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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

3 Download All Resources and Important FAQ

**Introduction to neural networks**

4 Introduction to neural networks

5 Training the model

6 Types of machine learning

7 The linear model

8 Need Help with Linear Algebra

9 The linear model. Multiple inputs

10 The linear model. Multiple inputs and multiple outputs

11 Graphical representation

12 The objective function

13 L2-norm loss

14 Cross-entropy loss

15 One parameter gradient descent

16 N-parameter gradient descent

**Setting up the working environment**

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

18 Why Python and why Jupyter

19 Installing Anaconda

20 The Jupyter dashboard – part 1

21 The Jupyter dashboard – part 2

22 Jupyter Shortcuts

23 Installing TensorFlow 2

24 Installing packages – exercise

25 Installing packages – solution

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

26 Minimal example – part 1

27 Minimal example – part 2

28 Minimal example – part 3

29 Minimal example – part 4

30 Minimal example – Exercises

**TensorFlow – An introduction**

31 TensorFlow outline

32 TensorFlow 2 intro

33 A Note on Coding in TensorFlow

34 Types of file formats in TensorFlow and data handling

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

36 Interpreting the result and extracting the weights and bias

37 Cutomizing your model

38 Minimal example with TensorFlow – Exercises

**Going deeper Introduction to deep neural networks**

39 Layers

40 What is a deep net

41 Understanding deep nets in depth

42 Why do we need non-linearities

43 Activation functions

44 Softmax activation

45 Backpropagation

46 Backpropagation – visual representation

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

47 Backpropagation. A peek into the Mathematics of Optimization

**Overfitting**

48 Underfitting and overfitting

49 Underfitting and overfitting – classification

50 Training and validation

51 Training, validation, and test

52 N-fold cross validation

53 Early stopping

**Initialization**

54 Initialization – Introduction

55 Types of simple initializations

56 Xavier initialization

**Gradient descent and learning rates**

57 Stochastic gradient descent

58 Gradient descent pitfalls

59 Momentum

60 Learning rate schedules

61 Learning rate schedules. A picture

62 Adaptive learning rate schedules

63 Adaptive moment estimation

**Preprocessing**

64 Preprocessing introduction

65 Basic preprocessing

66 Standardization

67 Dealing with categorical data

68 One-hot and binary encoding

**The MNIST example**

69 The dataset

70 How to tackle the MNIST

71 Importing the relevant packages and load the data

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

73 Preprocess the data – scale the test data

74 Preprocess the data – shuffle and batch the data

75 Preprocess the data – shuffle and batch the data

76 Outline the model

77 Select the loss and the optimizer

78 Learning

79 MNIST – exercises

80 MNIST – solutions

81 Testing the model

**Business case**

82 Exploring the dataset and identifying predictors

83 Outlining the business case solution

84 Balancing the dataset

85 Preprocessing the data

86 Preprocessing exercise

87 Load the preprocessed data

88 Load the preprocessed data – Exercise

89 Learning and interpreting the result

90 Setting an early stopping mechanism

91 Setting an early stopping mechanism – Exercise

92 Testing the model

93 Final exercise

**Appendix Linear Algebra Fundamentals**

94 What is a Matrix

95 Scalars and Vectors

96 Linear Algebra and Geometry

97 Scalars, Vectors and Matrices in Python

98 Tensors

99 Addition and Subtraction of Matrices

100 Errors when Adding Matrices

101 Transpose of a Matrix

102 Dot Product of Vectors

103 Dot Product of Matrices

104 Why is Linear Algebra Useful

**Conclusion**

105 See how much you have learned

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

107 An overview of CNNs

108 How DeepMind uses deep learning

109 An overview of RNNs

110 An overview of non-NN approaches

**Bonus lecture**

111 Bonus lecture Next steps

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