Book Image

Machine Learning for Developers

By : Rodolfo Bonnin
Book Image

Machine Learning for Developers

By: Rodolfo Bonnin

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.
Table of Contents (10 chapters)

Convolutional Neural Networks

Well, now things are getting fun! Our models can now learn more complex functions, and we are now ready for a wonderful tour around the more contemporary and surprisingly effective models

After piling layers of neurons became the most popular solution to improving models, new ideas for richer nodes appeared, starting with models based on human vision. They started as little more than research themes, and after the image datasets and more processing power became available, they allowed researchers to reach almost human accuracy in classification challenges, and we are now ready to leverage that power in our projects.

The topics we will cover in this chapter are as follows:

  • Origins of convolutional neural networks
  • Simple implementation of discrete convolution
  • Other operation types: pooling, dropout
  • Transfer learning