TensorFlow 2.0 Practical Advanced

Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 5 advanced practical projects

4.15 (344 reviews)

Udemy

platform

English

language

Data Science

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.

Why take this course?

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.

Screenshots

TensorFlow 2.0 Practical Advanced - Screenshot_01

TensorFlow 2.0 Practical Advanced - Screenshot_02

TensorFlow 2.0 Practical Advanced - Screenshot_03

TensorFlow 2.0 Practical Advanced - Screenshot_04

Our review

Based on the recent reviews and the global course rating of 4.15, here's a comprehensive review of the "TensorFlow 2.0 Practical Advanced" course: **Pros:** - **Comprehensive Understanding**: Many reviewers found the course helpful in understanding the basics of neural networks and advanced concepts. It has been praised for its thorough explanation of neural network architectures, including GANs, deep dream, and LSTM projects. (Ryszard Kudlicki, Anonymous Reviewer 1, Anonymous Reviewer 3) - **Great Knowledge**: The course is conducted with great knowledge of the subject, which is evidenced by the depth of content covered. - **Practical Application**: It provides practical experience and helps in developing intuition for deep learning applications. (Anonymous Reviewer 3, Anonymous Reviewer 6) - **Excellent Theory Explanation**: The theory behind neural networks is explained very well in the course. (Anonymous Reviewer 10) - **Useful for Certification Preparation**: It serves as a great tool for those preparing for the Google TensorFlow Certification exam, as it complements the TFC course from the same creators. (Anonymous Reviewer 12) **Cons:** - **Basic Content**: Despite being titled "TensorFlow 2.0 Advanced," several reviewers found that a significant portion of the course covers basic information and repeated content, which might be more suitable for beginners rather than advanced learners. (Anonymous Reviewer 11, Anonymous Reviewer 8) - **Repetitive Explanations**: Some reviewers pointed out that the explanations were repetitive and lacked diversity in perspective. The vocabulary used was limited, and there was an excessive amount of reading from scripts without proper explanation or clarification. (Anonymous Reviewer 16, Anonymous Reviewer 17) - **Coding Issues**: There are concerns regarding the speed at which code is explained and executed, with some learners feeling that it was not detailed enough. Additionally, some coding practices may not align with individual preferences. (Anonymous Reviewer 2, Anonymous Reviewer 14) - **Video Editing Flaws**: There were mentions of editing issues within the videos, such as lector distractions and unnecessary prolongation of certain topics. (Anonymous Reviewer 9, Anonymous Reviewer 18) - **Outdated Examples**: Some coding examples are outdated, with a few being copied directly from TensorFlow's official documentation without significant adaptation or customization. (Anonymous Reviewer 7, Anonymous Reviewer 8) - **Insufficient Practical Experience**: While the course covers some advanced topics, it falls short in providing practical experience with deep learning, particularly in not challenging learners to set up code by themselves. (Anonymous Reviewer 4, Anonymous Reviewer 13) **General Observations:** - **Diverse Feedback**: The feedback from users is mixed, with some finding the course ideal for their needs and others feeling it did not meet the expectations set by its title. - **Room for Improvement**: There is a consensus that with fine-tuning, the course could be excellent. Areas for improvement include code explanations, practical challenges, and the provision of proper warnings about deprecated methods. (Anonymous Reviewer 5, Anonymous Reviewer 15) - **Customer Support Concerns**: Some users have reached out to the instructors regarding project descriptions and issues with provided code, only to receive unsatisfactory responses or no response at all. (Anonymous Reviewer 19, Anonymous Reviewer 20) In conclusion, the "TensorFlow 2.0 Practical Advanced" course has its strengths in theoretical explanations and practical applications for those looking to advance their knowledge of neural networks, particularly if they are preparing for certification exams. However, it may not fully meet the expectations of advanced learners due to the repetitive nature of content and the lack of challenging, hands-on projects. It's recommended that learners looking for an "advanced" course should approach this one with the understanding that it may also cover foundational topics and consider it as part of a broader learning path.

Charts

Price

Rating

Enrollment distribution

TensorFlow 2.0 Practical Advanced - Distribution chart