50 Algorithms Every Programmer Should Know: Tackle computer science challenges with classic to modern algorithms in machine learning, software design, data systems, and cryptography

I am a practising programmer for last 10 years, I have been studying a lot of programming questions and varieties for my personal and professional growth, I stumbled upon this book which has changed the narrative and outlook of my learning, This is the most concise book anyone can buy to start coding it has all the necessary steps to break the ice and get into hands on development, it has live written examples which you can code it would display necessary result and also the book progresses from easy to hard coding problems. This book also helps in developing intuitiveness, the book also have max coverage in Python which helps young programmers to work and learn

This is a great book every developer/programmer should have. It provides so much knowledge and I totally recommend.

Well structured, very informative and it is easy to follow. I bought the book to learn and incorporate algorithms, the author starts with ordering and searching, then dive into ML. While that is my ultimate objective, I wanted to get a little bit more familiar with "lesser" algorithms first, to better understand the ML's, but it's ok, ML is very exciting, and the author does a good job introducing it the sooner he can.There room for improvement for future editions though, as there are several errors in the books, especially in the chapter 6 and 7, where the author (or the corrector) either invert terms or acronyms (like in the explanation of TPR, which goes to TRP back a forth confusion the explanation (page 208)), or the notation, like when defining the dimension of a matrix (b files and n features, then you have a matrix n x b, it is actually b x n (page 191). in linear algebra, order matters), or figures without caption (Figure 7.2 Add a caption here...). The code is clear, also with several mistakes in some cases, but these can be corrected as you get the code in GitHUb. Some others are more due to Python versions (like deprecated words i.e. affinity vs metric) or old .csv paths, no longer valid. Some conclusions get also mismatched, like "we increase the decision boundary to get better precision and can expect more recall, and we lower the decision boundary to get better recall and can expect less precision".I haven't finished the book yet, and for sure will find more omissions, but don't get the wrong conclusion, the book is quite good, and you can learn a lot from the way the author structures the flow from how an algorithm works, how to implement it and what application it is useful for.

This is the second book describing algorithms every programmer should know. I use this book to teach new programmers new languages: They must implement the algorithms in each new language they want to learn.

I've gone through the big and found it is great for those who are beginners in algorithms. It will explain in detail about the o(n) complexity and the difference of the implementation in different algorithms. I've always had problems understanding the o(n) complexity, but this has helped my mind to better understand the meaning and start to figure out which algorithms make sense in which situations.The best part of the book is that it uses python to teach the algorithms which is a very familiar language to most programmers, even those working with NLP.

This book is perfect for anyone wanting to learn about algorithms and machine learning without the math stress. Complex math is made easy to understand, thanks to the author’s clear explanations. Even if you’re not great at math, you’ll get it! Helpful Python examples are spread throughout the book to help make tricky ideas clear for both beginners and experienced readers. The book's friendly style makes learning fun and takes away the math worry. It’s definitely one of my top picks of data science or AI books.

50 Algorithms, I didn't try to count them, but I like the book's content - Fundamental and Core Algs, ML Algs, and Advanced Topics. The author has been teaching for Google and has a PhD in computer science. The book is very well laid out, clean, and easy to read. This would be a great book to use when first learning to program, at least the first section.In the Fundamentals and Core section, Python is used in the data structures examples, and I like how he covers the time complexity for each type. He also addresses the "basic idea behind the use of stacks and queues" and "practical examples" of trees, rather than just saying - here's how these can be implemented, though the stacks and queues info is very basic and not necessarily an example one would face on the job. A collection of sorting algorithms is presented, along with advice for choosing one. I like that interpolation search is included, as well as an implementation in Python, and a section on "Practical applications" related to search, i.e. a little something extra to go w/the definitions. Chapter 4 Designing Algorithms is awesome and includes "understanding algorithmic strategies" such as divide-and-conquer, dynamic programming, and greedy programming. Linear programming is explained. Finally, in the Fundamentals and Core Section there is a terrific chapter on Graph Algorithms , which includes info on network analysis, the BFS and DFS graph traversals, and a fraud detection network analysis case study.The ML Algs section covers the basics - unsupervised learning, traditional supervised learning, neural networks, NLP, and sequential models. A nice feature is that a classifiers challenge is used for several classification algorithms to compare them, similarly a regressors challenge is stated and applied to three different algorithms. The scikit-learn ML library for Python is used for the challenge examples. Keras is used to define a NN model. TensorFlow is explained, as are the different types of NNs. The NN sections are very nice, concisely explained, w/beautiful color diagrams.The Advanced Topics chapter covers the evolution of advanced sequential modeling techniques. This gets into autoencoders, the Seq2Seq model, the attention mechanism, self-attention, transformers: the evolution after self-attention, and LLMs. Recommendation engines, strategies for data handling, cryptography - including bits on Blockchain and MTIM attacks, large-scale algs - including Amdahl's law, CUDA, and large-scale algs in cloud computing, and a final, terrific closing chapter on Practical Considerations - including telling the story of the Failure of Tay, the Twitter AI bot.This is a clean, tight, concisely written book with great content and highly relevant and useful color diagrams. I think a nice companion to the book could be a workbook with exercises for getting practice on choosing fundamental data structures and computing the complexity when using them in algorithms, with solutions, that is, an aid to help developers get some practice making choices and analyzing the consequences. Similarly, exercises could be written for other parts of the book. This is a book that the author can definitely be proud of, and that the reader can easily read and gain from. The index looks helpful too.