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Python Deep Learning Cookbook
Python Deep Learning Cookbook

Python Deep Learning Cookbook: Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python

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Python Deep Learning Cookbook

Introduction


The focus of this chapter is to provide solutions to common implementation problems for FNN and other network topologies. The techniques discussed in this chapter also apply to the following chapters.

FNNs are networks where the information only moves in one direction and does not cycle (as we will see in Chapter 4, Recurrent Neural Networks). FNNs are mainly used for supervised learning where the data is not sequential or time-dependent, for example for general classification and regression tasks. We will start by introducing a perceptron and we will show how to implement a perceptron with NumPy. A perceptron demonstrates the mechanics of a single unit. Next, we will increase the complexity by increasing the number of units and introduce single-layer and multi-layer neural networks. The high number of units, in combination with a high number of layers, gives the depth of the architecture and is responsible for the name deep learning. 

Understanding the perceptron


First, we need to understand the basics of neural networks. A neural consists of one or multiple layers of neurons, named after the neurons in human brains. We will demonstrate the mechanics of a single neuron by implementing a perceptron. In a perceptron, a single unit (neuron) performs all the computations. Later, we will scale the number of units to create deep neural networks:

Figure 2.1: Perceptron

A can have multiple inputs. On these inputs, the unit performs some computations and outputs a single value, for example a binary value to classify two classes. The computations performed by the unit are a simple matrix multiplication of the input and the weights. The resulting values are summed up and a bias is added:

These computations can easily be scaled to high dimensional input. An activation function (φ) determines the final output of the in the forward pass:

The weights and bias are initialized. After each epoch (iteration over the training data), the...

Implementing a single-layer neural network


Now we can move on to neural networks. We will start by the simplest form of a neural network: a single-layer neural network. The difference from a perceptron is that the computations are done by multiple units (neurons), hence a network. As you may expect, adding more units will increase the number of problems that can be solved. The units perform their computations separately and are in a layer; we call this layer the hidden layer. Therefore, we call the units in this layer the hidden units. For now, we will only consider a single hidden layer. The output layer performs as a perceptron. This time, as input we have the hidden units in the hidden layer instead of the input variables:

Figure 2.4: Single-layer neural network with two input variables, n hidden units, and a single output unit

In our implementation of the perceptron, we've used a unit step function to determine the class. In the next recipe, we will use a non-linear activation function...

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Key benefits

  • - Practical recipes on training different neural network models and tuning them for optimal performance
  • -Use Python frameworks like TensorFlow, Caffe, Keras, Theano for Natural Language Processing, Computer Vision, and more
  • -A hands-on guide covering the common as well as the not so common problems in deep learning using Python

Description

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.

Who is this book for?

This book is intended for machine learning professionals who are looking to use deep learning algorithms to create real-world applications using Python. Thorough understanding of the machine learning concepts and Python libraries such as NumPy, SciPy and scikit-learn is expected. Additionally, basic knowledge in linear algebra and calculus is desired.

What you will learn

  • • Implement different neural network models in Python
  • • Select the best Python framework for deep learning such as PyTorch, Tensorflow, MXNet and Keras
  • • Apply tips and tricks related to neural networks internals, to boost learning performances
  • • Consolidate machine learning principles and apply them in the deep learning field
  • • Reuse and adapt Python code snippets to everyday problems
  • • Evaluate the cost/benefits and performance implication of each discussed solution

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 27, 2017
Length: 330 pages
Edition : 1st
Language : English
ISBN-13 : 9781787125193
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Product Details

Publication date : Oct 27, 2017
Length: 330 pages
Edition : 1st
Language : English
ISBN-13 : 9781787125193
Vendor :
Google
Category :
Languages :
Concepts :
Tools :

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Table of Contents

14 Chapters
Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks Chevron down icon Chevron up icon
Feed-Forward Neural Networks Chevron down icon Chevron up icon
Convolutional Neural Networks Chevron down icon Chevron up icon
Recurrent Neural Networks Chevron down icon Chevron up icon
Reinforcement Learning Chevron down icon Chevron up icon
Generative Adversarial Networks Chevron down icon Chevron up icon
Computer Vision Chevron down icon Chevron up icon
Natural Language Processing Chevron down icon Chevron up icon
Speech Recognition and Video Analysis Chevron down icon Chevron up icon
Time Series and Structured Data Chevron down icon Chevron up icon
Game Playing Agents and Robotics Chevron down icon Chevron up icon
Hyperparameter Selection, Tuning, and Neural Network Learning Chevron down icon Chevron up icon
Network Internals Chevron down icon Chevron up icon
Pretrained Models Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(3 Ratings)
5 star 66.7%
4 star 0%
3 star 0%
2 star 0%
1 star 33.3%
Gert Dec 30, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
positive points:• The book builds on simple recipes toward more complex recipes.• Great book if you want to learn by example!• A lot of useful code is included in the book that you can reuse for different projects.• What I like is that the author focusses on experimentation and doesn’t assume one method is better over another.negative points:• Sometimes a bit more explanation why some of the choices have been made would be good.• It would be better if the images are in colour (especially some charts).
Amazon Verified review Amazon
newby19 Nov 19, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I think this book is a great way to get started using deep learning in a hands-on way. Especially for someone who's relatively new to deep learning and wants to experiment and work on different projects.Each recipe in the book is broken down to the different steps to take. Each step is described shortly and to the point. I used the recipes from the Computer Vision chapter right away to create my own image classification project.
Amazon Verified review Amazon
Some Python Guy Dec 19, 2017
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Found the code files on git but for example the wav/video files from chapter 9 are missing. Likely other source data files are missing. Please provide all relevant input files to run the samples.
Amazon Verified review Amazon
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