Also as a side note, does anyone know why are the Amazon categories so bad? https://www.amazon.com/Systems-Performance-Enterprise-Brenda...since it's essentially about how to perf measure, and debug computers in the cloud which is the annoying part of distributed systems. Also you should keep in mind that there are some books that are specifically about doing distributed systems using a particular framework e.g. this one on using Akka on JVM https://www.amazon.com/Akka-Action-Raymond-Roestenburg/dp/16...
since it's essentially about how to perf measure, and debug computers in the cloud which is the annoying part of distributed systems.
Also you should keep in mind that there are some books that are specifically about doing distributed systems using a particular framework e.g. this one on using Akka on JVM https://www.amazon.com/Akka-Action-Raymond-Roestenburg/dp/16...
# Elements of Programming
https://www.amazon.com/Elements-Programming-Alexander-Stepan...This book proposes how to write C++-ish code in a mathematical way that makes all your code terse. In this talk, Sean Parent, at that time working on Adobe Photoshop, estimated that the PS codebase could be reduced from 3,000,000 LOC to 30,000 LOC (=100x!!) if they followed ideas from the book https://www.amazon.com/Grammar-Graphics-Statistics-Computing...This book changed my perception of creativity, aesthetics and mathematics and their relationships. Fundamentally, the book provides all the diverse tools to give you confidence that your graphics are mathematically sound and visually pleasing. After reading this, Tufte just doesn't cut it anymore. It's such a weird book because it talks about topics as disparate Bayesian rule, OOP, color theory, SQL, chaotic models of time (lolwut), style-sheet language design and a bjillion other topics but always somehow all of these are very relevant. It's like if Bret Victor was a book, a tour de force of polymathical insanity. The book is in full color and it has some of the nicest looking and most instructive graphics I've ever seen even for things that I understand, such as Central Limit Theorem. It makes sense the the best graphics would be in the book written by the guy who wrote a book on how to do visualizations mathematically. The book is also interesting if you are doing any sort of UI interfaces, because UI interfaces are definitely just a subset of graphical visualizations. # Scala for Machine Learning https://www.amazon.com/Scala-Machine-Learning-Patrick-Nicola...This book almost never gets mentioned but it's a superb intro to machine learning if you dig types, scalable back-ends or JVM. It’s the only ML book that I’ve seen that contains the word monad so if you sometimes get a hankering for some monading (esp. in the context of ML pipelines), look no further. Discusses setup of actual large scale ML pipelines using modern concurrency primitives such as actors using the Akka framework. # Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...Not released yet but I've been reading the drafts and it's a nice intro to machine learning using modern ML frameworks, TensorFlow and Scikit-Learn. # Basic Category Theory for Computer Scientists https://www.amazon.com/Markov-Logic-Interface-Artificial-Int...Have you ever wondered what's the relationship between machine learning and logic? If so look no further. # Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) https://www.amazon.com/Designing-Scalability-Erlang-OTP-Faul...Even though this is an Erlang book (I don't really know Erlang), 1/3 of the book is devoted to designing scalable and robust distributed systems in a general setting which I found the book worth it on it's own. # Practical Foundations for Programming Languages https://www.amazon.com/First-Course-Network-Theory/dp/019872...Up until recently I didn't know the difference between graphs and networks. But look at me now, I still don't but at least I have a book on it.
This book proposes how to write C++-ish code in a mathematical way that makes all your code terse. In this talk, Sean Parent, at that time working on Adobe Photoshop, estimated that the PS codebase could be reduced from 3,000,000 LOC to 30,000 LOC (=100x!!) if they followed ideas from the book https://www.amazon.com/Grammar-Graphics-Statistics-Computing...This book changed my perception of creativity, aesthetics and mathematics and their relationships. Fundamentally, the book provides all the diverse tools to give you confidence that your graphics are mathematically sound and visually pleasing. After reading this, Tufte just doesn't cut it anymore. It's such a weird book because it talks about topics as disparate Bayesian rule, OOP, color theory, SQL, chaotic models of time (lolwut), style-sheet language design and a bjillion other topics but always somehow all of these are very relevant. It's like if Bret Victor was a book, a tour de force of polymathical insanity. The book is in full color and it has some of the nicest looking and most instructive graphics I've ever seen even for things that I understand, such as Central Limit Theorem. It makes sense the the best graphics would be in the book written by the guy who wrote a book on how to do visualizations mathematically. The book is also interesting if you are doing any sort of UI interfaces, because UI interfaces are definitely just a subset of graphical visualizations. # Scala for Machine Learning https://www.amazon.com/Scala-Machine-Learning-Patrick-Nicola...This book almost never gets mentioned but it's a superb intro to machine learning if you dig types, scalable back-ends or JVM. It’s the only ML book that I’ve seen that contains the word monad so if you sometimes get a hankering for some monading (esp. in the context of ML pipelines), look no further. Discusses setup of actual large scale ML pipelines using modern concurrency primitives such as actors using the Akka framework. # Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...Not released yet but I've been reading the drafts and it's a nice intro to machine learning using modern ML frameworks, TensorFlow and Scikit-Learn. # Basic Category Theory for Computer Scientists https://www.amazon.com/Markov-Logic-Interface-Artificial-Int...Have you ever wondered what's the relationship between machine learning and logic? If so look no further. # Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) https://www.amazon.com/Designing-Scalability-Erlang-OTP-Faul...Even though this is an Erlang book (I don't really know Erlang), 1/3 of the book is devoted to designing scalable and robust distributed systems in a general setting which I found the book worth it on it's own. # Practical Foundations for Programming Languages https://www.amazon.com/First-Course-Network-Theory/dp/019872...Up until recently I didn't know the difference between graphs and networks. But look at me now, I still don't but at least I have a book on it.
