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- 85% TensorFlow 2.0 Practical Advanced

TensorFlow 2.0 Practical Advanced

$94.99 $13.99Track price

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8.3/10 (Our Score)
Product is rated as #186 in category Data Science

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:

Instructor Details

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 50,000+ 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.

Specification: TensorFlow 2.0 Practical Advanced

Duration

12.5 hours

Year

2020

Level

All

Certificate

Yes

Quizzes

No

12 reviews for TensorFlow 2.0 Practical Advanced

3.3 out of 5
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  1. Avatar

    Tito Mitra

    Seems the trainer has no knowledge of RNN at all. ZERO legitimate explanation of RNN. Almost all the material are copied and just recites the codes.

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  2. Avatar

    YSPark

    All through the course, instructor just shows the pre written code, without showing whole process of writing code . Except this, Overall It’s Good course for learn Tensorflow Deep Learning .

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  3. Avatar

    Angus Lou

    Very practical, real life, and tactical techniques for TF 2.0. Strongly recommend to those who want to take TF 2.0 into their real cases.

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  4. Avatar

    Johan Genis

    Support is not up to standard.

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  5. Avatar

    Samuel Cuscovitch

    Yes. Some concepts were a bit of a stretch given not having been grounded is certain fundamentals.

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  6. Avatar

    Chris Morris

    I like the review of the linear algebra and derivatives. He explained it very well, and it’s important in understanding machine learning concepts

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  7. Avatar

    Raunaq Badjatia

    The instructor did little to explain the more complicated parts of the course material. He stuck to repeating the low hanging fruit. For example, the way he just rushed through the code of RNNs was disappointing. For things like GANs, I actually found it more useful to use Tensorflow’s documentation (from which he has heavily borrowed) and use some internet searches to get some clarification to learn.

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    Jimmy McInerney

    Clear and easy to understand

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  9. Avatar

    Avnish

    The course just demos the existing tutorials available The course should have taken a real world use case and taken through rather than just using mnist samples

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  10. Avatar

    Aman Singh

    If the course would have explained about the data preprocessing for csvs with names, numerical, categorical data and then pass it to the keras model then it would have been great. Plus there should be a 2 3 examples of the core tensorflow(tf.nn) instead of tf.keras as it was advanced tensorflow 2.0. Over all i like the way instructor explains the steps and code

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  11. Avatar

    John Joachim

    Sound quality and content editing was fairly poor, and two of the Projects even failed for the the Instructor! I am surprised he was so unaware of the concepts of correction and subsequent playback of Recorded Lectures, and I don’t believe I’m being unreasonable to point this out or expect better. I would have chosen different Projects to acquaint one with TensorFlow 2 (nearly everything here is covered more adequately by the TF online documentation). Most frustrating of all was the Instructor’s penchant to keep reviewing preliminary Machine Learning details, while other selective details were not elaborated upon and dismissed as fairly introductory concepts (when they really were not). With each accumulative Project, I became less convinced the Instructor was familiar with the topic and the code. Just my opinion, but that’s how it was presented, overall.

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  12. Avatar

    Durga Sandeep

    I already know many concepts from TensorFlow 2.0 Practical. I am looking for a few more advanced and hands on, instead of theory and repetitive lectures. This needs to be addressed. Yeah, some concepts are pretty advanced and they are well taught.

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    TensorFlow 2.0 Practical Advanced

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