learning.

We often hear about AI, or self-driving cars, or the ‘algorithmic magic’ at Google, Facebook, and Amazon. But it is not magic – it is deep learning. And more specifically, it is usually deep neural networks – the one algorithm to rule them all.

Cool, that sounds like a really important skill; how do I become a Master of Deep Learning?

There are two routes you can take:

The unguided route – This route will get you where you want to go, eventually, but expect to get lost a few times. If you are looking at this course you’ve maybe been there.

The 365 route – Consider our route as the guided tour. We will take you to all the places you need, using the paths only the most experienced tour guides know about. We have extra knowledge you won’t get from reading those information boards and we give you this knowledge in fun and easy-to-digest methods to make sure it really sticks.

Clearly, you can talk the talk, but can you walk the walk? – What exactly will I get out of this course that I can’t get anywhere else?

Good question! We know how interesting Deep Learning is and we love it! However, we know that the goal here is career progression, that’s why our course is business focused and gives you real world practice on how to use Deep Learning to optimize business performance.

We don’t just scratch the surface either – It’s not called ‘Skin-Deep’ Learning after all. We fully explain the theory from the mathematics behind the algorithms to the state-of-the-art initialization methods, plus so much more.

Theory is no good without putting it into practice, is it? That’s why we give you plenty of opportunities to put this theory to use. Implement cutting edge optimizations, get hands on with TensorFlow and even build your very own algorithm and put it through training!

Wow, that’s going to look great on your resume!

Speaking of resumes, you also get a certificate upon completion which employers can verify that you have successfully finished a prestigious 365 Careers course – and one of our best at that!

Now, I can see you’re bragging a little, but I admit you have peaked my interest. What else does your course offer that will make my resume shine?

Trust us, after this course you’ll be able to fill your resume with skills and have plenty left over to show off at the interview.

- Of course, you’ll get fully acquainted with Google’ TensorFlow and NumPy, two tools essential for creating and understanding Deep Learning algorithms.
- Explore layers, their building blocks and activations – sigmoid, tanh, ReLu, softmax, etc.
- Understand the backpropagation process, intuitively and mathematically.
- You’ll be able to spot and prevent overfitting – one of the biggest issues in machine and deep learning
- Get to know the state-of-the-art initialization methods. Don’t know what initialization is? We explain that, too
- Learn how to build deep neural networks using real data, implemented by real companies in the real world. TEMPLATES included!
- Also, I don’t know if we’ve mentioned this, but you will have created your very own Deep Learning Algorithm after only 1 hour of the course.
- It’s this hands-on experience that will really make your resume stand out

What you’ll learn

- Gain a Strong Understanding of TensorFlow – Google’s Cutting-Edge Deep Learning Framework
- Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow
- Set Yourself Apart with Hands-on Deep and Machine Learning Experience
- Grasp the Mathematics Behind Deep Learning Algorithms
- Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules
- Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization
- Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding

**Table of Contents**

**Welcome! Course introduction**

1 Meet your instructors and why you should study machine learning?

2 What does the course cover?

**Introduction to neural networks**

3 Introduction to neural networks

4 Training the model

5 Types of machine learning

6 The linear model

7 Need Help with Linear Algebra?

8 The linear model. Multiple inputs

9 The linear model. Multiple inputs and multiple outputs

10 Graphical representation

11 The objective function

12 L2-norm loss

13 Cross-entropy loss

14 One parameter gradient descent

15 N-parameter gradient descent

**Setting up the working environment**

16 Setting up the environment – An introduction – Do not skip, please!

17 Why Python and why Jupyter?

18 Installing Anaconda

19 The Jupyter dashboard – part 1

20 The Jupyter dashboard – part 2

21 Jupyter Shortcuts

22 Installing TensorFlow 2

23 Installing packages – exercise

24 Installing packages – solution

**Minimal example – your first machine learning algorithm**

25 Minimal example – part 1

26 Minimal example – part 2

27 Minimal example – part 3

28 Minimal example – part 4

29 Minimal example – Exercises

**TensorFlow – An introduction**

30 TensorFlow outline

31 TensorFlow 2 intro

32 A Note on Coding in TensorFlow

33 Types of file formats in TensorFlow and data handling

34 Model layout – inputs, outputs, targets, weights, biases, optimizer and loss

35 Interpreting the result and extracting the weights and bias

36 Cutomizing your model

37 Minimal example – Exercises

**Going deeper: Introduction to deep neural networks**

38 Layers

39 What is a deep net?

40 Understanding deep nets in depth

41 Why do we need non-linearities?

42 Activation functions

43 Softmax activation

44 Backpropagation

45 Backpropagation – visual representation

**Backpropagation. A peek into the Mathematics of Optimization**

46 Backpropagation. A peek into the Mathematics of Optimization

**Overfitting**

47 Underfitting and overfitting

48 Underfitting and overfitting – classification

49 Training and validation

50 Training, validation, and test

51 N-fold cross validation

52 Early stopping

**Initialization**

53 Initialization – Introduction

54 Types of simple initializations

55 Xavier initialization

**Gradient descent and learning rates**

56 Stochastic gradient descent

57 Gradient descent pitfalls

58 Momentum

59 Learning rate schedules

60 Learning rate schedules. A picture

61 Adaptive learning rate schedules

62 Adaptive moment estimation

**Preprocessing**

63 Preprocessing introduction

64 Basic preprocessing

65 Standardization

66 Dealing with categorical data

67 One-hot and binary encoding

**The MNIST example**

68 The dataset

69 How to tackle the MNIST

70 Importing the relevant packages and load the data

71 Preprocess the data – create a validation dataset and scale the data

72 Preprocess the data – scale the test data

73 Preprocess the data – shuffle and batch the data

74 Preprocess the data – shuffle and batch the data

75 Outline the model

76 Select the loss and the optimizer

77 Learning

78 MNIST – exercises

79 MNIST – solutions

80 Testing the model

**Business case**

81 Exploring the dataset and identifying predictors

82 Outlining the business case solution

83 Balancing the dataset

84 Preprocessing the data

85 Preprocessing exercise

86 Load the preprocessed data

87 Load the preprocessed data – Exercise

88 Learning and interpreting the result

89 Setting an early stopping mechanism

90 Setting an early stopping mechanism – Exercise

91 Testing the model

92 Final exercise

**Appendix: Linear Algebra Fundamentals**

93 What is a Matrix?

94 Scalars and Vectors

95 Linear Algebra and Geometry

96 Scalars, Vectors and Matrices in Python

97 Tensors

98 Addition and Subtraction of Matrices

99 Errors when Adding Matrices

100 Transpose of a Matrix

101 Dot Product of Vectors

102 Dot Product of Matrices

103 Why is Linear Algebra Useful?

**Conclusion**

104 See how much you have learned

105 What’s further out there in the machine and deep learning world

106 An overview of CNNs

107 How DeepMind uses deep learning

108 An overview of RNNs

109 An overview of non-NN approaches

**Bonus lecture**

110 Bonus lecture: Next steps

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