English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 133 lectures (13h 5m) | 2.58 GB

Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0

Welcome to Tensorflow 2.0!

TensorFlow 2.0 has just been released, and it introduced many features that simplify the model development and maintenance processes. From the educational side, it boosts people’s understanding by simplifying many complex concepts. From the industry point of view, models are much easier to understand, maintain, and develop.

Deep Learning is one of the fastest growing areas of Artificial Intelligence. In the past few years, we have proven that Deep Learning models, even the simplest ones, can solve very hard and complex tasks. Now, that the buzz-word period of Deep Learning has, partially, passed, people are releasing its power and potential for their product improvements.

The course is structured in a way to cover all topics from neural network modeling and training to put it in production.

In Part 1 of the course, you will learn about the technology stack that we will use throughout the course (Section 1) and the TensorFlow 2.0 library basics and syntax (Section 2).

In Part 2 of the course, we will dig into the exciting world of deep learning. Through this part of the course, you will implement several types of neural networks (Fully Connected Neural Network (Section 3), Convolutional Neural Network (Section 4), Recurrent Neural Network (Section 5)). At the end of this part, Section 6, you will learn and build their own Transfer Learning application that achieves state of the art (SOTA) results on the Dogs vs. Cats dataset.

After passing the part 2 of the course and ultimately learning how to implement neural networks, in Part 3 of the course, you will learn how to make your own Stock Market trading bot using Reinforcement Learning, specifically Deep-Q Network.

Part 4 is all about TensorFlow Extended (TFX). In this part of the course, you will learn how to work with data and create your own data pipelines for production. In Section 8 we will check if the dataset has any anomalies using the TensorFlow Data Validation library and after learn how to check a dataset for anomalies, in Section 9, we will make our own data preprocessing pipeline using the TensorFlow Transform library.

In Section 10 of the course, you will learn and create your own Fashion API using the Flask Python library and a pre-trained model. Throughout this section, you will get a better picture of how to send a request to a model over the internet. However, at this stage, the architecture around the model is not scalable to millions of request. Enter the Section 11. In this section of the course, you will learn how to improve solution from the previous section by using the TensorFlow Serving library. In a very easy way, you will learn and create your own Image Classification API that can support millions of requests per day!

These days it is becoming more and more popular to have a Deep Learning model inside an Android or iOS application, but neural networks require a lot of power and resources! That’s where the TensorFlow Lite library comes into play. In Section 12 of the course, you will learn how to optimize and convert any neural network to be suitable for a mobile device.

To conclude with the learning process and the Part 5 of the course, in Section 13 you will learn how to distribute the training of any Neural Network to multiple GPUs or even Servers using the TensorFlow 2.0 library.

What you’ll learn

- How to use Tensorflow 2.0 in Data Science
- Important differences between Tensorflow 1.x and Tensorflow 2.0
- How to implement Artificial Neural Networks in Tensorflow 2.0
- How to implement Convolutional Neural Networks in Tensorflow 2.0
- How to implement Recurrent Neural Networks in Tensorflow 2.0
- How to build your own Transfer Learning application in Tensorflow 2.0
- How to build a stock market trading bot using Reinforcement Learning (Deep-Q Network)
- How to build Machine Learning Pipeline in Tensorflow 2.0
- How to conduct Data Validation and Dataset Preprocessing using TensorFlow Data Validation and TensorFlow Transform.
- Putting a TensorFlow 2.0 model into production
- How to create a Fashion API with Flask and TensorFlow 2.0
- How to serve a TensorFlow model with RESTful API

