Hi Ruiz. It’s only in PDF right now. Because the book contains mostly visuals, it might be tricky to have an EPUB format that can give a good reading experience. But TBH I haven’t explored it in great detail, will definitely take a closer look.

Thanks 👍

Hi Meor, welcome 👋

Hi Alexey, thanks for having me here.

Hi Tino. It’s my pleasure! Unfortunately, the book doesn’t cover Explainable AI. Where this book can contribute is by helping readers build a clear framework of understanding before diving deeper into further deep learning topics like Explainable AI.

Got it! Thanks 🙂

Hi Gur, thanks for checking out my posts and for your kind words! Indeed, there are already a number of amazing illustrated DL books from top authors. Where I try to add value is making a book that helps the reader navigate the various concepts with the least friction. For example, the same dataset is used in all chapters so you have the same, consistent reference. And the math is kept to an absolute minimum because the main goal is to help the reader build a strong intuition first.

Great! Thanks, Meor Amer.
I’m looking forward to seeing more of your work :)

Hi Dustin. Yes, that’s what got me interested in this field. It’s been quite some time and the progress was much slower then. But now it’s really exciting with the emergence of companies like Elon Musk’s Neuralink. The use case possibilities are huge, stroke rehabilitation and amputee assistance being a couple of examples. Though we are still some way from practical and affordable solutions, it has definitely being given a boost in recent years.

Hi ouskä
Who is your target audience for this book?
People just beginning their journey into deep learning before going further into the technicals. Also leaders looking to understand deep learning from first principles.
Are there any prerequisites needed to get the most out of the book?
No pre-requisites. This book is beginner-friendly.
What the Book Doesn’t Cover?
Mathematical derivations, code examples, and further topics such as optimizers, regularization, embeddings, etc.
What are the most challenging lessons that you have learned while working on this book?
I’m big on ensuring a piece of material has an element of continuity and storyline. So the challenge was making sure all the technical concepts are covered while finding a way to make the reading enjoyable.
What advice would you have for beginners in machine learning / deep learning?
Start with hands-on projects, then reading the theory will make much more sense. The initial steps could be reusing existing code e.g. Kaggle notebooks, then modify and build upon what’s there.

Hi Benedict. My inspiration is the amazing designer Jack Butcher (Visualize Value). Ironically, what he’d taught me is you don’t have to be good at designing (that’s me!) to be able to come up with impactful visuals. It’s more about sharing your perspectives than worrying about the aesthetics. It is still challenging, but through practice, it gets easier. As for the tool, I use Figma - highly recommended!

Hi Jing

1. I can totally relate. That’s why sometime soon I’ll release the accompanying code used to run the examples in the book. And yes, further down the line I do have ideas for creating additional hands-on exercises.
   
2. Yes, that’s my intention with the book - to help readers build an intuition first (by way of visuals) using the minimum possible math and equations.
   
3. The next one I’m planning is A Visual Introduction to Machine Learning.
   
4. Absolutely, I’m always open to potential collaborations to help make deep learning accessible for more!
   
5. That’s an interesting prospect. Would love to explore this!

Hi Meor Amer, thanks for your response, looking forward to collaborate with you on the Chinese edition of the books🤝

Hi Alexey. Here are some that have worked for me:

• As for any generic ML visuals, it helps to observe for phenomenons/things outside of ML/Tech. Because a lot of things around us happen via similar universal principles, we can take one concept from one area and apply it to another.

• As for the book, first I built the storyline of how I want to book’s content to flow. From there, I listed down the key concepts of each page. And then I figured out the appropriate visuals to depict the idea.

I’m using Figma and I really love it!

Hi Hafiz. Unfortunately that topic is not covered in the book.

👍

Hi Tim. Yes, the book covers these concepts. For designing, it covers the tweaks required in the neural network to address four types of tasks: linear regression, non-linear regression, binary classification, and multi-class classification. For tuning, it covers the common hyperparameters that can be used to improve performance.

Hi Ali. You are most welcome.
1-It took me around 3 months of almost full-time work. The most time-intensive is the continuous structuring of what to cover vs. not to cover to ensure that the whole flow makes sense.
2-Less labels and multi-modal i.e. getting closer to how humans learn.
3-The book is written with the beginner in mind. So the good news is you don’t need any background in ML to read it!
4-Yes, I do. The next I have planned is A Visual Introduction to Machine Learning. It will be more challenging to write though because it’s broad whereas deep learning is specific.
5-Initially by working on domains I am familiar/interested in e.g. telecommunications, biomedical engineering. Combined that with theory learning like courses, books, etc.

Hi Roy. This book is beginner-friendly and so it’s fine for those without prior exposure to DL. If you are already comfortable with it, then probably you won’t gain as much new information. But perhaps the visuals can still help give different perspectives to what you already know.

Hi Meghana, thanks for going through the sample. Yes, that’s how I got started. It’s been some time and I’d say the progress hasn’t been as fast as people would hope it to be. Viable consumer applications are still not there yet. Having said that, I’m excited that companies like Neuralink and Kernel are bringing these ideas more into the mainstream, which help to bring more talents into this field and accelerate the progress.

https://gumroad.com/a/63231091