Deep learning is the new big trend in machine learning. It had many recent successes in computer vision, automatic speech recognition and natural language processing.
The goal of this blog post is to give you a hands-on introduction to deep learning. To do this, we will build a Cat/Dog image classifier using a deep learning algorithm called convolutional neural network (CNN) and a Kaggle dataset.
This post is divided into 2 main parts. The first part covers some core concepts behind deep learning, while the second part is structured in a hands-on tutorial format.
In the first part of the hands-on tutorial (section 4), we will build a Cat/Dog image classifier using a convolutional neural network from scratch. In the second part of the tutorial (section 5), we will cover an advanced technique for training convolutional neural networks called transfer learning. We will use some Python code and a popular open source deep learning framework called Caffe to build the classifier. Our classifier will be able to achieve a classification accuracy of 97%.
By the end of this post, you will understand how convolutional neural networks work, and you will get familiar with the steps and the code for building these networks.
The source code for this tutorial can be found in this github repository.