English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 82 lectures (12h 36m) | 5.47 GB
Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 5 advanced practical projects
Google has recently released TensorFlow 2.0 which is Google’s most powerful open source platform to build and deploy AI models in practice. Tensorflow 2.0 release is a huge win for AI developers and enthusiast since it enabled the development of super advanced AI techniques in a much easier and faster way.
The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Advanced Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab. This course will cover advanced, state-of-the–art AI models implementation in TensorFlow 2.0 such as DeepDream, AutoEncoders, Generative Adversarial Networks (GANs), Transfer Learning using TensorFlow Hub, Long Short Term Memory (LSTM) Recurrent Neural Networks and many more. The applications of these advanced AI models are endless including new realistic human photographs generation, text translation, image de-noising, image compression, text-to-image translation, image segmentation, and image captioning.
The global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020. The technology is progressing at a massive scale and being adopted in almost every sector. The course provides students with practical hands-on experience in training Advanced Artificial Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to:
Develop, train, and test State-of-the art DeepDream algorithm to create AI-based art masterpieces!
Implement revolutionary Generative Adversarial Networks known as GANs to generate brand new images.
Develop Long Short Term Memory (LSTM) networks to generate new Shakespeare-style text!
Deploy AI models in practice using TensorFlow 2.0 Serving.
Apply Auto-Encoders to perform image compression and de-noising.
Apply transfer learning to transfer knowledge from pre-trained networks to classify new images using TensorFlow 2.0 Hub.
The course is targeted towards students wanting to gain a fundamental understanding of how to build, train, test and deploy advanced models in Tensorflow 2.0. Basic knowledge of programming and Artificial Neural Networks is recommended. Students who enroll in this course will master Advanced AI and Deep Learning techniques and can directly apply these skills to solve real world challenging problems.
What you’ll learn
- Build, train, test and deploy Advanced Artificial Neural Networks (ANNs) models using Google’s newly released TensorFlow 2.0.
- Understand the underlying theory and mathematics behind Generative Adversarial Neural Networks (GANs).
- Apply revolutionary GANs to generate brand new images using Keras API in TF 2.0.
- Understand the underlying theory and mathematics behind Auto encoders and Variational Auto Encoders (VAEs).
- Train and test Auto-Encoders to perform image compression and de-noising using Keras API in TF 2.0.
- Understand the underlying theory and mathematics behind DeepDream algorithm. Develop, train, and test State-of-the art DeepDream algorithm to create AI-based art masterpieces using Keras API in TF 2.0!
- Understand the intuition behind Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs).
- Train Long Short Term Memory (LSTM) networks to generate new Shakespeare-style text using Keras API in TF 2.0!
- Apply transfer learning to transfer knowledge from pre-trained MobileNet and ResNet networks to classify new images using TensorFlow 2.0 Hub.
- Develop ANNs models and train them in Google’s Colab while leveraging the power of GPUs and TPUs.
- Deploy AI models in practice using TensorFlow 2.0 Serving.
Table of Contents
INTRODUCTION AND COURSE OUTLINE
1 Course Introduction and Welcome Message
2 Course Overview
3 BONUS Learning Path
4 ML, AI and DL
5 Machine Learning Big Picture
6 TF 2.0 and Google Colab Overview
7 Whats New in TensorFlow 2.0
8 What is Google Colab
9 Google Colab Demo
10 Eager Execution
11 Keras API
12 Get the materials
REVIEW OF ARTIFICIAL NEURAL NETWORKS AND CONVOLUTIONAL NEURAL NETWORKS
13 ANN and CNN – Part 1
14 ANN and CNN – Part 2
15 ANN and CNN – Part 3
16 ANN and CNN – Part 4
17 ANN and CNN – Part 5
18 ANN and CNN – Part 6
19 ANN and CNN – Part 7
20 ANN and CNN – Part 8
21 Project 1 – Solution Part 1
22 Project 1 – Solution Part 2
TRANSFER LEARNING (TF HUB)
23 What is Transfer learning
24 Transfer Learning Process
25 Transfer Learning Strategies
26 ImageNet
27 Transfer Learning Project 1 – Coding P1
28 Transfer Learning Project 1 – Coding P2
29 Transfer Learning Project 1 – Coding P3
30 Transfer Learning Project 1 – Coding P4
31 Transfer Learning Project 1 – Coding P5
32 Transfer Learning Project 2 – Coding P1
33 Transfer Learning Project 2 – Coding P2
34 Transfer Learning Project 2 – Coding P3
AUTOENCODERS
35 Autoencoders intuition
36 Autencoders Math
37 Linear Autoencoders vs. PCA
38 Autoencoders Applications
39 Variational Autoencoders (VARS)
40 Autoencoders CNN Dimensionality Review
41 Autoencoders Project 1 – Coding P1
42 Autoencoders Project 1 – Coding P2
43 Autoencoders Project 1 – Coding P3
44 Autoencoders Project 1 – Coding P4
45 Autoencoders Project 1 – Coding P5
46 Autoencoders Project 2 – Coding P1
47 Autoencoders Project 2 – Coding P2
DEEP DREAM
48 What is Deep Dream
49 How does DeepDream Algo work
50 Deep Dream Simpified
51 Deep Dream Coding P1
52 Deep Dream Coding P2
53 Deep Dream Coding P3
54 Deep Dream Coding P4
55 Deep Dream Coding P5
GANs
56 GANS intuition
57 Discriminator and Generator Networks
58 Let’s put the Discriminator and Generator together
59 GAN Lab
60 GANs applications
61 GANS Project 1 P1
62 GANS Project 1 P2
63 GANS Project 1 P3
64 GANS Project 1 P4
65 GANS Project 1 P5
RECURRENT NEURAL NETWORKS (RNNs) AND LSTMs
66 Recurrent Neural Networks Intuition
67 RNN Architecture
68 What makes RNN so special
69 RNN Math
70 Fun with RNN
71 Vanishing Gradient Problem
72 Long Short Term Memory LSTM
73 RNN Project #1 – Part #1
74 RNN Project #1 – Part #2
75 RNN Project #1 – Part #3
76 RNN Project #1 – Part #4
TENSORFLOW SERVING AND TENSORBOARD
77 TF Serving Coding Part 1
78 TF Serving Coding Part 2
79 TF Serving Coding Part 3
80 Tensorboard Example 1
81 Tensorboard Example 2
82 Distributed Strategy
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