Tensorflow 2.0: Deep Learning and Artificial Intelligence

Tensorflow 2.0: Deep Learning and Artificial Intelligence

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 134 lectures (22h 8m) | 6.83 GB

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!

Welcome to Tensorflow 2.0!

What an exciting time. It’s been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.

Tensorflow is Google’s library for deep learning and artificial intelligence.

Deep Learning has been responsible for some amazing achievements recently, such as:

  • Generating beautiful, photo-realistic images of people and things that never existed (GANs)
  • Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)
  • Self-driving cars (Computer Vision)
  • Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)
  • Even creating videos of people doing and saying things they never did (DeepFakes – a potentially nefarious application of deep learning)

Tensorflow is the world’s most popular library for deep learning, and it’s built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.

In other words, if you want to do deep learning, you gotta know Tensorflow.

This course is for beginner-level students all the way up to expert-level students. How can this be?

If you’ve just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.

Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).

Current projects include:

  • Natural Language Processing (NLP)
  • Recommender Systems
  • Transfer Learning for Computer Vision
  • Generative Adversarial Networks (GANs)
  • Deep Reinforcement Learning Stock Trading Bot

Even if you’ve taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.

This course is designed for students who want to learn fast, but there are also “in-depth” sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).

Advanced Tensorflow topics include:

  • Deploying a model with Tensorflow Serving (Tensorflow in the cloud)
  • Deploying a model with Tensorflow Lite (mobile and embedded applications)
  • Distributed Tensorflow training with Distribution Strategies
  • Writing your own custom Tensorflow model
  • Converting Tensorflow 1.x code to Tensorflow 2.0
  • Constants, Variables, and Tensors
  • Eager execution
  • Gradient tape

What you’ll learn

  • Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
  • Predict Stock Returns
  • Time Series Forecasting
  • Computer Vision
  • How to build a Deep Reinforcement Learning Stock Trading Bot
  • GANs (Generative Adversarial Networks)
  • Recommender Systems
  • Image Recognition
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Use Tensorflow Serving to serve your model using a RESTful API
  • Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices
  • Use Tensorflow’s Distribution Strategies to parallelize learning
  • Low-level Tensorflow, gradient tape, and how to build your own custom models
  • Natural Language Processing (NLP) with Deep Learning
  • Demonstrate Moore’s Law using Code
  • Transfer Learning to create state-of-the-art image classifiers
Table of Contents

Welcome
1 Introduction
2 Outline
3 Where to get the code

Google Colab
4 Intro to Google Colab, how to use a GPU or TPU for free
5 Tensorflow 2.0 in Google Colab
6 Uploading your own data to Google Colab
7 Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn
8 How to Succeed in this Course

Machine Learning and Neurons
9 What is Machine Learning
10 Code Preparation (Classification Theory)
11 Classification Notebook
12 Code Preparation (Regression Theory)
13 Regression Notebook
14 The Neuron
15 How does a model learn
16 Making Predictions
17 Saving and Loading a Model
18 Why Keras
19 Suggestion Box

Feedforward Artificial Neural Networks
20 Artificial Neural Networks Section Introduction
21 Beginners Rejoice The Math in This Course is Optional
22 Forward Propagation
23 The Geometrical Picture
24 Activation Functions
25 Multiclass Classification
26 How to Represent Images
27 Code Preparation (ANN)
28 ANN for Image Classification
29 ANN for Regression

Convolutional Neural Networks
30 What is Convolution (part 1)
31 What is Convolution (part 2)
32 What is Convolution (part 3)
33 Convolution on Color Images
34 CNN Architecture
35 CNN Code Preparation
36 CNN for Fashion MNIST
37 CNN for CIFAR-10
38 Data Augmentation
39 Batch Normalization
40 Improving CIFAR-10 Results

