Pramod Singh is a senior machine learning engineer at Walmart Labs. He has over 10 years of hands-on experience in machine learning, deep learning, AI, data engineering, designing algorithms, and application development.He’s the author of three books Machine Learning with PySpark, Learn PySpark, and Learn TensorFlow 2.0. He’s also a regular speaker at major conferences such as the O’Reilly Strata Data and AI Conferences. Pramod holds Masters degree from Symbiosis and Business Analytics certification from IIM-Calcutta.He is also a visiting faculty to teach and mentor on ML & AI in different education institutes.
Text or Image classification done using deep neural networks presents us with a unique way to identify each trained image/word via something known as 'Embedding'. Embedding refers to fix sized vectors which are learnt during the training process of a neural network but it is very difficult to make sense of these random values. However, these embeddings are very powerful and carry a lot of hidden information about the object that it represents. This session will unlock some of the different ways in which embeddings can be visualised and be comprehended from the aspect of performance of the model as well as underlying signal in these embeddings to represent the actual object (text/image). For example, the customer journey online can be translated into these embeddings and can be used to find the real intent and to differentiate between the potentially interested visitor vs a casual visitor .Once decoded, these embeddings become more friendly and can be plugged in at a number of places such as comparison, classification and retraining of the model itself. This session will cover how to unlock the real power of these embeddings using different tools.