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.
Table of Contents (10 chapters)

Neural Networks

As a developer, you have surely gained an interest in machine learning from looking at all the incredibly amazing applications that you see on your regular devices every day—automatic speech translation, picture style transfer, the ability to generate new pictures from sample ones, and so on. Brace yourself... we are heading directly into the technology that has made all these things possible.

Linear and logistic models, such as the ones we've observed, have certain limitations in terms of the complexity of the training dataset they train a model on, even when they are the basis of very articulated and efficient solutions.

How complex does a model have to be to capture the style of writing of an author, the concept of an image of a cat versus an image of a dog, or the classification of a plant based on visual elements? These things require the summation...