Questions tagged [books]
For questions related to books in the context of artificial intelligence. For example, if you're looking for a reference AI book, you may use this tag. If you want someone to clarify something in an AI book, you can use this tag.
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Book suggestion about deploying real deep learning models in real world
Can you suggest books about deploying machine learning algorithms on robots, especially on real time stream? I don't know how to deal with latency and other challenges that real time inference/stream ...
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Gradient: any resource on how to understand everything about it?
I have read some resources about AI, and they all speak about the gradient.
Is there any book focused on this? maybe with tons of images / diagrams?
Cheers
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Where can I find the solutions to the problems in the book "An Introduction to Computational Learning Theory"?
I have been going through "An Introduction to Computational Learning Theory" (Kearns-Vazirani). I don't know if my solutions to the problems are correct and have no other way of checking my ...
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Best calculus books for Deep Learning
Recommend some calculus books for Deep Learning and neural networks. I know what is integration, differentiation, derivates, limits on a based level. I would like to understand on deep level the ...
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Machine Learning book for fundamentals - Simon Haykin vs. Christopher M. Bishop
Since I started studying Machine Learning, I was torn between two books in this area, and I could never decide which one is the best to follow.
The first book is widely used and known: Pattern ...
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Mathematics books for reinforcement learning
This question is not about the math prerequisites of reinforcement learning, but about the textbooks of mathematics that are enough to understand the literature on reinforcement learning.
What are the ...
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Concise and mathematically-oriented book on AI and neural networks suitable as a gift [closed]
I would like to buy a book about AI and neural networks written on accessible level for a 17 years old mathematically very gifted student interested in these topics. The book should contain some ...
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Which formula of p(x, y) to use?
The probability distribution $p(x, y)$ can be calculated in two ways :
$p(x, y) = p(y \mid x) p(x)$
$p(x, y) = p(x \mid y) p(y)$
But according to the book Deep Generative Modeling (page number 3 ...
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Does it make sense to compare images (samples) with words (features)?
Consider the following paragraphs from the introduction of the chapter named Recurrent Neural Networks from the textbook titled Dive into Deep Learning
So far we encountered two types of data: ...
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Textbook for CNN-LSTM networks of predictions of numerical data
I am learning NN algorithms because I'd like to create my own project.
What I found on the internet, is that for my type of project which I have in mind CNN-LSTM neural network would be ideal.
But now ...
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What do we mean by "orderly opinions" in this sentence in the context of Bayes theorem?
In this page, it's written (emphasis mine)
If probabilities are thought to describe orderly opinions, Bayes theorem describes how the opinions should be updated in the light of new information
What ...
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Book/course recommendation on game theory application to multi-agent system (reinforcement learning)
Is there any great game theory book or course that discusses the application of game theory to modern reinforcement learning or multi-agent systems? Or a classic reference book that can help me get a ...
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What is the difference between the US and global edition of the AIMA book by Russell and Norvig?
The book Artificial Intelligence: A Modern Approach by Russell and Norvig has two editions: global and the US. It looks like these two are generally the same, but have some differences in the order of ...
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779
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What are the Calculus books recommended for beginner to advanced researchers in artificial intelligence?
Calculus is a branch of mathematics that primarily deals with the rate of change of outputs of a function w.r.t the inputs.
It contains several concepts including limits, first-order derivatives, ...
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Why does critical points and stationary points are used interchangeably?
Consider the following paragraph from Numerical Computation of the deep learning book.
When $f'(x) = 0$, the derivative provides no information about which
direction to move. Points where $f'(x)$ = 0 ...
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Why not undefined expression is different from numerical underflow?
Consider an architecture or programming language that uses $n$ bits for storing a floating point number in a particular format. Then each and every floating point number it can store should be in a ...
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What exactly is a grid-like topology according to the book Deep Learning?
I am reading this book called "Deep Learning" (by Goodfellow, Bengio and Courville).
