TensorFlow 2.0 Practical Advanced

TensorFlow 2.0 Practical Advanced, available at $49.99, has an average rating of 4.4, with 88 lectures, based on 345 reviews, and has 5317 subscribers.

You will learn about Build, train, test and deploy Advanced Artificial Neural Networks (ANNs) models using Google鈥檚 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鈥檚 Colab while leveraging the power of GPUs and TPUs. Deploy AI models in practice using TensorFlow 2.0 Serving. This course is ideal for individuals who are Data Scientists who want to apply their knowledge on Real World Case Studies or AI Developers or AI Researchers It is particularly useful for Data Scientists who want to apply their knowledge on Real World Case Studies or AI Developers or AI Researchers.

Enroll now: TensorFlow 2.0 Practical Advanced

Summary

Title: TensorFlow 2.0 Practical Advanced

Price: $49.99

Average Rating: 4.4

Number of Lectures: 88

Number of Published Lectures: 83

Number of Curriculum Items: 88

Number of Published Curriculum Objects: 83

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Build, train, test and deploy Advanced Artificial Neural Networks (ANNs) models using Google鈥檚 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鈥檚 Colab while leveraging the power of GPUs and TPUs.

Deploy AI models in practice using TensorFlow 2.0 Serving.

Who Should Attend

Data Scientists who want to apply their knowledge on Real World Case Studies

AI Developers

AI Researchers

Target Audiences

Data Scientists who want to apply their knowledge on Real World Case Studies

AI Developers

AI Researchers

Google has recently released TensorFlow 2.0 which is Google鈥檚 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鈥揳rt 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:

1. Develop, train, and test State-of-the art DeepDream algorithm to create AI-based art masterpieces!

2. Implement revolutionary Generative Adversarial Networks known as GANs to generate brand new images.

3. Develop Long Short Term Memory (LSTM) networks to generate new Shakespeare-style text!

4. Deploy AI models in practice using TensorFlow 2.0 Serving.

5. Apply Auto-Encoders to perform image compression and de-noising.

6. 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.

Course Curriculum

Chapter 1: INTRODUCTION AND COURSE OUTLINE

Lecture 1: Course Introduction and Welcome Message

Lecture 2: Course Overview

Lecture 3: EXTRA: Learning Path

Lecture 4: ML, AI and DL

Lecture 5: Machine Learning Big Picture

Lecture 6: TF 2.0 and Google Colab Overview

Lecture 7: Whats New in TensorFlow 2.0

Lecture 8: What is Google Colab

Lecture 9: Google Colab Demo

Lecture 10: Eager Execution

Lecture 11: Keras API

Lecture 12: Get the materials

Chapter 2: REVIEW OF ARTIFICIAL NEURAL NETWORKS AND CONVOLUTIONAL NEURAL NETWORKS

Lecture 1: ANN and CNN – Part 1

Lecture 2: ANN and CNN – Part 2

Lecture 3: ANN and CNN – Part 3

Lecture 4: ANN and CNN – Part 4

Lecture 5: ANN and CNN – Part 5

Lecture 6: ANN and CNN – Part 6

Lecture 7: ANN and CNN – Part 7

Lecture 8: ANN and CNN – Part 8

Lecture 9: Project 1 – Solution Part 1

Lecture 10: Project 1 – Solution Part 2

Chapter 3: TRANSFER LEARNING (TF HUB)

Lecture 1: What is Transfer learning?

Lecture 2: Transfer Learning Process

Lecture 3: Transfer Learning Strategies

Lecture 4: ImageNet

Lecture 5: Transfer Learning Project 1 – Coding P1

Lecture 6: Transfer Learning Project 1 – Coding P2

Lecture 7: Transfer Learning Project 1 – Coding P3

Lecture 8: Transfer Learning Project 1 – Coding P4

Lecture 9: Transfer Learning Project 1 – Coding P5

Lecture 10: Transfer Learning Project 2 – Coding P1

Lecture 11: Transfer Learning Project 2 – Coding P2

Lecture 12: Transfer Learning Project 2 – Coding P3

Chapter 4: AUTOENCODERS

Lecture 1: Autoencoders intuition

Lecture 2: Autencoders Math

Lecture 3: Linear Autoencoders vs. PCA

Lecture 4: Autoencoders Applications

Lecture 5: Variational Autoencoders (VARS)

Lecture 6: Autoencoders CNN Dimensionality Review

Lecture 7: Autoencoders Project 1 – Coding P1

Lecture 8: Autoencoders Project 1 – Coding P2

Lecture 9: Autoencoders Project 1 – Coding P3

Lecture 10: Autoencoders Project 1 – Coding P4

Lecture 11: Autoencoders Project 1 – Coding P5

Lecture 12: Autoencoders Project 2 – Coding P1

Lecture 13: Autoencoders Project 2 – Coding P2

Chapter 5: DEEP DREAM

Lecture 1: What is Deep Dream

Lecture 2: How does DeepDream Algo work

Lecture 3: Deep Dream Simpified

Lecture 4: Deep Dream Coding P1

Lecture 5: Deep Dream Coding P2

Lecture 6: Deep Dream Coding P3

Lecture 7: Deep Dream Coding P4

Lecture 8: Deep Dream Coding P5

Chapter 6: GANs

Lecture 1: GANS intuition

Lecture 2: Discriminator and Generator Networks

Lecture 3: Lets put the Discriminator and Generator together

Lecture 4: GAN Lab

Lecture 5: GANs applications

Lecture 6: GANS Project 1 P1

Lecture 7: GANS Project 1 P2

Lecture 8: GANS Project 1 P3

Lecture 9: GANS Project 1 P4

Lecture 10: GANS Project 1 P5

Chapter 7: RECURRENT NEURAL NETWORKS (RNNs) AND LSTMs

Lecture 1: Recurrent Neural Networks Intuition

Lecture 2: RNN Architecture

Lecture 3: What makes RNN so special

Lecture 4: RNN Math

Lecture 5: Fun with RNN

Lecture 6: Vanishing Gradient Problem

Lecture 7: Long Short Term Memory LSTM

Lecture 8: RNN Project #1 – Part #1

Lecture 9: RNN Project #1 – Part #2

Lecture 10: RNN Project #1 – Part #3

Lecture 11: RNN Project #1 – Part #4

Chapter 8: TENSORFLOW SERVING AND TENSORBOARD

Lecture 1: TF Serving Coding Part 1

Lecture 2: TF Serving Coding Part 2

Lecture 3: TF Serving Coding Part 3

Lecture 4: Tensorboard Example 1

Lecture 5: Tensorboard Example 2

Lecture 6: Distributed Strategy

Chapter 9: Congratulations!! Dont forget your Prize 馃檪

Lecture 1: Bonus: How To UNLOCK Top Salaries (Live Training)

Instructors

Dr. Ryan Ahmed, Ph.D., MBA
Best-Selling Professor, 400K+ students, 250K+ YT Subs

SuperDataScience Team
Helping Data Scientists Succeed

Mitchell Bouchard
B.S, Host @RedCapeLearning 540,000 + Students

Ligency Team
Helping Data Scientists Succeed

Rating Distribution

1 stars: 13 votes

2 stars: 20 votes

3 stars: 47 votes

4 stars: 121 votes

5 stars: 144 votes

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