Getting learners to read textbooks and use other teaching aids effectively can be tricky. Especially, when the books are just too dreary.
In this post, we’ve compiled great e-resources for you digital natives looking to explore the exciting world of Machine Learning and Neural Networks. But before you dive into the deep end, you need to make sure you’ve got the fundamentals down pat.
It doesn’t matter what catches your fancy, machine learning, artificial intelligence, or deep learning; you need to know the basics of math and stats—linear algebra, calculus, optimization, probability—to get ahead.
This 2013 edition by Golub and Van Loan, published by The Johns Hopkins University Press, teaches you about matrix analysis, linear systems, eigenvalues, discrete Poisson solvers, least squares, parallel LU, pseudospectra, Singular Value Decomposition, and much more.
This book is an indispensable tool for engineers and computational scientists. It has great reviews on Amazon, especially by users looking for problems, discussions, codes, solutions, and references in numerical linear algebra.
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Written by Devroye, Lugosi, and Györfi, this an excellent book for graduate students and researchers. The book covers various probabilistic techniques including nearest neighbour rules, feature extraction, Vapnik-Chervonenkis theory, distance measures, parametric classification, and kernel rules.
Amazon reviewers laud it for its nearly 500 problems and exercises.
Wikipedia says “The terms pattern recognition, machine learning, data mining and knowledge discovery in databases are hard to separate, as they largely overlap in their scope.”
No wonder, machine learning enthusiasts swear by this comprehensive, theoretical book on “nonparametric, distribution-free methodology in Pattern Recognition.”
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Erwin Kreyszig’s book beautifully covers the basics of applied math in a comprehensive and simplistic manner for engineers, computer scientists, mathematicians, and physicists.
It teaches you Fourier analysis, vector analysis, linear algebra, optimization, graphs, complex analysis, and differential and partial differential equations.
It has up-to-date and effective problem sets that ensure you understand the concepts clearly.
A collection of math and stats reference material from the University of California (Berkeley) and other sources put together by Matthias Vallentin, this cookbook is a must-have for learners.
There are no elaborate explanations but concise representations of key concepts. You can view it on GitHub, or download a PDF file using the link below.
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This book written by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani is meant for non-math students.
For data scientists, this is a valuable addition because of its R labs.
The TOC includes linear regression, classification, resampling methods, linear model and regularization, tree-based methods, shrinkage approaches, clustering, support vector machines, and unsupervised learning.
With interesting real-world examples and attractive graphics, this is a great text for statistical tools and techniques.
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Cameron Davidson-Pilon describes Bayesian methods and probabilistic programming from math and computation perspectives.
The book discusses modeling Bayesian problems using Python’s PyMC, loss functions, the Law of Large Numbers, Markov Chain Monte Carlo, priors, and so lots more.
The content is open sourced. The print version has updated examples, EOC questions, and improved and extra sections.
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Authors Trevor Hastie, Robert Tibshirani, and Jerome Friedman (all three are Stanford professors) discuss supervised learning, linear methods of regression and classification, kernel smoothing methods, regularization, model selection and assessment, additive trees, SVM, neural networks, random forests, nearest neighbors, unsupervised learning, ensemble methods, and more.
This book covers a broad range of topics is particularly useful for researchers interested in data mining and machine learning.
You need to know linear algebra and some stats before you can appreciate the text.
This is what one of the reviewers said about the book on Amazon: The Elements of Statistical Learning is a comprehensive mathematical treatment of machine learning from a statistical perspective.
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David Barber’s books is a comprehensive piece of writing on graphical models and machine learning.
Meant for final-year undergraduate and graduate students, this text has ample guidelines, examples,and exercises. The author also offers a MATLAB toolbox and a related website.
It covers inference in probabilistic models including belief networks, inference in trees,the junction tree algorithm, decision trees; learning in probabilistic models including Naive Bayes, hidden variables and missing data, supervised and unsupervised linear dimension reduction, Gaussian processes, and linear models; dynamic models including discrete- and continuous-state model Markov models, and distribution computation; and approximate inference.
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David MacKay exciting book discusses key concepts that form the core of machine learning, data mining, pattern recognition, bioinformatics, and cryptography.
Amazon reviewers find the illustrations, depth, and “esoteric” approach remarkable.
It is a great book on information theory and inference, which covers topics such as data compression, noisy-channel coding, probabilities, neural networks, and sparse graph codes.
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This what Elon Musk, co-founder of Tesla Motors, has to say about this definitive text written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
The authors talk about applied math and machine learning basics, deep networks and modern practices, and deep learning research.
For engineers interested in neural networks, this could well be their bible.
The book is highly recommended for people in academia, providing the required mathematical background to fully appreciate deep learning in its current state.
Free Book: Download here
Michael Nielsen’s free online book is a comprehensive text on the core concepts of deep learning and artificial neural networks.
The book has great interactive elements, but it does not provide solutions for the exercises. Laid out like a narrative, Nielsen holds onto core math and code to explain the key ideas.
He talks about back propagation, hyper parameter optimization, activation functions, neural networks as functional approximates, regularization, a little about convolution neural networks, etc.
The author includes valuable links to ongoing research and influential research papers and related tutorials.
Free Book: Download here
Alex Graves discusses how to classify and transcribe sequential data, which is important in part-of-speech tagging, gesture, handwriting, and speech recognition, and protein secondary structure prediction.
He talks about the role of recurrent neural networks in sequence labeling.
Long short-term memory, a comparison of network architectures, hidden Markov model hybrids, connectionist temporal classification, multidimensional networks, and hierarchical sub sampling networks are other chapters in this book.
Free Book: Download here
Richard S. Sutton and Andrew G. Barto’s pioneering book on reinforcement learning covers the intellectual background, applications, algorithms, and the future of this exciting field. These University of Massachusetts Professors describe this artificial intelligence concept with clarity and simplicity.
This book includes interesting topics such as Markov decision processes, Monte Carlo methods, dynamic programming, temporal-difference learning, eligibility traces, and artificial neural networks.
Free Book: Download here
What’s better than getting educational resources that are free and authored by pioneers in the field?
Can’t think of a downside really…Especially for struggling students, these ebooks are a boon.
They don’t need to wait for the books to turn up at the library or swap with others; grab them and start learning!
So, what’s stopping you from picking up one of these excellent books and fashioning a successful career in data science, AI, or machine learning?
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