big data in healthcare Muktabh Mayank - Making a contextual recommendation engine

Making a Recommendation Engine at ParallelDots
a. Why normal full-text search will not work: The problem of incorrect tagging and slow search queries.

b. ParallelDots’ MVP with Topic Models: Issues with accuracy and scaling.

c. Decision to use Deep Learning and aims of the new architecture (Not enough funds for distributed system, search related posts from millions of documents in reasonable time)

Basics of Deep Learning
a. Deep Neural Networks

b. Types of Deep Neural Networks. Convolutional, DBNs, Recurrent and Recursive. How do they differ in structure, types of neurons and training.

c. Backpropogation and its variants

d. Features of various Deep Learning libraries in Python.

Deep Learning in NLP
a. Solving problem of high-dimensionality using word embeddings.

b. Common approaches to word embedding.

c. Modelling language as a series of characters using Recurrent Neural Networks .

c. Models we use : Named Entity Recognition with Neural Nets

d. Models we use: Combining word embeddings using heuristics and recursive neural networks.

Search Engine
a. Using Search Data Structures to convert search related posts operations from O(n) to O(log(n))

b. Space Partitioning Trees : Search for nearest Neighbours. Examples of such trees: KD-Tree / Ball Tree / VP Tree

c. Why we chose VP Tree ? What libraries to use to code up in Python ?

d. Parallelization. Data Parallel Python’s multiprocessing parallelization not the best, working towards a shared memory parallel version.

Scaling up system
a. Hacks to scale up recommendations.

b. Using golang’s channels to unique requests.

big data in healthcare Muktabh Mayank - Making a contextual recommendation engine
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