Book Image

Machine Learning for Developers

By : Rodolfo Bonnin, Md Mahmudul Hasan
Book Image

Machine Learning for Developers

By: Rodolfo Bonnin, Md Mahmudul Hasan

Overview of this book

Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This book is a systematic guide teaching you how to implement various Machine Learning techniques and their day-to-day application and development. You will start with the very basics of data and mathematical models in easy-to-follow language that you are familiar with; you will feel at home while implementing the examples. The book will introduce you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement Regression, Clustering, classification, Neural networks, and more with fun examples. As you get to grips with the techniques, you’ll learn to implement those concepts to solve real-world scenarios for ML applications such as image analysis, Natural Language processing, and anomaly detections of time series data. By the end of the book, you will have learned various ML techniques to develop more efficient and intelligent applications.

Related Content you might be interested in

Current Title:

Small book image
Machine Learning for Developers
Rodolfo Bonnin | Md Mahmudul Hasan
Oct, 2017 | 270 pages
Small book image
TensorFlow 1.x Deep Learning Cookbook
Amita Kapoor | Antonio Gulli
Dec, 2017 | 536 pages
Small book image
Deep Learning with Keras
Sujit Pal | Antonio Gulli
Apr, 2017 | 318 pages
Small book image
Hands-On Deep Learning Architectures with Python
Yuxi Hayden Liu | Saransh Mehta
Apr, 2019 | 316 pages
Table of Contents (10 chapters)
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
Introduction - Machine Learning and Statistical Science
Introduction - Machine Learning and Statistical Science
Machine learning in the bigger picture
Tools of the trade–programming language and libraries
Basic mathematical concepts
Summary
2
The Learning Process
The Learning Process
Understanding the problem
Dataset definition and retrieval
Feature engineering
Dataset preprocessing
Model definition
Loss function definition
Model fitting and evaluation
Model implementation and results interpretation
Summary
References
3
Clustering
Clustering
Grouping as a human activity
Automating the clustering process
Finding a common center - K-means
Nearest neighbors
K-NN sample implementation
Summary
References
4
Linear and Logistic Regression
Linear and Logistic Regression
Regression analysis
Linear regression
Data exploration and linear regression in practice
Logistic regression
Summary
References
5
Neural Networks
Neural Networks
History of neural models
Implementing a simple function with a single-layer perceptron
Summary
References
6
Convolutional Neural Networks
Convolutional Neural Networks
Origin of convolutional neural networks
Deep neural networks
Deploying a deep neural network with Keras
Exploring a convolutional model with Quiver
References
Summary
7
Recurrent Neural Networks
Recurrent Neural Networks
Solving problems with order — RNNs
LSTM
Univariate time series prediction with energy consumption data
Summary
References
8
Recent Models and Developments
Recent Models and Developments
GANs
Reinforcement learning
Basic RL techniques: Q-learning
References
Summary
9
Software Installation and Configuration
Software Installation and Configuration
Linux installation
macOS X environment installation
Windows installation
Summary

Origin of convolutional neural networks

Convolutional neural networks (CNNs) have a remote origin. They developed while multi-layer perceptrons were perfected, and the first concrete example is the neocognitron.

The neocognitron is a hierarchical, multilayered Artificial Neural Network (ANN), and was introduced in a 1980 paper by Prof. Fukushima and has the following principal features:

  • Self-organizing
  • Tolerant to shifts and deformation in the input

This original idea appeared again in 1986 in the book version of the original backpropagation paper, and was also employed in 1988 for temporal signals in speech recognition.

The design was improved in 1998, with a paper from Ian LeCun, Gradient-Based Learning Aapplied to Document Recognition, presenting the LeNet-5 network, an architecture used to classify handwritten digits. The model showed increased performance compared to other...

Previous Section
End of Section 2
Next Section