Eva

A love story set in British private schools and across the battlegrounds of WWI. Sidney Ellwood and Henry Gaunt, dashing dapper private school boys, constantly reciting poetry and reading Greek epics, are secretly in love. When one of them feels pressured to enlist for England before his time, the other follows shortly behind. What follows are many heartbreaks, of what war does to young bodies and minds.

I feel like I read several books with different tonalities. First we're experiencing the angst of forbidden gay schoolboy crushes (honestly kind of shocked how accepted sexual abuse between the boys seemed to be at those times) with lingering fears and dreams of heroics on the battlefield. Then the cruel No Man's Land reality of WWI is pure tragedy and heartbreak. Followed by suspiciously jolly adventures of a few of the former schoolmates attempting to escape German prisoner-of-war camps while rereading Adam Bede again and again (I had to laugh out loud several times at the continuous Adam Bede jokes). Followed by a final sombre act that makes it very clear how war produces broken men that never can fully heal.

Tonal differences asides, I enjoyed them all, and didn't mind at all how expertly they toyed with my emotions. Winn's writing of the schoolboy charm made me chuckle a lot, and the audio narration was quite excellent as well.

I barely remembered anything from the movie besides Clive Owen in a grey rubble world, so I had an open mind going into this. The book world is similar, a near future that is ridden with infertility. The dystopian premises is very interesting, what does it do to a society, a generation, when they learn they will be the last of humankind? When you're unable to bear children, destined to grow old without caregivers, destined to know that all your contributions to this world will ultimately be useless. While the world building is intriguing, the main plot and the main protagonist are less so. It depicts the transformation of a middle-aged snobbish Brit from resigned detachment to newborn hopefulness. Yet the narrative moves along sluggishly and gets lost in too many details most of the time. Spoiler alert: the last scene and last line of the book is quite similar to the ending of [b:Brideshead Revisited 30933 Brideshead Revisited The Sacred and Profane Memories of Captain Charles Ryder Evelyn Waugh https://images.gr-assets.com/books/1438579340s/30933.jpg 2952196].

Those who travel to mountain tops are half in love with themselves, and half in love with oblivion.

Told as the history of mountains and moutaineering Macfarlane investigates humanity's fascination with high altitude. He documents our relationship with mountains over the last couple centuries: from storytellers of geological history, to romantic pursuits of solitude, to fatal obsessions to reach earth's highest points.

Mountaineering is build on myths of glory, it's a collection of tales from those who survive and those who don't. And those stories exert forces ons us that pull us upwards. They propell us to go where no one else has gone before, to explore the unknown, to risk our lives for the bliss and clarity that awaits on mountain tops.

I loved this, as i love all tales of explorers of the unkown.

This seems to be the only up-to-date book out there giving an overview of the discipline of Machine Learning, but nobody seems to be quite happy with it, and I can see why.

Domingos goes in detail on what he calls the “five tribes” of machine learning:

- Symbolism / Logic with it's decision trees and inverse deduction
- Connectionism with its multilayer perceptrons and backpropagation
- Evolutionaries with their genetic algorithms
- Bayesians with probabilistic inference
- Analogizers with their support vector machines

The level of complexity of his explanations and examples isn't well balanced, some are easy to follow, while other's are just too high-level and would require more hand-holding. Nevertheless you get a decent overview of the field.

The book fails where the author tries to insert himself, his opinions and his quest for the “Master Algorithm”. Or when he tries to add creative analogies, as when he describes the 5 machine learning strategies as boroughs of a city. And then spends multiple pages riffing on that analogy.

Algorithms are recipes and strategies, and whenever we have to make decisions in real life - influenced by a set of restrictions on time/money/space - we apply our own internal algorithms. Sometimes our techniques have grown from years of experience, sometimes we can explain their reasoning, sometimes we refer to it as a gut feeling. Mostly, impressively, there are not too far from how an optimised computer algorithm would solve the same problem. Algorithms to Live By goes through daily-life examples and explains the probabilities and math behind such decision-making problems. How to find a close-enough parking spot without wasting time circling the block. When to explore new restaurant options instead of returning to favorites. How the messiest desk of piles of paper actually resembles the most efficient last-in-last-out caching strategy. Some have magical numbers attached to them (stop exploring after 37% of your options and exploit the next best option), others are well-known principles (like ‘perfect' being the enemy of ‘good'). The book is a good companion to [b:How Not to Be Wrong: The Power of Mathematical Thinking 18693884 How Not to Be Wrong The Power of Mathematical Thinking Jordan Ellenberg https://images.gr-assets.com/books/1387726285s/18693884.jpg 26542434], as both try to coach us into understanding the mathematical parts of our lives a bit more.