This book changed my perception of creativity, aesthetics and mathematics and their relationships. Fundamentally, the book provides all the diverse tools to give you confidence that your graphics are mathematically sound and visually pleasing. After reading this, Tufte just doesn't cut it anymore. It's such a weird book because it talks about topics as disparate Bayesian rule, OOP, color theory, SQL, chaotic models of time (lolwut), style-sheet language design and a bjillion other topics but always somehow all of these are very relevant. It's like if Bret Victor was a book, a tour de force of polymathical insanity.
The book is in full color and it has some of the nicest looking and most instructive graphics I've ever seen even for things that I understand, such as Central Limit Theorem. It makes sense the the best graphics would be in the book written by the guy who wrote a book on how to do visualizations mathematically. The book is also interesting if you are doing any sort of UI interfaces, because UI interfaces are definitely just a subset of graphical visualizations.
# Scala for Machine Learning
https://www.amazon.com/Scala-Machine-Learning-Patrick-Nicola...This book almost never gets mentioned but it's a superb intro to machine learning if you dig types, scalable back-ends or JVM. It’s the only ML book that I’ve seen that contains the word monad so if you sometimes get a hankering for some monading (esp. in the context of ML pipelines), look no further. Discusses setup of actual large scale ML pipelines using modern concurrency primitives such as actors using the Akka framework. # Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...Not released yet but I've been reading the drafts and it's a nice intro to machine learning using modern ML frameworks, TensorFlow and Scikit-Learn. # Basic Category Theory for Computer Scientists https://www.amazon.com/Markov-Logic-Interface-Artificial-Int...Have you ever wondered what's the relationship between machine learning and logic? If so look no further. # Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) https://www.amazon.com/Designing-Scalability-Erlang-OTP-Faul...Even though this is an Erlang book (I don't really know Erlang), 1/3 of the book is devoted to designing scalable and robust distributed systems in a general setting which I found the book worth it on it's own. # Practical Foundations for Programming Languages https://www.amazon.com/First-Course-Network-Theory/dp/019872...Up until recently I didn't know the difference between graphs and networks. But look at me now, I still don't but at least I have a book on it.
This book almost never gets mentioned but it's a superb intro to machine learning if you dig types, scalable back-ends or JVM.
It’s the only ML book that I’ve seen that contains the word monad so if you sometimes get a hankering for some monading (esp. in the context of ML pipelines), look no further.
Discusses setup of actual large scale ML pipelines using modern concurrency primitives such as actors using the Akka framework.
# Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...Not released yet but I've been reading the drafts and it's a nice intro to machine learning using modern ML frameworks, TensorFlow and Scikit-Learn. # Basic Category Theory for Computer Scientists https://www.amazon.com/Markov-Logic-Interface-Artificial-Int...Have you ever wondered what's the relationship between machine learning and logic? If so look no further. # Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) https://www.amazon.com/Designing-Scalability-Erlang-OTP-Faul...Even though this is an Erlang book (I don't really know Erlang), 1/3 of the book is devoted to designing scalable and robust distributed systems in a general setting which I found the book worth it on it's own. # Practical Foundations for Programming Languages https://www.amazon.com/First-Course-Network-Theory/dp/019872...Up until recently I didn't know the difference between graphs and networks. But look at me now, I still don't but at least I have a book on it.
Not released yet but I've been reading the drafts and it's a nice intro to machine learning using modern ML frameworks, TensorFlow and Scikit-Learn.
# Basic Category Theory for Computer Scientists
https://www.amazon.com/Markov-Logic-Interface-Artificial-Int...Have you ever wondered what's the relationship between machine learning and logic? If so look no further. # Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) https://www.amazon.com/Designing-Scalability-Erlang-OTP-Faul...Even though this is an Erlang book (I don't really know Erlang), 1/3 of the book is devoted to designing scalable and robust distributed systems in a general setting which I found the book worth it on it's own. # Practical Foundations for Programming Languages https://www.amazon.com/First-Course-Network-Theory/dp/019872...Up until recently I didn't know the difference between graphs and networks. But look at me now, I still don't but at least I have a book on it.
