Florian florian

Liked it.

A book about the origin story of Ethereum. I read this as a way to challenge my assumption that a) cryptocurrencies are pointless hype and b) that anyone who says 'crypto will revolutionize the world' just uses this as an excuse to speculate in 'get rich.

I understand now that ethereum does have a very interesting core idea: a distributed computer that you can run "code" on, which typically means creating a token that has certain effects when it is bought or sold. Alright, not completely pointless then. I am still not convinced that any of the distributed applications that I've heard of actually need to be implemented that way. Big money speculation seems to be the primary driver, still in 2021. So there is definitely a big hype that still needs to settle down to see what role this technology will play in the future.

In any case, the (audio) book was a pleasant narration of the story and I liked it.

A political thriller set in an alternative post-war Berlin where the Nazis had won the war. This was Robert Harris‘ first novel after he had researched the stories of the forged Hitler Diaries in the 1980s. He clearly succeeded in mixing history and fiction – I for one was quite captivated by the story.

Very well written, which also made it intense to read. Not the right material to lighten my mood during dark pandemic times, but that wasn't the book's job, of course.

This was the book I needed to take me one step further: From just knowing "how to train a neural network" to a better understanding of "MLOps", including training workflows, aspects of scalable serving, and reproducibility.

The three authors are employed at Google and it shows in many chapters: The example of choice is always a Google Cloud AI offering or a Tensorflow code snippet. They do make an effort to also mention competitor products and open source alternatives. Because their insight from Google provided them with this wide range of best practices, I won't hold any of this against the book.

The book isn't without its flaws, though. This (recent) first edition has a number of distracting errors (such as misleading numbers in figures and weird code indentation), plus the greyscale print makes it hard to read many of the figures. That fact cost the book its fifth star. A 2nd edition will probably catch up once it irons out these issues.

I for one will keep this book on my shelf for future reference. It's a great collection of best practices to move a team and an organization ahead in terms of "AI readiness".

Interesting retelling of the stories surrounding the female US code breakers of WW2. I learned some things about the war in the Pacific that I didn't know before. It was mostly straight-forward though: The stories of how (mostly) women were recruited to break ciphers for the army and the navy, some explanation on basic code breaking principles, embedded in the chronological development of the war and its effect on American society. I didn't like the audio version too much, to be honest. The narration was a little robotic and a bit slow for my taste. In retrospect, I think I would have preferred the paper version.

This was heavy. Read any chapter in this book if ever you forget for a moment the atrocities that took place in Auschwitz.

Highlighting the gender data gap: The absence of information and mechanisms required to make life fair for both genders.

Caroline Criado Perez shows how "male" is almost always the norm and "female" the exception - from the workplace over the size of smartphones to medical research.

The data she presents draws a sobering picture of inequality across all areas of life. A very important book.

A book about deep learning that really caters to my preferred learning style: It covers a lot of real-world applications (text analysis, sentiment analysis, vision, ...) and provides clear and practical code examples that invite you to try for yourself. Ultimately, trying it out and building something yourself is the way to really grasp the concepts, I think, and this book does a really good job at it.

While Francois Chollet does give some introduction in the beginning, it may be too little for the complete beginner. For anyone starting at a slightly-above beginner to intermediate level, I'd wholeheartedly recommend this book to learn Deep Learning with Python.

The first time I really leaned into Greek mythology. Stephen Fry does a great job at retelling the myths, providing humor and context so that the stories stick. It's still a lot, so I won't remember all of it. This may also be because I listened to the audiobooks so I couldn't earmark or highlight certain chapters or characters. Still, Stephen Fry reads it himself, so I would definitely recommend the audio version. He's definitely my favorite person to read any audiobook in the English language.

Wow, what a story.

I was only vaguely aware of the publishing scandal surrounding the "Hitler diaries" in 1983. The Hamburg-based magazine Stern had spent millions to fetch them through one of their journalists who in turn received them from a dealer that managed to acquire them from East Germany. Except: None of this was true and honest. The Stern management had committed to the deal behind their editors' back, the journalist kept half the money for himself (and spent lavishly on Hitler memorabilia) and the dealer in fact did not acquire the "diaries", but obediently forged them one by one as more money kept pouring in.

I couldn't stop reading. This story has everything and raises some interesting questions aside from the story itself: To what lengths do we go to deceive ourselves if we desire for something to be true? How easy do we calm our inquisitory and skeptic nature if an authority we trust has provided us with enough plausible explanations, even if they themselves have been deceived due to a series of mistakes and oversights?

Robert Harris wrote this book in 1986, briefly after the whole story had collapsed. I do not know what additional information has come to light in the 30+ years since, so this book may in fact be a little out of date. Also, I would have wished for a little transparency on how Harris was able to reconstruct the series of events, down to individual conversations. If the book has shown me one thing, it is to be skeptical of someone's narration of events, as long as the source isn't validated completely. What remains is a little uncertainty as to which assumptions Harris had to make to be able to tell it in such a cohesive and compelling manner. In any case, a book well worth reading if the story interests you.

The story of astrophysicist Mike Brown whose calling it is to observe the skies, trying to find new objects in our solar system. When he finds one particular object, the space community has come to grips with the fact that Pluto may actually not be a planet - and what exactly is a planet anyways?

Extremely entertaining, and sometimes shocking to learn about the politics of "who saw it first". I liked it a lot.

The subtitle of the book is "Memories of a Nation" and that describes it pretty well: From a non-German's perspective, MacGregor, who is the director of the British museum, describes German history in the format of many little episodes, each centered around one object, person, place or theme. He highlights how these things are good examples that explain the formation of a collective identity. They are tied together under one overarching thesis: This German identity has been defined by four great traumas:

1. 1618-1648: The Thirty Year's War
2. 1806-ish: Napoleon's victory over Prussia; most distinctly Napoleon's entering and occupation of Berlin
3. 1933-1945: The "Third Reich" and the Holocaust
4. 1949-1990: The division of Germany in East and West.

This is a history book. But it isn't a book about all of German history. MacGregor cherry-picks to tell the stories that best illustrate his main thesis. I think this is actually the strength of the book: Told as multiple stories, it doesn't feel like a dry list of events. Instead, it's compelling, interesting and even entertaining to read.

I've highlighted much for further reading and can recommend this book to anyone who is interested in German history and who wants to understand how German identity has been and is being formed.

A book about data analysis with Python using the popular Pandas library (de-facto standard for data wrangling), written by the creator of Pandas himself. Or as I like to call it: The Pandas Book.

First of, don't get me wrong: The 3-star rating doesn't mean this is not a good book. It just wasn't written in a style that I would have personally preferred.

Pros:

• Very extensive coverage of (almost) the complete Pandas API. I feel like I have seen (and tried) all major Pandas features now.
• Many code examples to see features in action.
• Excellent last chapter where the author goes through real-world data sets and shows how to explore and analyse data using Pandas features.

Cons:

• Large majority of examples using dummy data (foo and bar and random numbers). While this shows the technical interface, it didn't help me grasp the application potential in many cases.
• The structure made the book feel like official API documentation extended with a bit of prose. To be fair, the author made that clear in the preface, but the book had promised me a "hands-on guide (...) packed with practical case studies", and I only found that to be true in the last chapter.

What helped me was having a group of friends to discuss the book. We read one chapter a week and shared our notebooks of playing around with Pandas and our own data sets. While I personally prefer a slightly different style of coding books, studying this one has helped tremendously in becoming more familiar and confident in using Pandas for my data science projects.