On page 326, in the first paragraph, it says:
CNNs, are a specialized kind of neural network for ...
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Is there an entry level textbook on Bayesian Inference that is a nice blend of theory and applications?
I am looking for a textbook that is a nice entry level to Bayesian Inference. I was hoping that there is a nice blend of theory and applications (data sets) on how concepts are applied. Programming ...
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Are the held-out datasets used for testing, validation or both?
I came across a new term "held-out corpora" and I confused regarding its usage in the NLP domain
Consider the following three paragraphs from N-gram Language Models
#1: held-out corpora as a ...
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Example of lemma having multiple boldface forms
Number of lemmas can be used as a rough measure for the number of words in a language. A lemma can have multiple word-form types. It can be understood from the following paragraph taken from p12 of ...
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Why can't recurrent neural network handle large corpus for obtaining embeddings?
In order to learn the embeddings, we need to train a model based on some objective function. The model can be an RNN and the objective function can be the likelihood. We learn the embeddings by ...
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Is there a recent book that covers the theoretical and philosophical aspects of artificial intelligence?
What are some recent books that introduce AI and neural networks while also discussing the related philosophical issues, like epistemology and whether AI is really thinking, etc.?
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What are the 'noisy factors' leading to overfitting?
Consider the following excerpt from section 5.5 Regularization (p. 13) of this chapter Logistic Regression.
There is a problem with learning weights that make the model perfectly match the training ...
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Why do we commonly use the $\log$ to squash frequencies?
Term frequency and inverse document frequency are well-known terms in information retrieval.
I am presenting the definitions for both from p:12,13 of Vector Semantics and Embeddings
On term frequency
...
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Why Word2Vec is called a neural model if no neural network is used in it?
Word2Vec model does not use any neural network. It uses logistic regression only.
Consider the following paragraph from p:18 of Vector Semantics and Embeddings
We’ll see how to do neural ...
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1
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What is the meaning of "continuous" in a continuous bag-of-words model?
The word continuous in mathematics is a property of either a set or a function that says that the underlying object has no discontinuity in the range mentioned. If the object is a set, then $[-1,1]$ ...
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618
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What is the exact difference between distributional semantics and distributed semantics?
While studying word embeddings in natural language processing, I encountered the following statement on page 327 of the textbook Natural Language Processing by Jacob Eisenstein
Distributional ...
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340
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Book(s) for text embedding
Text here refers to either character or word or sentence.
Is there any recent textbook that encompasses from classical methods to the modern techniques for embedding texts?
If a single textbook is ...
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How is the state-value function expressed as a product of sums?
The state-value function for a given policy $\pi$ is given by
$$\begin{align}
V^{\pi}(s) &=E_{\pi}\left\{r_{t+1}+\gamma r_{t+2}+\gamma^{2} r_{t+3}+\cdots \mid s_{t}=s\right\} \\
&=E_{\pi}\...
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What is meant by "real-valued argument" in this context of the convolution operation?
Consider the following statement from Deep Learning book (p. 327, chapter 9: Convolutional Networks)
In its most general form, convolution is an operation on two functions
of a real-valued argument.
...
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What does "statistical efficiency" mean in this context?
Consider the following statement(s) from Deep Learning book (p. 333, chapter 9: Convolutional Networks) by Ian Goodfellow et al.
Convolution is thus dramatically more efficient than dense matrix
...
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Is there any comprehensive book that reviews topics in the area of brain-inspired computing?
I am looking to write my master's thesis next year about brain-inspired computing. Hence, I am looking to get a good overview of this domain.
Do you know of any comprehensive book that reviews topics ...
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In Probabilistic Graphical Model (written by Daphne Koller), what's the meaning of "parameter" in representation of the distribution?
I just started to read the PGM book written by Daphne Koller.
In the chapter of Bayesian Network Representation(Chapter 3), there are some descriptions about the standard parameterization of the joint ...
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What are other examples of theoretical machine learning books?
