Becoming a deep learning researcher.
It is a journey that starts with the purchase of a GPU and the installation of Deep Learning library such as PyTorch or Keras. But this is only the beginning. 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 Data Scientists. And then they start interviewing. And here is usually the end of the story, repeatedly being rejected by interviewers.
But there can be so much more.
There is so much to be learned to become a real researcher and succeed in a deep learning job interview, but little of the important stuff is written down in a condensed manner. A typical data science blog is the perfect summation of this lacking strategy today. Few hours of scrolling but no real depth. Everything looks the same.
This volume on the other hand, aims to be comprehensive, and to that end comprises practically all the terms of modern Data Science and Deep Learning. This makes a remarkably hefty number of topics. No singular work has yet been published in which these subjects are collectively compiled; nor could a knowledge of such topics be obtained but by the consultation of many and expensive writings.
It has consequently been the objective of the author to compress all the interesting information to within a narrow compass, and at the same time to render this information as thoroughly and technically as possible to reflect the true nature of a job interview. To this end, the focus of the book lies primarily on developing an understanding of the principles and concepts underlying practical data science.
Clearness and depth, comprehensiveness and precision, guided me while writing this volume. This book has been designed primarily for the use of prospective job seeker in the fields of machine and deep learning whose abilities reach or approach some- thing like what is usually described as STEM track. I hope that it may be useful to other classes of readers, but it is this class whose wants I have considered first. It is in any case a book for engineers, mathematicians computer science graduate and the like: I have nowhere made any attempt to meet the needs of students of other disciplines or indeed any class of students whose interests are not primarily mathematical.
I regard the book as being complete and I have done my best to avoid the inclusion of anything that involves really unnecessary ideas.
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