Artificial Intelligence for Humans, Volume 3: Deep Learning by Jeff Heaton

By Jeff Heaton

Neural networks were a mainstay of man-made intelligence on account that its earliest days. Now, fascinating new applied sciences equivalent to deep studying and convolution are taking neural networks in daring new instructions. during this e-book, we'll exhibit the neural networks in numerous real-world projects resembling photo acceptance and knowledge technology. We learn present neural community applied sciences, together with ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.

Show description

Read or Download Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks PDF

Similar intelligence & semantics books

Programming the Semantic Web

I ended interpreting via bankruptcy 6 to date. .. my total impact is, average, yet believe inadequate.

There are a few dialogue i love: for instance, the straightforward triple shop implementation is illustrative, proposal clever. even if, the dialogue on RDF serialization structure, the instance given, ontology, it simply feels the phrases are challenging to swallow. you'll imagine a publication approximately semantic must have very exact common sense and clarification might be crystal transparent. despite the fact that, as I learn it, I frequently get the texture anything . .. "this can be this tough to provide an explanation for, what's he speaking approximately the following? " . .. might be i'm waiting for an excessive amount of.

Symbolic dynamics. One-sided, two-sided and countable state Markov shifts

This can be a thorough advent to the dynamics of one-sided and two-sided Markov shifts on a finite alphabet and to the elemental houses of Markov shifts on a countable alphabet. those are the symbolic dynamical structures outlined through a finite transition rule. the fundamental houses of those platforms are demonstrated utilizing ordinary tools.

Machine Learning: An Artificial Intelligence Approach

The power to benefit is among the such a lot basic attributes of clever habit. hence, development within the conception and machine modeling of research­ ing approaches is of serious value to fields considering figuring out in­ telligence. Such fields comprise cognitive technology, synthetic intelligence, infor­ mation technology, development reputation, psychology, schooling, epistemology, philosophy, and comparable disciplines.

Principles of Noology: Toward a Theory and Science of Intelligence

The assumption of this bookis toestablish a brand new clinical self-discipline, “noology,” below which a suite of basic rules are proposed for the characterization of either clearly happening and synthetic clever structures. The technique followed in rules of Noology for the characterization of clever platforms, or “noological systems,” is a computational one, very similar to that of AI.

Extra info for Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks

Sample text

4 that designates the sigmoid activation function. 11. 5. 5 when x is near 0. Because all the curves merge together at the top right or bottom left, it is not a complete shift. In a complete network, the output from many different neurons will combine to produce complex output patterns. If you want a house that has a nice view or a large backyard, then only one needs to be present. You can express this idea in the following way: ([nice view] AND [large yard]) OR ((NOT [large yard]) and [park]) You can express the previous statement with the following logical operators: In the above statement, the OR looks like a letter “v,” the AND looks like an upside down “v,” and the NOT looks like half of a box.

3 that is fully connected and has an additional layer. Most networks will have between zero and two hidden layers. Unless you have implemented deep learning strategies, networks with more than two hidden layers are rare. This type of neural network is called a feedforward neural network. Later in this book, we will see recurrent neural networks that form inverted loops among the neurons. Types of Neurons In the last section, we briefly introduced the idea that different types of neurons exist.

Furthermore, the size of the input and output vectors for the neural network will be the same if the neural network has neurons that are both input and output. Hidden neurons are often grouped into fully connected hidden layers. In other words, this network should be able to learn to produce (or approximate) any output from any input as long as it has enough hidden neurons in a single layer. Although a single-hidden-layer neural network can theoretically learn anything, deep learning facilitates a more complex representation of patterns in the data.

Download PDF sample

Rated 4.95 of 5 – based on 24 votes