Ivan Vasilev is the author of Advanced Deep Learning with Python, we got the chance to sit down with him and find out more about his experience of writing with Packt.
Q: What is/are your specialist tech area(s)?
Ivan: Deep Learning, Machine Learning, Software Engineering.
Q: How did you become an author for Packt? Tell us about your journey. What was your motivation for writing this book?
Ivan: I was contacted by Packt Acquisition Editor. Writing a book seemed like a great way to systematize my own knowledge on the topic, as well as sharing it with the readers.
Q: What kind of research did you do, and how long did you spend researching before beginning the book?
Ivan: I’ve been studying (and working) on the field of deep learning since 2013, and I wrote my first book in 2018. The second book was finished at the end of 2019.
Q: Did you face any challenges during the writing process? How did you overcome them?
Ivan: The main challenge I faced was the tight schedule. I had to combine the writing process with my regular job, so I had to work during the weekends too 🙂
The other challenge is that the book will be seen and judged by the readers. Because of this, I needed to have complete and in-depth understanding of every topic that I discussed in the book. If I was doing the research for myself, I could afford to skip some details and obtain only general idea about certain topic. However, when I had to transfer my knowledge on the “”white sheet”” I had to make sure that content is coherent, no details are omitted, and there are no errors.
Q: What’s your take on the technologies discussed in the book? Where do you see these technologies heading in the future?
Ivan: Deep learning (and machine learning in general) is one of the most promising computer science fields. I believe that in some years (and after a few more “qualitative” improvements), it has the potential to change our lives profoundly.
Q. Why should readers choose this book over others already on the market? How would you differentiate your book from its competition?
Ivan: The book offers a broad deep learning coverage and a detailed analysis of the most popular concepts and neural network models like convolutional networks, recurrent networks, and the attention mechanism and transformers. At the same time it adds a unique mix of more advanced topics like graph networks, memory networks, meta-learning, and autonomous vehicles. These topics are covered theoretically, but also in the context of computer vision and natural language processing. In addition, the book implements samples in both Keras and PyTorch, offering a comparison between the libraries.
Q. What are the key takeaways you want readers to come away from the book with?
Ivan: I would like the readers to extend their knowledge of fundamental deep learning concepts, and to gain broader view of the most recent advances.
Q. What advice would you give to readers learning tech? Do you have any top tips?
Ivan: I would advice the readers to choose a specific task and try to solve it end-to-end. I found this to be the best way to truly understand certain topic.
Q. Do you have a blog that readers can follow?
Ivan: Unfortunately no, but here is a link to my GitHub repository https://github.com/ivan-vasilev/
Q. How would you describe your author journey with Packt? Would you recommend Packt to aspiring authors?
Ivan: Packt is an agile organization, which gives the authors a lot freedom in choosing the content of their books. I would recommend it to aspiring authors.
Q. What are your favorite tech journals? How do you keep yourself up to date on tech?
Ivan: I would suggest to check out the https://www.reddit.com/r/MachineLearning/ sub-reddit as a way to keep in touch with the latest developments.
Q. What is that one writing tip that you found most crucial and would like to share with aspiring authors?
Ivan: Writing takes a lot more time than anticipated 🙂
You can find Ivan’s book on Amazon by following the link below cover image: