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Machine Learning Using TensorFlow Cookbook

by Alexia Audevart, Konrad Banachewicz, Luca Massaron

The book of the week from 03 May 2021 to 07 May 2021

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. You will work through recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow.

This cookbook begins by introducing you to the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll then take a deep dive into some real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and for regression to provide a baseline for tabular data problems.

Questions and Answers

Ksenia

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?

Alexia Audevart

Hello Ksenia,
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.

Saulius Lukauskas

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?

Alexia Audevart

Hi Saulius Lukauskas,
Software Engineering is a different paradigm of Machine Learning paradigm. So it’s a new skill.

Alexia Audevart

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.

Saulius Lukauskas

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?

Alexia Audevart

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 ;-)

Saskia Kutz

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)?

Alexia Audevart

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 ;-)

Saskia Kutz

Thank you. So your book is about implementing these machine learning concepts we all learned about elsewhere?

Alexia Audevart

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.

Vladimir Finkelshtein

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?

Alexia Audevart

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

Vladimir Finkelshtein

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?

Alexia Audevart

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

Vladimir Finkelshtein

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..

Vladimir Finkelshtein

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.

Alexia Audevart

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

Riddhi Dasani

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 .

Alexia Audevart

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. 💪

Riddhi Dasani

Awesome , Good to know . Thanks Alexia Audevart

Alexey Grigorev

Can you tell us about projects you have in the book? How did you select the datasets for these projects?

Alexey Grigorev

And what’s your favourite chapter? 🙂

Alexia Audevart

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…)

Matthew Emerick

Hey, Alexia Audevart! Thanks for doing this and congratulations on the book. What projects did you want to include but couldn’t?

Matthew Emerick

Will you write another book?

Matthew Emerick

Are there any books you recommend we read before yours?

Alexia Audevart

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.

Lamjed Debbich

Hello Alexia Audevart! Great book, I am wondering, what this book can bring more for experienced Data Scientist ?

Alexia Audevart

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 😊

Lamjed Debbich

Thankyou Alexia Audevart

Ksenia

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?

Alexia Audevart

Hi Ksenia,
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 ;-)

Ksenia

Alexia Audevart looking forward to it!

Glenn

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?

Alexia Audevart

Hi Glenn,
Very good question! I think we covered all the fundamental topic… May be we can go deeper into the attention architecture.

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