Deep Learning has become a buzzword in now a days in the field of Artificial Intelligence (AI). For many years, we used Machine Learning (ML) for imparting intelligence to machines. In recent days, deep learning has become more famous due to its supremacy in predictions as compared to traditional ML techniques.
Deep Learning essentially means training an Artificial Neural Network (ANN) with a large amount of data. In deep learning, the network learns by itself and thus requires humongous data for learning. While traditional machine learning is essentially a set of algorithms that parse data and learn from it. They then utilized this learning for making intelligent decisions.
Now, coming to Keras, it is a high-level neural networks API that runs on top of TensorFlow - an end-to-end open source machine learning platform. Utilizing Keras, you easily define complex ANN architectures to experiment on your big data. Keras also supports GPU, which becomes essential for processing huge amount of data and developing machine learning models.
In this study notes, you will learn the use of Keras in building deep neural networks. We shall look at the practical examples for learning. The problem at hand is recognizing handwritten digits using a neural network that is trained with deep learning.
Just to get you more excited in deep learning, below is a screenshot of Google trends on deep learning here −
As you can see from the screenshot, the interest in deep learning is steadily growing over the last several years. There are many areas such as computer vision, natural language processing, speech recognition, bioinformatics, drug design, and so on, where the deep learning has been successfully applied. This study notes will get you quickly started on deep learning.
So keep reading and happy reading!