Recurrent Neural Networks, Time Series, and Sequence Data
41 Sequence Data
42 Forecasting
43 Autoregressive Linear Model for Time Series Prediction
44 Proof that the Linear Model Works
45 Recurrent Neural Networks
46 RNN Code Preparation
47 RNN for Time Series Prediction
48 Paying Attention to Shapes
49 GRU and LSTM (pt 1)
50 GRU and LSTM (pt 2)
51 A More Challenging Sequence
52 Demo of the Long Distance Problem
53 RNN for Image Classification (Theory)
54 RNN for Image Classification (Code)
55 Stock Return Predictions using LSTMs (pt 1)
56 Stock Return Predictions using LSTMs (pt 2)
57 Stock Return Predictions using LSTMs (pt 3)
58 Other Ways to Forecast

Natural Language Processing (NLP)
59 Embeddings
60 Code Preparation (NLP)
61 Text Preprocessing
62 Text Classification with LSTMs
63 CNNs for Text
64 Text Classification with CNNs

Recommender Systems
65 Recommender Systems with Deep Learning Theory
66 Recommender Systems with Deep Learning Code

Transfer Learning for Computer Vision
67 Transfer Learning Theory
68 Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
69 Large Datasets and Data Generators
70 Approaches to Transfer Learning
71 Transfer Learning Code (pt 1)
72 Transfer Learning Code (pt 2)

GANs (Generative Adversarial Networks)
73 GAN Theory
74 GAN Code

Deep Reinforcement Learning (Theory)
75 Deep Reinforcement Learning Section Introduction
76 Elements of a Reinforcement Learning Problem
77 States, Actions, Rewards, Policies
78 Markov Decision Processes (MDPs)
79 The Return
80 Value Functions and the Bellman Equation
81 What does it mean to “learn”
82 Solving the Bellman Equation with Reinforcement Learning (pt 1)
83 Solving the Bellman Equation with Reinforcement Learning (pt 2)
84 Epsilon-Greedy
85 Q-Learning
86 Deep Q-Learning DQN (pt 1)
87 Deep Q-Learning DQN (pt 2)
88 How to Learn Reinforcement Learning

Stock Trading Project with Deep Reinforcement Learning
89 Reinforcement Learning Stock Trader Introduction
90 Data and Environment
91 Replay Buffer
92 Program Design and Layout
93 Code pt 1
94 Code pt 2
95 Code pt 3
96 Code pt 4
97 Reinforcement Learning Stock Trader Discussion
98 Help! Why is the code slower on my machine

Advanced Tensorflow Usage
99 What is a Web Service (Tensorflow Serving pt 1)
100 Tensorflow Serving pt 2
101 Tensorflow Lite (TFLite)
102 Why is Google the King of Distributed Computing
103 Training with Distributed Strategies
104 Using the TPU

Low-Level Tensorflow
105 Differences Between Tensorflow 1.x and Tensorflow 2.x
106 Constants and Basic Computation
107 Variables and Gradient Tape
108 Build Your Own Custom Model

In-Depth Loss Functions
109 Mean Squared Error
110 Binary Cross Entropy
111 Categorical Cross Entropy

In-Depth Gradient Descent
112 Gradient Descent
113 Stochastic Gradient Descent
114 Momentum
115 Variable and Adaptive Learning Rates
116 Adam (pt 1)
117 Adam (pt 2)

Extras
118 How to Choose Hyperparameters
119 Where Are The Exercises
120 Links to TF2.0 Notebooks

Setting up your Environment (FAQ by Student Request)
121 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
122 Anaconda Environment Setup
123 Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer

Extra Help With Python Coding for Beginners (FAQ by Student Request)
124 Beginner’s Coding Tips
125 How to Code Yourself (part 1)
126 How to Code Yourself (part 2)
127 Proof that using Jupyter Notebook is the same as not using it
128 Is Theano Dead

Effective Learning Strategies for Machine Learning (FAQ by Student Request)
129 How to Succeed in this Course (Long Version)
130 Is this for Beginners or Experts Academic or Practical Fast or slow-paced
131 Machine Learning and AI Prerequisite Roadmap (pt 1)
132 Machine Learning and AI Prerequisite Roadmap (pt 2)

Appendix FAQ Finale
133 What is the Appendix
134 BONUS Lecture

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