50 Algorithms Each Programmer Ought to Know: Deal with laptop science challenges with basic to trendy algorithms in machine studying, software program design, knowledge techniques, and cryptography
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From the Writer
What makes this ebook completely different from different books on algorithms for programmers?
Whereas many algorithm books cowl conventional and foundational matters, I’ve tried to maintain this ebook trendy by diving into state-of-the-art algorithms in areas like deep studying, Massive Language Fashions (LLMs), and sequential fashions. I wish to present that the world of algorithms, just isn’t static, however at all times evolving, which I additionally assume is a extra complete view of algorithms.
An necessary matter I’d wish to deliver up about that is bias and explainability. In right now’s world, the place AI ethics is essential, I’ve wished this ebook to face out in addressing the all-important matter of recognizing hidden biases in knowledge and making certain the transparency and explainability of algorithms.
(Picture: Deep and vast LLM structure fashions)
What this ebook covers Neural networks and deep studying Graph algorithms for fraud detection Machine studying functions Pure language processing Massive Language Fashions Advice techniques Cryptography on cloud platforms Parallel processing strategies
This second version of fifty Algorithms Each Programmer Ought to Know not solely has the up to date variations of most algorithms from the primary version consistent with present IT traits, nevertheless it additionally goals to assist readers delve into superior deep studying architectures and new chapters on sequential fashions like LSTMs, GRUs, RNNs, and LLMs.
Moreover, via this version, I’ve tried to make clear modern matters similar to addressing hidden knowledge biases and demystifying algorithm explainability.
What method does this ebook take to get readers began?
I’ve designed this ebook to strike a stability between serving to readers choose and use an algorithm to unravel a real-world drawback and explaining the logic behind it for a deeper understanding.
For instance, it begins with an introduction to algorithms, permitting readers to grasp the varied algorithm design strategies. As readers progress, they discover extra superior ideas and are taught the sensible utility of those algorithms utilizing real-world examples.
(Picture: A elementary algorithm design)
How has your profession helped you write this ebook?
I’ve had a wealthy background in cloud computing, AI, and machine studying that I wish to deliver to the desk with this ebook. I’ve had expertise in high-profile tasks with the Canadian Federal Authorities, taught at Google, and am a visiting professor at Carleton College. My expertise in all of those completely different tasks with their very own focuses and challenges has taught me some insights that I’d prefer to share, whether or not it’s concerning the applicability of some algorithms, or perhaps their ethics, or their future path. I hope it additionally provides a singular perspective to what I’ve written.
Writer : Packt Publishing; 2nd ed. version (September 29, 2023)
Language : English
Paperback : 538 pages
ISBN-10 : 1803247762
ISBN-13 : 978-1803247762
Merchandise Weight : 2.05 kilos
Dimensions : 1.21 x 7.5 x 9.25 inches
Prospects say
Prospects discover this ebook supplies a complete introduction to algorithms and machine studying with out math stress. It helps develop intuitiveness with useful Python examples all through. The writer’s clear explanations make complicated math straightforward to grasp, making it probably the most concise ebook anybody should purchase to begin. The code is evident, although some errors are present in some instances.
AI-generated from the textual content of buyer opinions
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Lateef Audu –
Educating
This is a great book every developer/programmer should have. It provides so much knowledge and I totally recommend.
Roboticist –
Truly essential knowledge
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.
Jose Alexander Cadiz –
Well structured, informative, with room to improve
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.
Customer –
Programming holy Grail
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
Allen Wyma –
Great Book for Beginners into Algorthms
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.
lucia –
The perfect data science book
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 authorâs clear explanations. Even if youâre not great at math, youâll 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. Itâs definitely one of my top picks of data science or AI books.
RSR –
It looks a well written book, so far â¦
It is just too early for a review. The logic sequence of its content looks good. Initially, I will give it 3 stars and through the reading I will increase or decrease the stars.
Ali Hamza –
Having journeyed through numerous programming and algorithm books, including the previous edition “40 Algorithms Every Programmer Should Know”, I am thrilled with the advancements and in-depth additions in Imran Ahmad’s new edition, “50 Algorithms Every Programmer Should Know”. This version not only expands the arsenal of algorithms but also refines and deepens our understanding of each topic.From the onset, Ahmad provides a robust introduction, seamlessly merging foundational knowledge from the previous edition while introducing fresh and advanced content. The progression is smooth and intuitive, ensuring both newcomers and returning readers are catered to effectively.A highlight of this new edition is its enriched discussion on Advanced Deep Learning Architectures. While the entire book is a testament to Ahmad’s meticulous approach, Chapters 10 and 11 stand out. They introduce Sequential Networks in such an articulate manner, paving the way for readers to grasp the nuances of Generative AI beautifully. These chapters are a testament to the evolution of deep learning and are a must-read for anyone keen on the future of AI.The real-world case studies, the insights into neural networks, and the deep dive into recommendation engines remain as enlightening as ever. But what elevates this edition is its focus on modern challenges like hidden bias in data and the pressing need for algorithm explainability.While the previous edition was a gem in its own right, this major new version broadens the horizon, making it an essential read for Python programmers and algorithm enthusiasts alike. Even if you’ve devoured the previous edition, this one promises new insights, deeper understanding, and a refreshed perspective on the world of algorithms.In sum, if the previous edition was a foundational pillar, “50 Algorithms Every Programmer Should Know” is a towering edifice of knowledge. It’s not just an upgrade; it’s a transformative experience in understanding algorithms. A hearty applause to Imran Ahmad for this monumental work!
Andreas –
Often unnecessarily convoluted and repetitive language while the content so far (after 7 chapters) feels like a series of entry level articles lacking depth or proper explanations.Multiple minor inconsistencies between code and text, typos and bad formatting and seemingly missing the section “Performance analysis of merge sort”.
Marzio –
Il libro è buono, copre molti concetti abbastanza bene e lo consiglio. Tuttavia ci sono vari errori nella formule presentate e alcuni temi sono trattati in maniera approssimativa e confusa. La parte su Deep learning, RNN e Transformers potrebbe essere spiegata in maniera più fluida, creando maggiore legame tra i concetti.
Amazon Customer –
Admittedly I have only looked into 1 chapter, chapter 9: natural language processing but I’m not impressed. First 3 pages talk about what NLP is at the depth that could be summarised in 5 sentences. Next 2 pages are dedicated to tokenisation which has the complexity of splitting text by white space. For this we use a python library. Next 3 pages are in similar vein I kid you not spending 1/3rd of a page on white space removal. After burning 2 pages on tokenisation. The only actual explanation on how things will work will rely on using a python library and how to pass arguments to the library functions in about 4 lines of python.There is some useful information in the chapter as a whole, but it isn’t a description of any algorithm. It a vague description of a topic, I probably can get more info about on wikipedia.