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’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. 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’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.
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’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:
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Develop, train, and test State-of-the art DeepDream algorithm to create AI-based art masterpieces!
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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!
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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.
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: Let's 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!! Don't forget your Prize 🙂
Lecture 1: Bonus: How To UNLOCK Top Salaries (Live Training)
Instructors
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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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