## Table of Contents

**Introduction**

1 Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit

2 Course Curriculum & Colab Toolkit

3 BONUS 10 advantages of TensorFlow

4 BONUS Learning Path

**TensorFlow 2.0 Basics**

5 From TensorFlow 1.x to TensorFlow 2.0

6 Constants, Variables, Tensors

7 Operations with Tensors

8 Strings

**Artificial Neural Networks**

9 Project Setup

10 Data Preprocessing

11 Building the Artificial Neural Network

12 Training the Artificial Neural Network

13 Evaluating the Artificial Neural Network

14 HOMEWORK Artificial Neural Networks

15 HOMEWORK SOLUTION Artificial Neural Networks

**Convolutional Neural Networks**

16 Project Setup & Data Preprocessing

17 Building the Convolutional Neural Network

18 Training and Evaluating the Convolutional Neural Network

19 HOMEWORK Convolutional Neural Networks

20 HOMEWORK SOLUTION Convolutional Neural Networks

**Recurrent Neural Networks**

21 Project Setup & Data Preprocessing

22 Building the Recurrent Neural Network

23 Training and Evaluating the Recurrent Neural Network

**Transfer Learning and Fine Tuning**

24 What is Transfer Learning

25 Project Setup

26 Dataset preprocessing

27 Loading the MobileNet V2 model

28 Freezing the pre-trained model

29 Adding a custom head to the pre-trained model

30 Defining the transfer learning model

31 Compiling the Transfer Learning model

32 Image Data Generators

33 Transfer Learning

34 Evaluating Transfer Learning results

35 Fine Tuning model definition

36 Compiling the Fine Tuning model

37 Fine Tuning

38 Evaluating Fine Tuning results

**Deep Reinforcement Learning Theory**

39 What is Reinforcement Learning

40 The Bellman Equation

41 Markov Decision Process (MDP)

42 Q-Learning Intuition

43 Temporal Difference

44 Deep Q-Learning Intuition – Step 1

45 Deep Q-Learning Intuition – Step 2

46 Experience Replay

47 Action Selection Policies

**Deep Reinforcement Learning for Stock Market trading**

48 Project Setup

49 AI Trader – Step 1

50 AI Trader – Step 2

51 AI Trader – Step 3

52 AI Trader – Step 4

53 AI Trader – Step 5

54 Dataset Loader function

55 State creator function

56 Loading the dataset

57 Defining the model

58 Training loop – Step 1

59 Training loop – Step 2

**Data Validation with TensorFlow Data Validation (TFDV)**

60 Project Setup

61 Loading the pollution dataset

62 Creating dataset Schema

63 Computing test set statistics

64 Anomaly detection with TensorFlow Data Validation

65 Preparing Schema for production

66 Saving the Schema

67 What’s next

**Dataset Preprocessing with TensorFlow Transform (TFT)**

68 Project Setup

69 Initial dataset preprocessing

70 Dataset metadata

71 Preprocessing function

72 Dataset preprocessing pipeline

73 What’s next

**Fashion API with Flask and TensorFlow 2.0**

74 Project Setup

75 Importing project dependencies

76 Loading a pre-trained model

77 Defining the Flask application

78 Creating classify function

79 Starting the Flask application

80 Sending API requests over internet to the model

**Image Classification API with TensorFlow Serving**

81 What is the TensorFlow Serving

82 TensorFlow Serving architecture

83 Project setup

84 Dataset preprocessing

85 Defining, training and evaluating a model

86 Saving the model for production

87 Serving the TensorFlow 2.0 Model

88 Creating a JSON object

89 Sending the first POST request to the model

90 Sending the POST request to a specific model

**TensorFlow Lite Prepare a model for a mobile device**

91 What is the TensorFlow Lite

92 Project setup

93 Dataset preprocessing

94 Building a model

95 Training, evaluating the model

96 Saving the model

97 TensorFlow Lite Converter

98 Converting the model to a TensorFlow Lite model

99 Saving the converted model

100 What’s next

**Distributed Training with TensorFlow 2.0**

101 What is the Distributed Training

102 Project Setup

103 Dataset preprocessing

104 Defining a non-distributed model (normal CNN model)

105 Setting up a distributed strategy

106 Defining a distributed model

107 Final evaluation – Speed test normal model vs distributed model

**Annex 1 – Artificial Neural Networks Theory**

108 Plan of Attack

109 The Neuron

110 The Activation Function

111 How do Neural Networks Work

112 How do Neural Networks Learn

113 Gradient Descent

114 Stochastic Gradient Descent

115 Backpropagation

**Annex 2 – Convolutional Neural Networks Theory**

116 Plan of Attack

117 What are Convolutional Neural Networks

118 Step 1 – Convolution

119 Step 1 Bis – ReLU Layer

120 Step 2 – Max Pooling

121 Step 3 – Flattening

122 Step 4 – Full Connection

123 Summary

124 Softmax & Cross-Entropy

**Annex 3 – Recurrent Neural Networks Theory**

125 Plan of Attack

126 What are Recurrent Neural Networks

127 Vanishing Gradient

128 LSTMs

129 LSTM Practical Intuition

130 LSTM Variations

**Bonus Lectures**

131 SPECIAL COVID-19 BONUS

132 YOUR SPECIAL BONUS

133 FREE LEARNING RESOURCES FOR YOU

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