Mini book review: Surely You’re Joking Mr. Feynman

Mini book review: Surely You’re Joking Mr. Feynman


If you don’t know Richard Feynman, you’re in for a treat. He has done a lot of unbelievable shit: He won the Nobel prize in Physics, he worked with Oppenheimer on the Manhattan Project, he lectured alongside Einstein at Princeton and he played a key role in determining the failings that led to the Challenger disaster.

What’s it about

Well, it’s not about Physics.

It’s a selection of anecdotes from Feynman himself, jumping back and forth in time. It doesn’t cover his time in the Challenger investigation but a lot of key events in his life are talked about in detail. The book has a bit of a disjointed format. I wasn’t sure if I’d like the lack of structure at first but I found it refreshing as I made my way through. One chapter might be about the friction between him and his military bosses and the next about his bongo playing adventures in Rio.

The one thing that is consistent is his simple presentation and hilarious tone. Despite understanding and developing incredibly difficult concepts, he had an amazing ability to explain things in a way a child could understand. In fact, he’d point out that not being able to do that means you really don’t understand the thing you’re talking about at all! I remember when I was younger my brother (a big Feynman fan) always asking me to explain what I had learned in school that week. If I couldn’t do it without tripping over, he knew I hadn’t really learned it.

My only real criticism is that some of his chat comes across as dated. His attitude towards women at times is probably a bit controversial at best. He does acknowledge it though, and for the time I think he was probably pretty progressive. Reading it in 2017 though can induce a bit of cringe.

Why should I read it?

If you’re on this blog, you probably develop or are interested in developing software. Anyone who follows the Feynman method is going to improve their dev skills.

He also shows how it’s a great thing to not be afraid of looking like an idiot. Just because everyone else in the room seems to understand something, doesn’t mean they do and also doesn’t mean you should. Likewise, just because everyone else in the room does something a certain way, doesn’t mean it’s the right way to do it.

He didn’t respect authority just for the sake of it and gives numerous examples of how an ego has gotten in the way of many solutions to problems. He talks about his experiences with imposter syndrome and having confidence in your position in life.

Pretty much all of the book (except the bits about women and bongos!) can be applied to software development, especially for the guys and girls writing the code. You’ll also laugh out loud at bits.

My rating


Get it here

Mini Book Review: Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks

The book

Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks. I read this one a good while back but have been meaning to revisit it to see if there’s anything I missed.



Jeff Heaton’s book provides an overview of the major Neural Network models in common use as well as a summary of deep learning. It also gives a simple explanation of training algorithms and where they are appropriate. As a relative beginner to the topic, I felt like I was the exact target audience.

Where it goes right

Although 345 pages can be made to seem long if the topic is dry, this one is definitely an easy read. As opposed to other materials on the subject, the author kept the maths very light. If you’re looking to be able to hold a conversation about what a neural net does and why you might use them then this is the book for you. Although I had been studying neural nets and machine learning before picking it up, I felt like certain concepts were explained very well and hearing them in plain english helped reinforce my understanding.
Another way I personally benefited was Heaton’s discussion of trends. As a hobbyist, it’s great to see why certain ideas or algorithms have gone out of favour by the people who use this stuff day in day out.

Where it goes wrong

It’s all a little too simplistic. I came away from it understanding everything I had been told, but was left wanting. A few more chapters that took the level of detail to the next level would have been great. I think anyone who already has a good fundamental understanding of neural nets (i.e. have implemented a few) and machine learning is not going to get much out of this volume. If there had been more concrete examples I think it would have been a bonus. Although it’s in the title, I also felt that deep learning could have been more in depth, although I’d let that slide given how much it’s evolving.


Solid 4.0/5. Like I said, I’ve been meaning to re-read it which is a good sign. The “ELI5” approach helped certain concepts that I had already come across sink in, but it wasn’t revolutionary.