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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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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1answer
149 views

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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33 views

What are the Calculus books recommended for begineer to advanced researchers in artificial intelligence?

Calculus is a branch of mathematics that 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, higher-order ...
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5 views

How much depth is recommended to study constrained optimization for deep learning?

I am studying the chapter named Numerical Computation from the deep learning textbook In the chapter, there is a section named Constrained Optimization. The authors recommended to read the portion of ...
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2answers
80 views

Why does critical points and stationary points are used interchangeably?

Consider the following paragraph form Numerical Computation of deep learning book. When $f'(x) = 0$, the derivative provides no information about which direction to move. Points where $f'(x)$ = 0 are ...
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24 views

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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1answer
31 views

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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1answer
37 views

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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11 views

How does the bigram terms are contributing to sophisticated version of linear interpolation?

While studying about linear interpolation technique in natural language processing to deal with less frequent $n-$gram. I came across a sophisticated version of linear interpolation. The simple and ...
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2answers
48 views

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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23 views

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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25 views

How to perform negative sampling in Word2Vec?

Word2Vec is an algorithm that generates word embeddings using logistic regression. Positive and negative examples are needed for logistic regression. Word2Vec uses the neighboring words that are ...
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22 views

Derivation of an probability expansion used in Word2Vec classifier model

We are using the following notations, for this question, to calculate the probability values \begin{array}{|c|c|} \hline \text{$w$} & \text{target word embedding vector} \\ \hline \text{$c$} &...
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1answer
39 views

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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2answers
79 views

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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49 views

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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1answer
40 views

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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2answers
51 views

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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2answers
81 views

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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1answer
54 views

Is existence and uniqueness of state-value function at $\gamma < 1$ theoretical?

Consider the following statement from 4.1 Policy Evaluation of the first edition of Sutton and Barto's book. The existence and uniqueness of $V^{\pi}$ are guaranteed as long as either $\gamma < 1$...
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1answer
162 views

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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33 views

suitable reference for reinforcement learning for beginners [duplicate]

I am looking for a reference book on RL for first time learners, one that is a gentle introduction and not as wordy as Sutton/Barto. I am interested in something similar to Georgia Institute of ...
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1answer
27 views

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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1answer
239 views

What does "statistical efficiency" mean in this context?

Consider the following statement(s) from Deep Learning book (p. 333, chapter 9: Convolutional Networks) Convolution is thus dramatically more efficient than dense matrix multiplication in terms of ...
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1answer
55 views

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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22 views

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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1k views

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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215 views

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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27 views

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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1answer
766 views

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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4answers
103 views

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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1answer
98 views

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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92 views

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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2answers
95 views

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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1answer
38 views

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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0answers
62 views

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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2answers
253 views

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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3answers
1k views

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 some specific books....
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2answers
2k views

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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2answers
296 views

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 ...