Questions and Answers
Hello Alexia Audevart!
I am happy to see the advanced book on TensorFlow 2, thank you! Many use-cases and examples over the internet come on <TensorFlow 2 and require some time and efforts to adapt.
What common real-world problems are demonstrated and resolved as practical use-cases in the book?
Thank you for your feedback. We covered many use cases with images and sequence data (image classification, applying stylenet, retraining existing models, text generation, sentiment analysis, stock price prediction, and so on). A special chapter is dedicated to ML in Production.
Hi Alexia Audevart ! This looks like a great book! I see more and more job ads that explicitly require experience in ML frameworks like TensorFlow these days over more general software engineering requirements like “experience in numeric programming using python”. I always wonder about why is this the case. Alexia Audevart, as a person who just wrote the book on the topic, what do you think are the peculiarities about TensorFlow that do not translate well from other programming/data wrangling paradigms? What concepts one should try to learn in a structured way, from a book like yours, because they are “hard to get right” otherwise?
Hi Saulius Lukauskas,
Software Engineering is a different paradigm of Machine Learning paradigm. So it’s a new skill.
TensorFlow is one of the more popular Deep Learning framework. But Deep Learning is a subset of Machine Learning. ML and DL are part of the artificial intelligence family.
Hi Alexia Audevart, thanks for the answer! Maybe I didn’t phrase my question properly, I feel like my use of the term Software Engineering was a red herring. Maybe I could paraphrase it a bit differently: for a person who is familiar with say, python and general machine learning concepts (on pen and paper for instance), but not TensorFlow framework per se - what are the specifics (or maybe “gotchas”) of TensorFlow that one should be aware of and pay extra attention when studying it?
TensorFlow is a Deep Learning API so you’ll need to start with dense neural networks and understand deep learning concepts such as activation functions, dense layers, epochs, gradient descent, etc. Then, you can focus on CNN for images classification and RNN for sequence data. I think this will be a good starting point ;-)
Hi Alexia Audevart! The content of your book and the corresponding github account look interesting.
What are the prerequisites to learn from your book (apart from knowing basic python)?
Hi Saskia Kutz,
If you know some basics of Python. You can read the book. May be, you can look at some Machine learning concepts… Enjoy your reading ;-)
Thank you. So your book is about implementing these machine learning concepts we all learned about elsewhere?
This book assumes a basic familiarity with Python and Machine Learning concepts. We explained all the Deep Learning concept step by step in all chapters.
In the contents, I couldn’t find a chapter on debugging neural networks. Is it because there are no common practices yet, or because it is just outside of the scope of the book. Can you recommend any reading on that?
Hi Vladimir Finkelshtein,
Indeed, we didn’t write a specific chapter on that topic. But you can find some tips in each chapter. TF v2 is by default in eager execution mode, so it’s now easier to debug than 2 years ago
For most classical ML models, one can’t use data with missing values. In some use case, imputing missing values is a bad practice, and may bias the model too much.
I keep hearing that it is allowed in neural networks to leave missing values (I guess you just don’t activate some neurons if you have nans). However, I have not seen any code that does this. Do you cover such examples? Or is it as simple as adding categorical columns that some values is missing/present?
No, we did not address this topic because we used ready-made datasets. May be you can find some useful inputs in this research paper : https://hal.inria.fr/hal-03044144/file/how_to_deal_with_missing_data_in_supervised_deep_learning_.pdf
Hm, I like the comparison they did, but I didn’t like that they do it on MNIST. When one has random missing pixels in MNIST, a good imputation will be taking average color of observed neighboring pixels. So it’s not surprising that they learned something that beats zero and mean imputations.
Thanks for the paper, it also has references to surveys, they seem to be worth checking..
I know you are biased towards tensorflow. If someone was just starting with deep learning, would you recommend them tensorflow or pytorch? What will be more common in 3 years from now? Google trends doesn’t seem to favor tf.
I’m just starting to use PyTorch so I don’t have enough experience to give you a complete comparison. PyTorch is more pythonic than TF. In 3 years a lot of things could happen… I totally agree with this article: https://www.tooploox.com/blog/pytorch-vs-tensorflow-a-detailed-comparison
Hi Alexia Audevart ,
Wonderful content Indeed , Thank you for writing this . 🙏
I was wondering can I recommend this book to people who are preparing for Tensorflow Developer Certificate .
Your book from the content seem to have all concepts covered , But your experiences on readers who completed this exam after reading the book .
Hi Riddhi Dasani,
Thank you for your message. This book coupled with Laurence Moroney’s MOOC on Coursera is a good way to get the TF certification. 💪
Awesome , Good to know . Thanks Alexia Audevart
Can you tell us about projects you have in the book? How did you select the datasets for these projects?
And what’s your favourite chapter? 🙂
Hi Alexey Grigorev,
My favourite recipe is without hesitation Deep Dream in the CNN chapter. It’s magic to create a psychedelic image by using abstract art.
We want readers that they learn by doing so in each recipe, we explore a dataset
(We’ve used many datasets which are included into Keras, public open datasets, Kaggle datasets, CSV files, and so on…)
Hey, Alexia Audevart! Thanks for doing this and congratulations on the book. What projects did you want to include but couldn’t?
Will you write another book?
Are there any books you recommend we read before yours?
Hi Matthew Emerick,
I think this book covers different aspects of what we have to know in deep learning. However, each day, researchers make new discovers on that topics… so we can create every day new recipes.
I think ML and DL in production would be the next challenge.
If you speak French, you can read my first book about AI and NeuroSciences.
Hello Alexia Audevart! Great book, I am wondering, what this book can bring more for experienced Data Scientist ?
Hi Lamjed Debbich,
In Data Science, you need to have IT skills, Maths & Stats skills and Business skills.
This book introduces you to a specific part of Machine Learning (the ability for a machine to learn something without being specifically programed to) called Deep Learning. I think that this skill will be more and more essential for a data scientist. I Hope I have answered your question correctly 😊
Thankyou Alexia Audevart
It is super compelling that in addition to the TensorFlow ecosystem, one will learn from the book how to take TensorFlow into production!
I am wondering whether you shared some recipes or hints for real-time model performance monitoring peculiar to TF ecosystem?
We have addressed some tips and guidelines ta take TF models into production but not the one you mention. Maybe my next book will be about ML in production ;-)
Alexia Audevart looking forward to it!
Hi Alexia, congrats on your book! It must be feel very exciting to write on such an interesting topic. If you had to add one more chapter, what would it be about?
Very good question! I think we covered all the fundamental topic… May be we can go deeper into the attention architecture.