Speaker "SK Reddy" Details Back



Natural Language Processing Workshop
Workshop: Frameworks galore..Which one to pick


NLP Workshop:
Machine learning is gaining tremendous traction. Stock market prediction, analyzing weather data, gaining better sense of sales data, etc are some of the problems that are being solved in machine learning. Here one uses numbers to train and predict a machine learning model.
But there is a huge focus on processing text to extract wisdom. Natural Language Processing (NLP) has made tremendous progress in processing text in the recent past. Question-answering, topic modeling, summarization, sentiment analyses, spam email detection, auto-response to emails, medical diagnosis, are few of the many problems that are being solved using NLP.

This workshop will try to achieve the following objectives: 
(i) Novices: Introduce NLP   
(ii) Intermediate: enhance the insights into NLP and sub topics
(iii) Experts: sharpen the skills in sub topics  
NLP Workshop:
(1) ML and NLP fundamentals
(2) Neural networks, RNN, LSTMs, GRUs.
(3) Question answering, topic modeling, sentiment analyses, summarization and language translation
(4) NLP frameworks. Intro to Tensorflow, Keras, Caffe
(5) Word embeddings, word2vec, glove, language modeling
(3) Define an NLP problem statement 
(4) Implement the model; download the dataset, train the model and test the model
(5) Define next steps to continue NLP learning

Workshop: Frameworks galore..Which one to pick
There are so many open sourced and other AI frameworks available. Tensorflow, Caffe, Torch, PyTorch, Caffe2, MXNet, Spark MLlib, CNTK, H2O, DMTK, Theano, Scikit, etc. Each of these frameworks have certain strengths. Some of tem work with certain languages and some do not. In addition, some of these frameworks have been used by popular authors of tech papers and have open sourced the code. Some of these are free and some are not. Some frameworks are better to use for image processing use cases and some are not.

Organizations and individuals are confused on which framework is best for what type of use case and which framework is better. I would like to discuss and compare various frameworks. Also I would like to pick a few use cases that will be implemented as a hands-on exercise using a few frameworks.

This is for those executives that are looking to decide on what framework to use. This workshop is also for those implementers who want to implement a couple of use cases on a couple of different frameworks 


1. Discuss a lot of frameworks: their use, peculiarities, capabilities, when to use and when not to use, which one is better than the other, which language works better with which framework, which framework works better with GPUs / large data, etc. Bring any questions you have and we could discuss.

2. Implement a couple of use cases in number crunching, NLP and Image processing areas and get a feel of how the framework responds.


SK is the Chief Product officer AI in Hexagon ( He is also an AI and ML expert and a successful twice startup entrepreneur. He is a frequent speaker in conferences and an AI blogger

His focus is to develop ML models to combine image and text processing. Also he has developed solutions in text summarization, question-answering and text mining.

Additional information about Mr Reddy could be found at