# WHAT’S INSIDE …

*You've earned a degree and have implemented a few models, but still find it hard to get to the final round interview. The audience of this book is **you**, the aspiring scientist or engineer, with a quantitative background and the gauntlet of the interviewing process dead ahead. That process is the **most significant hurdle** between you and a dream job. You have the ability, but you could use some guidance. *

*you*

*most significant hurdle*

*Written by Shlomo Kashani,* *the book focuses on statistical perspectives and goes way beyond mundane questions on gradient descent and the like, by including dedicated chapters on **Information Theory, Bayesian Inference, Logistic Regression and of-course numerous topics on Deep Learning**. The *two editions (for *data scientists** and for **financial engineers**)* *are a work in progress. **This preview, provides examples of the organisation, questions, style, topics and quality. *

*Information Theory, Bayesian Inference, Logistic Regression and of-course numerous topics on Deep Learning*

*data scientists*

*financial engineers*

*This preview, provides examples of the organisation, questions, style, topics and quality.*

# a Journey into deep learning

**A. **Have plans to venture into the field of Deep Learning?

Or put another way, are you looking to pursue a rewarding career in Data Science? If yes, you're in luck — everything from deep learning to artificial intelligence is huge right now. Essentially deep learning professionals are high in demand and they also happen to be among the highest-paid in companies around the world (chiefly the US and the UK).

With this in mind, it's apparent that a successful career in deep learning **can turn out to be a remarkable achievement in one's life. **But of course, at this point, the big question is; **how exactly can you sail through the all-important Deep Learning interview?** Deep learning libraries are so good that little interaction with the researcher is needed to get a properly running machine learning pipeline working.

**But this is where many people end their quest for artificial intelligence knowledge and pronounce that they are**. And then they start attending interviews.

*Data Scientists***And here is usually the end of the story, repeatedly being rejected by interviewers.**

**B. Yes, you are an aspiring data scientist**

And chances are you are **highly motivated, numerate** and prepared to take an active, hands-on role. You probably already have ex**tensive knowledge in the field of applied mathematics, computer science, statistics and maybe economics **(all of which are an incredible advantage in this regard). But at the same time, it's still crucial to hone your skills in deep learning before applying for a job. When applying to machine learning jobs, you're likely to come across a wide array of questions (with varying difficulty) in the course of the interview.

This is precisely why you need all the help you can get! So, what resource can assist you in cracking your upcoming deep learning job interview? Well, first off, it's possible to set eyes on a few basic questions and answers while surfing the web (this is perfectly normal).

**But in most cases, you'll need something that offers a lot more.**

**C. What could this “something“ be … ?**

**Just enter "Deep Learning Job Interviews". **Written based on years of research and personal experience, this book is home to a total of 250 fully solved questions (all of which are relevant to any deep learning interview you're likely to come across). With such an invaluable piece of information, every serious-minded researcher should be able to sail through even the most challenging deep learning interview.

**What does the book offer?**

Essentially, "Deep Learning Job Interviews" comes with several different chapters which are broken down into several sections including Introduction, Problems, and Solutions. You can expect each question or problem in this volume to be clear, practical and entirely relevant to the subject. Speaking of which, the problems in this book are divided into two parts namely conceptual and application — the former is aimed at testing and improving your knowledge of basic underlying concepts while the latter is targeted at practicing or applying what you've learnt (most of which are relevant to Python and PyTorch). The Formulas section also turns out to very helpful — it lets you in on a list of mathematical and statistical formulas you can use to find your way around certain questions.

# two target audiences

**A. Data Scientists & Deep/Machine Learning researchers. **

Are you worried about an upcoming interview? Perhaps you’re getting interviews but no offers? Want to be fully confident when answering the toughest AI interview questions?

If you are looking to pursue a career in the booming field of Artificial Intelligence (AI), this book is designed for you. Using this book a you can sharpen your applied mathematics for artificial intelligence and focus your learning curve on understanding the heart of deep learning algorithms.

This book will help first-time job seekers make the shift from the academia to employment. We recognise the data science job market has changed beyond recognition and finding that first position is more competitive than ever.

Recent graduates who are interested in pursuing a career in this field and need an in-depth knowledge of all aspects of artificial intelligence will find this book extremely valuable.

**B. Financial Engineers /Quants.**

While many financial engineering books are available, the data science aspects behind the implementation of the models used in the field are mostly missing. The book is an invaluable piece that deserves a prominent spot on the shelf of every Quantitative Researcher looking to pursue a booming career in Deep Learning.

It is aimed at exploring many of the question types Quants are likely to encounter when undergoing job interviews in the field of Deep Learning. The available quant job roles are becoming tougher and tougher; thus, requiring all financial data scientists and engineers to be at their best whenever they face an interview panel.

All aspects covered to develop **your readiness for the interview** and the book is suitable for **beginners and experienced** financial engineers alike. Written from scratch, the book includes **exclusive problems** not found anywhere else.

The first four Chapters lay down the mathematical elements that will help you with both deep learning in general and, perhaps more importantly, the proper data science mindset.

# PRICING & AVAILABILITY (FEB 2020)

### I’ve been authoring this book over the past 2 years. Although the price is high, it is a fraction of what it would cost you to hire an expert to get such professional and comprehensive training nonetheless. The book is distributed in **Paperback (amazon), Hardcover (EditionOne), EPUB (Apple) and Kindle **versions. The respective ISBN’s are as follows:

## Deep Learning Interviews For Quants (Financial Engineers).

## Deep Learning Interviews.

ISBN 978-1-9162435-0-7 (7 x 10, Hardcover, “Deep Learning Interviews”)

ISBN 978-1-9162435-1-4 (7 x 10, Paperback, “Deep Learning Interviews”)

ISBN 978-1-9162435-2-1 (Apple iBooks EPUB, “Deep Learning Interviews”)

ISBN 978-1-9162435-3-8 (8 x 10, Hardcover special edition, “Deep Learning Interviews”)

ISBN 978-1-9162435-4-5 (7 x 10, Hardcover, “Deep Learning Interviews for Quants”)

ISBN 978-1-9162435-5-2 (7 x 10, Paperback, “Deep Learning Interviews for Quants”)

ISBN 978-1-9162435-6-9 (Apple iBooks EPUB, “Deep Learning Interviews for Quants”)

ISBN 978-1-9162435-7-6 (8 x 10, Hardcover special edition, “Deep Learning Interviews for Quants”)

## To cite this book, please use this bibtex entry:

```
@Book{Kashani2019,
title = {Deep learning Interviews},
author = {Shlomo Kashani},
publisher = {INTERVIEWS AI LTD},
year = {2019},
edition = {1st},
note = {ISBN 13: 978-1-9162435-4-5 },
url = {https://www.interviews.ai},
}
```

# about shlomo kashani

As the **Head of AI** at the deep-learning start-up DeepOncology AI, it is my pleasure to build Medical AI models that enhance the life of people. **I'm privileged to promulgate my passions by:**

Publishing popular tutorials (Deep Learning, Medical AI), offering an In-classroom Deep Learning course using PyTorch at the Yandex Data Science Academy, leading a flourishing Deep Learning Study Group / Deep Learning Boot-camp out of in Tel-Aviv. See the Curriculum.

My forthcoming book, Deep Learning Job Interview Questions, is self-published. Copies will ship in 2019. In the meantime, a digital “rough cut” is available via Here. If you'd like to stay up-to-date on my content, you're most welcome to follow me on LinkedIn.