Have you ever wondered what's the relationship between machine learning and logic? If so look no further.
# Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
https://www.amazon.com/Designing-Scalability-Erlang-OTP-Faul...Even though this is an Erlang book (I don't really know Erlang), 1/3 of the book is devoted to designing scalable and robust distributed systems in a general setting which I found the book worth it on it's own. # Practical Foundations for Programming Languages https://www.amazon.com/First-Course-Network-Theory/dp/019872...Up until recently I didn't know the difference between graphs and networks. But look at me now, I still don't but at least I have a book on it.
Even though this is an Erlang book (I don't really know Erlang), 1/3 of the book is devoted to designing scalable and robust distributed systems in a general setting which I found the book worth it on it's own.
# Practical Foundations for Programming Languages
https://www.amazon.com/First-Course-Network-Theory/dp/019872...Up until recently I didn't know the difference between graphs and networks. But look at me now, I still don't but at least I have a book on it.
Up until recently I didn't know the difference between graphs and networks. But look at me now, I still don't but at least I have a book on it.
https://smile.amazon.com/Designing-Scalability-Erlang-OTP-Fa...https://www.manning.com/books/the-little-elixir-and-otp-guid... https://www.manning.com/books/elixir-in-action
https://www.manning.com/books/the-little-elixir-and-otp-guid...
https://www.manning.com/books/elixir-in-action
Also as a side note, does anyone know why are the Amazon categories so bad? https://www.amazon.com/Systems-Performance-Enterprise-Brenda...
since it's essentially about how to perf measure, and debug computers in the cloud which is the annoying part of distributed systems.
Also you should keep in mind that there are some books that are specifically about doing distributed systems using a particular framework e.g. this one on using Akka on JVM https://www.amazon.com/Akka-Action-Raymond-Roestenburg/dp/16...
# Elements of Programming
https://www.amazon.com/Elements-Programming-Alexander-Stepan...
This book proposes how to write C++-ish code in a mathematical way that makes all your code terse. In this talk, Sean Parent, at that time working on Adobe Photoshop, estimated that the PS codebase could be reduced from 3,000,000 LOC to 30,000 LOC (=100x!!) if they followed ideas from the book https://www.amazon.com/Grammar-Graphics-Statistics-Computing...
This book changed my perception of creativity, aesthetics and mathematics and their relationships. Fundamentally, the book provides all the diverse tools to give you confidence that your graphics are mathematically sound and visually pleasing. After reading this, Tufte just doesn't cut it anymore. It's such a weird book because it talks about topics as disparate Bayesian rule, OOP, color theory, SQL, chaotic models of time (lolwut), style-sheet language design and a bjillion other topics but always somehow all of these are very relevant. It's like if Bret Victor was a book, a tour de force of polymathical insanity.
The book is in full color and it has some of the nicest looking and most instructive graphics I've ever seen even for things that I understand, such as Central Limit Theorem. It makes sense the the best graphics would be in the book written by the guy who wrote a book on how to do visualizations mathematically. The book is also interesting if you are doing any sort of UI interfaces, because UI interfaces are definitely just a subset of graphical visualizations.
# Scala for Machine Learning
https://www.amazon.com/Scala-Machine-Learning-Patrick-Nicola...
This book almost never gets mentioned but it's a superb intro to machine learning if you dig types, scalable back-ends or JVM.
It’s the only ML book that I’ve seen that contains the word monad so if you sometimes get a hankering for some monading (esp. in the context of ML pipelines), look no further.
Discusses setup of actual large scale ML pipelines using modern concurrency primitives such as actors using the Akka framework.
# Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...
Not released yet but I've been reading the drafts and it's a nice intro to machine learning using modern ML frameworks, TensorFlow and Scikit-Learn.
# Basic Category Theory for Computer Scientists
https://www.amazon.com/Markov-Logic-Interface-Artificial-Int...
Have you ever wondered what's the relationship between machine learning and logic? If so look no further.
# Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
https://www.amazon.com/Designing-Scalability-Erlang-OTP-Faul...
Even though this is an Erlang book (I don't really know Erlang), 1/3 of the book is devoted to designing scalable and robust distributed systems in a general setting which I found the book worth it on it's own.
# Practical Foundations for Programming Languages
https://www.amazon.com/First-Course-Network-Theory/dp/019872...
Up until recently I didn't know the difference between graphs and networks. But look at me now, I still don't but at least I have a book on it.
https://smile.amazon.com/Designing-Scalability-Erlang-OTP-Fa...
https://www.manning.com/books/the-little-elixir-and-otp-guid...
https://www.manning.com/books/elixir-in-action