
<h1 align="center">Deep Learning Interviews: The AI job market has changed beyond recognition. 
In this new landscape, finding that first position is more important than ever.</h1>
      
<p align="center">
  <a href="#about">About</a> •
  <a href="#credits">Credits</a> •  
  <a href="#author">Author</a> •  
  <a href="#license">License</a>
</p>

<h1 align="center">    
  <img src="https://github.com/BoltzmannEntropy/interviews.ai/blob/main/assets/cover-amazon-print.png" width="100%"></a>  
</h1>

---

This book (www.interviews.ai) was written for you: an aspiring data scientist with a quantitative background, facing down the gauntlet of the interview process in an increasingly competitive field. For most of you, the interview process is the most significant hurdle between you and a dream job. 
Even though you have the ability, the background, and the motivation to excel in your target position, you might need some guidance on how to get your foot in the door.

## About

In AI, an elite group of researches such as the ones at Google DeepMind, are breaking scientific frontiers time and again. 
In quantitative algorithms, for instance, a handful of researchers who are at the top of the field can crack challenges 
that seem otherwise out of reach, developing models that drive future trading.

Those experts rely on years of experience and thorough understanding, and they’re fueled by their love of 
complex problems. Hedge funds do everything they can to attract top number crunchers longing to crack intractable challenges. 
If you are an aspiring data scientist, with a quantitative background and the gauntlet of the interviewing process 
dead ahead, you probably know that process is the most significant hurdle between you and a 
dream job somewhere in a startup or a branch of the big five. You have the ability, but you could use some guidance and preparation

## What can it do for me? 

The book’s contents is a large inventory of numerous topics relevant to DL job interviews and graduate-level exams. That places this work at the forefront of the growing trend in science to teach a core set of practical mathematical and computational skills. It is widely accepted that the training of every computer scientist must include the fundamental theorems of ML, and AI appears in the curriculum of nearly every university. This volume is designed as an excellent reference for graduates of such programs:
-      Hundreds of fully-solved problems
-     Problems from numerous areas of deep learning
-      Clear diagrams and illustrations
-      A comprehensive index
-      Step-by-step solutions to problems
-      Not just the answers given, but the work shown
-      Not just the work shown, but reasoning given where appropriate
 
## Core subject areas
 
Your curiosity will pull you through the book’s problem sets, formulas, and instructions, and as you progress, you’ll deepen your understanding of deep learning. The connections between calculus, logistic regression, entropy, and deep learning theory are intricate: work through the book, and those connections will feel intuitive. VOLUME-I of the book focuses on statistical perspectives and blends background fundamentals with core ideas and practical knowledge. There are dedicated chapters on: 
-      Information Theory
-      Calculus & Algorithmic Differentiation
-      Bayesian Deep Learning & Probabilistic Programming
-      Logistic Regression
-      Ensemble Learning
-      Feature Extraction
-      Deep Learning: Expanded Chapter (100+ pages)
These chapters appear alongside numerous in-depth treatments of topics in Deep Learning with code examples in PyTorch, Python and C++. 
## Citation
```
@Book{Kashani2019, title = {Deep learning Interviews}, 
   author = {Shlomo Kashani}, 
   publisher = {Shlomo Kashani}, 
   year = {2020}, 
   edition = {1st}, 
   note = {ISBN 13: 978-1-9162435-4-5 }, 
   url = {https://www.interviews.ai}, 
}
```

 
## Disclaimers
 
 - "PyTorch" is a trademark of Facebook.

## Licensing

- Copyright © [Shlomo Kashani, author of the book "Deep Learning Interviews"](www.interviews.ai)
Shlomo Kashani, Author of the book _Deep Learning Interviews_ www.interviews.ai: entropy@interviews.ai

ALL RIGHTS RESERVED.
The content contained within this book may not be reproduced, duplicated or transmitted without direct written permission
from the author or the publisher. Under no circumstances will any blame
or legal responsibility be held against the publisher, or author, for any damages,
reparation, or monetary loss due to the information contained within this book.
Either directly or indirectly. This book is copyright protected. This book is only for
personal use. You cannot amend, distribute, sell, use, quote or paraphrase any part, or
the content within this book, without the consent of the author or publisher.
Please note the information contained within this document is for educational and
entertainment purposes only. All effort has been executed to present accurate, up to
date, and reliable, complete information. No warranties of any kind are declared or
implied. Readers acknowledge that the author is not engaging in the rendering of
legal, financial, medical or professional advice. The content within this book has been
derived from various sources. Please consult a licensed professional before attempting
any techniques outlined in this book. By reading this document, the reader agrees
that under no circumstances is the author responsible for any losses, direct or indirect,
which are incurred as a result of the use of information contained within this document,
including, but not limited to errors, omissions, or inaccuracies.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted
in any form or by any means, electronic, mechanical, photocopying, recording,
scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976
United States Copyright Act, without the prior written permission of the Publisher.
Limit of Liability/Disclaimer of Warranty. While the publisher and author have used
their best efforts in preparing this book, they make no representations or warranties
with respect to the accuracy or completeness of the contents of this book and specifically
disclaim any implied warranties of merchantability or fitness for a particular
purpose. No warranty may be created or extended by sales representatives or written
sales materials. The advice and strategies contained herein may not be suitable
for your situation. You should consult with a professional where appropriate. Neither
the publisher nor author shall be liable for any loss of profit or any other commercial
damages, including but not limited to special, incidental, consequential, or other
damages.

Notices. Knowledge and best practice in this field are constantly changing. As new
research and experience broaden our understanding, changes in research methods,
professional practices, or medical treatment may become necessary. Practitioners and
researchers must always rely on their own experience and knowledge in evaluating
and using any information, methods, compounds, or experiments described herein.
In using such information or methods they should be mindful of their own safety and
the safety of others, including parties for whom they have a professional responsibility.
To the fullest extent of the law, neither the Publisher nor the authors, contributors,
or editors, assume any liability for any injury and/or damage to persons or property
as a matter of products liability, negligence or otherwise, or from any use or operation
of any methods, products, instructions, or ideas contained in the material herein.