By Nikhil Ketkar

Discover the sensible facets of imposing deep-learning suggestions utilizing the wealthy Python environment. This publication bridges the distance among the educational state of the art and the state-of-the-practice by way of introducing you to deep studying frameworks comparable to Keras, Theano, and Caffe. The practicalities of those frameworks is usually bought by means of practitioners through studying resource code, manuals, and posting questions about group boards, which has a tendency to be a gradual and a painful approach. Deep studying with Python allows you to ramp as much as such functional knowledge in a quick time period and concentration extra at the area, versions, and algorithms.
This ebook in short covers the mathematical necessities and basics of deep studying, making this e-book an excellent place to begin for software program builders who are looking to start in deep studying. a quick survey of deep studying architectures can be included.
Deep studying with Python additionally introduces you to key recommendations of automated differentiation and GPU computation which, whereas no longer crucial to deep studying, are serious in terms of engaging in huge scale experiments. 
What you are going to Learn 
  • Leverage deep studying frameworks in Python particularly, Keras, Theano, and Caffe 
  • Gain the basics of deep studying with mathematical prerequisites 
  • Discover the sensible concerns of huge scale experiments 
  • Take deep studying types to production
Who This e-book Is For

Software builders who are looking to test deep studying as a pragmatic technique to a selected problem. Software builders in an information technological know-how workforce who are looking to take deep studying types built by means of facts scientists to production.

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The squared loss function given by å ( y - yˆ i =1 ) 2 should be used for regression problems. The output layer in this case will have a single unit. Types of Units/Activation Functions/Layers We will now look at a number of Units/Activation Functions/Layers commonly used for Neural Networks. Let’s start by enumerating a few properties of interest for activation functions. 1. In theory, when an activation function is non-linear, a two-layer Neural Network can approximate any function (given a sufficient number of units in the hidden layer).

This loss function should typically be used when the Neural Network is designed to predict the probability of the outcome. In such cases, the output layer has a single unit with a suitable sigmoid as the activation function. 2. The Cross entropy function given by the expression n -å yi log f ( xi ,q ) i =1 is the recommended loss function for multi-classification. This loss function should typically be used with the Neural Network and is designed to predict the probability of the outcomes of each of the classes.

Let us again use the idea behind Maximum Likelihood, which is to find a θ that maximizes P ( D | q ) . Assuming a Multinomial distribution and given that each of the examples {(x1, y1), (x2, y2), … (xn, yn)} are independent, we have the following expression: P ( D |q ) = n! n1 ! × n2 ! ××× nk ! n Õ f ( x ,q ) i =1 yi i 23 Chapter 3 ■ Feed Forward Neural Networks We can take a logarithm operation on both sides to arrive at the following: n log P ( D | q ) = log n ! - log n1 ! × n2 ! ××× nk ! + log Õ f ( xi , q ) yi i =1 This can be simplified to the following: n log P ( D | q ) = log n !

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