I am looking for a book about machine learning that would suit my physics background. I am more or less familiar with classical and complex analysis, theory of probability, сcalculus of variations, ...
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What introductory books to reinforcement learning do you know, and how do they approach this topic?
Currently, I'm only going through these two books
Reinforcement Learning: An Introduction, by Sutton and Barto: RL explained on an engineering level (mathematical, but readable for a non-...
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Are there any good resources (preferably books) about techniques used for entity extraction?
Given some natural language sentences like
I would like to talk to Mr. Smith
I would like to extract entities, like the person "Smith".
I know that frameworks, which are capable of doing ...
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What are some resources on computational learning theory?
Pretty soon I will be finishing up Understanding Machine Learning: From Theory to Algorithms by Shai Ben-David and Shai Shalev-Shwartz. I absolutely love the subject and want to learn more, the only ...
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What are examples of books or papers on the details of convolutional neural networks?
I'm studying a master's degree and my final work is going to be about the convolutional neural network.
I read a lot of books and I did Convolutional Network Standford's course, but I need more.
Are ...
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Which books or papers clearly explain the relation between Ising models and deep neural networks?
I am looking for a book or paper which clearly explains the relationship between Ising models and deep neural networks.
Can anyone provide any references?
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Which part of "Perceptrons: An Introduction to Computational Geometry" tells that a perceptron cannot solve the XOR problem?
In the book "Perceptrons: An Introduction to Computational Geometry" by Minsky and Papert (1969), which part of this book tells that a single-layer perceptron could not solve the XOR problem?
I have ...
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Which linear algebra book should I read to understand vectorized operations?
I am reading Goodfellow's book about neural networks, but I am stuck in the mathematical calculus of the back-propagation algorithm. I understood the principle, and some Youtube videos explaining this ...
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What are some books/papers that deal with fundamental and philosophical issues of ML and relate it to the global discourse of AIs?
In my experience, most of the time, when people talk about AI nowadays they mostly mean machine learning. Despite this, ML is usually seen as a mere technique to build high-performance software.
I ...
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What does the notation $[m]=\{1, \ldots, m\}$ mean in the equation of the empirical error?
The empirical error equation given in the book Understanding Machine Learning: From Theory to Algorithms is
My intuition for this equation is: total wrong predictions divided by the total number of ...
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What are examples of good reference books on unsupervised learning?
I am looking for good introductory and advanced books on unsupervised learning. I have already read books like Probabilistic Graphical Models from D. Kholler and Pattern Recognition and Machine ...
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What are some books or state of the art papers about the development of a strong-AI?
I am looking for books or to state of the art papers about current the development trends for a strong-AI.
Please, do not include opinions about the books, just refer the book with a brief ...
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What are the mathematical prerequisites for an AI researcher?
What are the mathematical prerequisites for understanding the core part of various algorithms involved in artificial intelligence and developing one's own algorithms?
Please, refer to some specific ...
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Is Christopher Bishop's "Pattern Recognition and Machine Learning" out of date in 2018?
I recently came across a reference to a book that was highly regarded: "Pattern Recognition and Machine Learning" by Christopher Bishop.
I am a beginner working my way through some machine ...
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What are some alternatives to the book "Artificial Intelligence: A Modern Approach"?
There are two textbooks that I most love and am most afraid of in the world: Introduction to Algorithms by Cormen et al. and Artificial Intelligence: A Modern Approach by Norvig et al. I have started ...
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What are some intermediate or advanced books on neural networks?
Is anyone able to recommend some resources (preferably books) on the topic of neural networks that goes beyond that of introductory reading?
I'm still relatively new to the subject, however, I have ...
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Which rules should I define for the predicate "not_to_far" of the exercise 1.1 of the book "Simply Logical: Intelligent Reasoning by Example"?
I've just started reading a book about AI. The book is Simply Logical: Intelligent Reasoning by Example. There is a very basic exercise (on page 19 of the pdf, page 5 of the book), but I can't figure ...
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