Bücherregal lädt …
Zufällig in der Bücherei entdeckt und mit viel Freude gelesen: Dieses Buch nimmt einen mit durch die Geschichte des Internets, von den allerersten Rechenmaschinen und die Vernetzung der Großrechner, über erste Bulletin Boards an Ost- und Westküste bis zum Aufstieg und Kommerzialisieriung des Web in den 90ern und dem Verschwinden vieler individueller Strömungen um die Jahrtausendwende. Das alles wird erzählt anhand vieler weiblicher Pionierinnen, von namhaften Personen wie Ada Lovelace und Grace Hoppe bis zu Namen, die vielleicht eher im Hintergrund bekannt sind. Ich fand die Geschichte(n) sehr gut erzählt und gerade die Anfänge des Web haben mich selbst wehmütig zurück blicken lassen. Gelesen habe ich die Deutsche Ausgabe, da sie in der Bücherei auslag. Mittlerweile hatte ich im Buchladen auch das Englische Original in der Hand ("Broadband"), das ich wahrscheinlich eher empfehlen würde: Die Ausgabe sieht deutlich cooler aus und sprachlich ist es naturgemäß in dieser US-zentrischen Geschichte immer etwas holprig, alles ins Deutsche zu übertragen. Aber auch auf Deutsch sehr lesenswert.
A book about all that goes into building ML systems for production, but with a good focus on everything aside from building the actual model. This book was great, as it talks about all of the topics typically ignored or glossed over in most machine learning books: data engineering, data collection, deployment, model failures, tooling, and team structures.
Read as part of our weekly Data Science study group.
A systematic framework for identifying opportunities and planning AI projects in a business context.
Who is this book for? Primarily for business people who want to establish AI in their company, but also for technical people (like me) who want to expand "technical planning" into a complete business project scope.
The author has been managing and supporting AI projects for a while and she writes about a breadth of topics: What should a company ensure before considering AI? When is AI not a good idea? How to identify high-impact AI opportunities? How to measure if a project was a success or failure?
I really enjoyed the book as it provides a framework for me to talk to other people in my job and lay out clear criteria about how to plan and when NOT to consider "AI" for a project.
A minor annoyance: The book introduces arbitrary abbreviations (typical for "business" frameworks in my experience). "PAI" is a "potential AI initiative" and the "HI-AI discovery framework" stands for "high impact AI initiatives". I think the content would work well without these terms and it would have been more readable.
Aside from that, the writing is clear, to the point and well-edited. I was positively surprised by the high quality of the layout and print, even though it appears to be distributed through Amazon's print-on-demand platform.
Not a printed "book", but free online material packaged up like a book. Part 1 is a summary of the ML interview process. Part 2 is a collection of practice exercises.
Being in a Machine Learning role myself, this was an interesting read to see a comprehensive summary of current topics, interview process, and role descriptions. I also got some inspiration for interviewing new candidates for my own team.
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 (
fooandbarand 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.
Read this as part of our "Data Science Study Group" that friends and I have been organising for the past three months. This book lends itself quite well to this kind of format: A broad overview of everything that Data Science entails. However, the book also stays at that high level.
While Steven Skiena goes into detail on some of the algorithms, that level of detail really isn't the focus of that book - and that's okay. Having read it, I now feel like I have a good grasp of the field, but to really cater to my personal learning style, I will have to read something else in addition. I personally learn best when there is practical coding work happening. We used our group discussions to work on some examples ourselves (Kaggle competitions and similar), which added a good amount of depth to the pure text book.
The book itself can be found as a free download on Springer ebooks, and if you want a broad overview of Data Science, I can recommend it. If you want to be a full data scientist after having read the book, you will need to put in some more practical work yourself.
The story of a former Apple engineer who was part of the team working on the software for the original iPhone -- hence "the golden age of Steve Jobs" as the subtitle of this book (sounds like he's only ever met Jobs 2 or 3 times though).
Interesting details in parts. A little surprising but also calming to read that some/most of hist struggles during work seem familiar from a daily coding experience.
In the book, he tries to sum up the core of what he thinks makes the creative process at Apple be what it is. Interesting to read in parts, but he clearly "only" had the inside view from one engineering team. The overarching meta view including management, marketing, etc is lacking. Still, some insightful anecdotes even though the process he distills in the end isn't completely convincing to me.
One thing that bugs me is the continuous stressing of how much Apple makes decisions driven by "Taste", rather than data-driven (he's throwing out punches at Google all the time). Right in the next paragraph, he tells the story of how ingeniously clever they derived the "perfect" size of an icon on the home screen. Surprise: They do it data-driven by running experiments with a simple app. Inconsistencies like this make the whole argument stumble here and there. Still, an interesting and quick read.
128 pages of scribbled notes. Very compact and informal introduction to electronics. No unnecessary stories told. Instead, there is room for 100 simple circuits to try out. And that's what I still have to do in order to really "complete" this book.
Teilweise ein wenig albern, aber wenn man sich darauf einlassen kann, ist das Buch eine gute und praktische Einführung in die Thematik. Gut: Alles wird in praktischen Projekten erklärt. Manchmal nicht so gut: Einige Projekte sind einfach Selbstzweck, um eine gewisse Sache zu illustrieren. Das ist okay - aber Projekte, die wirklich einzusetzen sind, sind schon cooler (gibt es aber auch im Buch). Ich habe übrigens die erste Ausgabe gelesen. Die war "nur" 600 Seiten lang. Ging aber erstaunlich schnell, da man mit Programmiererfahrung ungefähr ein Drittel einfach überfliegen kann.
Interesting read to learn about Apple's history and Steve Jobs' leadership style. The premise of the book however sounds a bit strange to me, as it seems to be trying to tell the reader what to do to be like Steve Jobs without questioning his actions. Also the writing is a bit out of date of course, as it was written a few years before Jobs' passing away. He certainly was an inspiring person and this book seems to give a good introduction to his role at Apple (and Pixar), but to have a more profound image about his life I think I'll read more from different sources.
Some kind of biography about Richard Stallman and his fight for Free Software. Interesting read that makes me want to create more publicly available software.
Ein kompaktes Buch über die Geschichte der KI. Keine wirklich tiefe Lektüre sondern eine sehr oberflächliche Sammlung von Meilensteinen aus Technologie, Philosophie, Literatur und Film. Eher ein Coffee Table Book.
Kurzweilig zu lesen und ich habe mir mal die erwähnten Filme notiert, die im Laufe der Jahrzehnte unser kulturelles Verständnis von KI verbildlichen:
Ich persönlich würde zumindest noch diese ergänzen: