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TensorFlow 2.0 Practical Advanced

Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 5 advanced practical projects
4.5
4.5/5
(312 reviews)
5,062 students
Created by

9.6

CourseMarks Score®

9.6

Freshness

8.8

Feedback

9.8

Content

Platform: Udemy
Video: 12h 36m
Language: English
Next start: On Demand

Top TensorFlow courses:

Detailed Analysis

CourseMarks Score®

9.6 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

9.6 / 10
This course was last updated on 2/2021 .

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

8.8 / 10
We analyzed factors such as the rating (4.5/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.8 / 10
Video Score: 9.5 / 10
The course includes 12h 36m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 6 hours 00 minutes of 44 TensorFlow courses on Udemy.
Detail Score: 9.9 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.9 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

2 articles.
4 resources.
0 exercise.
0 test.

Table of contents

Description

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.

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.

Requirements

• PC with internet connection
• Recommended – The Ultimate Tensorflow 2.0 Practical Course

This course is for

• Data Scientists who want to apply their knowledge on Real World Case Studies
• AI Developers
• AI Researchers

How much does the TensorFlow 2.0 Practical Advanced course cost? Is it worth it?

The course costs $17.99. And currently there is a 82% discount on the original price of the course, which was $99.99. So you save $82 if you enroll the course now.
The average price is $12.5 of 44 TensorFlow courses on Udemy.

Does the TensorFlow 2.0 Practical Advanced course have a money back guarantee or refund policy?

YES, TensorFlow 2.0 Practical Advanced has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the TensorFlow 2.0 Practical Advanced course, but there is a $82 discount from the original price ($99.99). So the current price is just $17.99.

Who is the instructor? Is Dr. Ryan Ahmed, Ph.D., MBA a SCAM or a TRUSTED instructor?

Dr. Ryan Ahmed, Ph.D., MBA has created 34 courses that got 23,772 reviews which are generally positive. Dr. Ryan Ahmed, Ph.D., MBA has taught 267,394 students and received a 4.6 average review out of 23,772 reviews. Depending on the information available, Dr. Ryan Ahmed, Ph.D., MBA is a TRUSTED instructor.
Professor & Best-selling Instructor, 250K+ students
Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan’s mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. 

Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Science, Technology, Engineering and Mathematics to over 200,000+ students globally. He has over 15 published journal and conference research papers on state estimation, AI, Machine learning, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. 

Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA.

* McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world.





9.6

CourseMarks Score®

9.6

Freshness

8.8

Feedback

9.8

Content

Platform: Udemy
Video: 12h 36m
Language: English
Next start: On Demand

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