In this chapter, we learned about an important class of machine learning model, namely neural networks, and their Bayesian implementation. These models are inspired by the architecture of the human brain and they continue to be an area of active research and development. We also learned one of the latest advances in neural networks that is called deep learning. It can be used to solve many problems such as computer vision and natural language processing that involves highly cognitive elements. The artificial intelligent systems using deep learning were able to achieve accuracies comparable to human intelligence in tasks such as speech recognition and image classification. With this chapter, we have covered important classes of Bayesian machine learning models. In the next chapter, we will look at a different aspect: large scale machine learning and some of its applications in Bayesian models.
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Dr. Hari M. Koduvely is an experienced data scientist working at the Samsung R&D Institute in Bangalore, India. He has a PhD in statistical physics from the Tata Institute of Fundamental Research, Mumbai, India, and post-doctoral experience from the Weizmann Institute, Israel, and Georgia Tech, USA. Prior to joining Samsung, the author has worked for Amazon and Infosys Technologies, developing machine learning-based applications for their products and platforms. He also has several publications on Bayesian inference and its applications in areas such as recommendation systems and predictive health monitoring. His current interest is in developing large-scale machine learning methods, particularly for natural language understanding.
Read more about Hari Manassery Koduvely
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Dr. Hari M. Koduvely is an experienced data scientist working at the Samsung R&D Institute in Bangalore, India. He has a PhD in statistical physics from the Tata Institute of Fundamental Research, Mumbai, India, and post-doctoral experience from the Weizmann Institute, Israel, and Georgia Tech, USA. Prior to joining Samsung, the author has worked for Amazon and Infosys Technologies, developing machine learning-based applications for their products and platforms. He also has several publications on Bayesian inference and its applications in areas such as recommendation systems and predictive health monitoring. His current interest is in developing large-scale machine learning methods, particularly for natural language understanding.
Read more about Hari Manassery Koduvely