- Deep Learning with Keras Tutorial
- Deep Learning with Keras - Home
- Deep Learning with Keras - Introduction
- Deep Learning
- Setting up Project
- Importing Libraries
- Creating Deep Learning Model
- Compiling the Model
- Preparing Data
- Training the Model
- Evaluating Model Performance
- Predicting on Test Data
- Saving Model
- Loading Model for Predictions
- Conclusion
- Deep Learning with Keras Resources
- Deep Learning with Keras - Quick Guide
- Deep Learning with Keras - Useful Resources
- Deep Learning with Keras - Discussion
Deep Learning with Keras - Introduction
Deep Learning has become a buzzword in recent 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 popular due to its supremacy in predictions as compared to traditional ML techniques.
Deep Learning essentially means training an Artificial Neural Network (ANN) with a huge 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 used 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. Using 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 tutorial, you will learn the use of Keras in building deep neural networks. We shall look at the practical examples for teaching. 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 diagram, 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 tutorial will get you quickly started on deep learning.
So keep reading!