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.

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

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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.

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