File Size: 4900 KB
Print Length: 354 pages
Publisher: Basic Books; 1 edition (September 22, 2015)
Publication Date: September 22, 2015
The guide also puts many techniques in historical perspective which i found very helpful, such as the rise, fall and rise again of deep nerve organs networks with support vector machines taking a guide as the hottest method in between (while also making clear that SVMs are a useful method with unique strengths today). Finally, it makes clear that these techniques are not all competing to be the best overall at everything, but that they can be taken quite supporting and/or they have unique talents within certain problem domains. The book accomplishes all of this through a survey of broad subfields of ML, how each has attempted to be *the* master algorithm, has fallen short in some ways, but remains the best at some things and can play a role in the state of the art master formula (while acknowledging we're not quite there yet). Therefore while the term 'master algorithm' is somewhat of a gimmick (as he or she acknowledges), it's a good way to consider what ML is attempting to accomplish as a field: building working, adaptive software systems with less and less individual assistance by learning from data, and also to see how many specific techniques have played a role happening.
What I don't know is how accessible this book might be to someone who's less technological. I think the first couple chapters would be a great read for anyone with a general interest, making clear how ML differs from the traditional software / automation that has taken us so far, but maybe the details within the relax of the chapters that go into more depth would be too in the weeds.
I've also read some other reviews from technical readers that assert the book does not have enough depth to be helpful, but this wasn't the case for me personally, in fact the level of detail was perfect—just deep enough to match with details I'd skimmed before in previous surveys of the field yet not so deep that I actually couldn't get through and enjoy the chapters in a casual evening read. The author also described some concepts better than I've read anywhere else before, such as the debate is between frequentist and bayesian statisticians., It is a tremendous work for people that are looking for a good overview of the major hypotheses behind machine learning but aren't necessarily seeking to jump deep into the underlying math. The book has a very humorous sculpt, when appropriate, and creates numerous examples of machine learning and AI in practical use in industry. Typically the book coincides with the free online course Pedro Domingos teaches at [... ]
Well done!, The Master Protocol by Pedro Domingos is a fascinating and accessible deep dive into unnatural intelligence written by a giant in the field. It explains the historical past, functionality, limitations, promise, and implications of a technology that is reshaping nearly every industry. The initial and final chapter are particularly important as they provide the conceptual framework for making sense of the machine learning universe. If you want to understand where computing is headed, this is required reading., Great overview including the math involved, which is described Ina creative but intuitive way. Some parts require one to reread a couple of times in part because it takes some time to visualize what's proceeding on, but overall very accessible.
The last part really frames the AI debate over humanity's future perfectly. It also provides the next steps for people to learn and explore machine learning on the own-it looks fun!, Allowed me to put the subject into a diverse perspective seeing how different aspects relate to each other. For non experts it's worth it to read sections more than once to improve understanding. Well written and a strong share., "The Master Algorithm" provides a stupendous panorama of curently one of the hotest and influential topics in Science and Technological innovation. I only get the impression that - although many won't agree - a little bit more of Math would actually facilitate understanding a number of the current five main pillars of ML. Once I actually started reading it, I actually couldn't stop until done. Really engaging., Really strange book, in between a scientific journal, science fiction and programming course.
A little little bit hard to follow the writer's thoughts from time to time, the way the sentences are organized is super dense and convoluted.
Total, a perfect intro to machine learning, would recommend., The book reviews various algorithms of machine learning, and discusses their ramifications to psychology and viewpoint. It is not clear who is supposed to be the reader of the book. Readers that do not need background in computer science will face problems comprehending the algorithms, and computer scientists will not be enthusiastic about the reviews since they include almost no technical explanations. The review of the philosophical history (mainly Hume's philosophy) is not accurate to put it lightly. Continue to the book raises interesting questions and presents interesting hypotheses.
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