"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

July 31, 2016

Fifth Elephant Day #2

Fifth Elephant Day #2 - Part I

Session #1 - Content Marketing
  • Distribute relevant consistent content. Traditional vs Content Marketing
Challenges
  • Delivering content with speed. Channel proliferation (mobile, computers, tablets)
  • Intersection of Brands, Trends, Community Interests (Social media post and metrics)
  • Data from social media pages, online aggregators



Technical Details
  • Computation of term frequency, inverse document frequency
  • Using Solr, Lucene for Indexes
  • Cosine Similarity
  • Greedy Algorithm
Session #2 - Reasoning
  • Prediction vs Reasoning problem
  • Prediction Problems Evolution 
  • At Advanced level Deep Learning, XGBoost, Graphical models
When Apply prediction ?
Features as input -> Prediction performed (Independent, stateless)

Reasoning - Sequential, Stateful Exploration
Reasoning Problems - Diagnosis, routes, games, crossing roads

Flavours of Reasoning
  • Algorithmic (Search)
  • Logical reasoning
  • Bayesian probabilistic reasoning
  • Markovnian reasoning
Knowledge, Learning the process of reasoning, Knowledge graphs were should in implementation of reasoning
{subject, predicate, object}















Session #3 - Continuous online learning
  • 70% noise in C2B communication
  • 100% noise in B2C communication
  • Zipfian
Technicalities
  • Apriori - Market Basket Analysis
  • XGBoost - Alternative to DL
  • Bias - Variance Tradeoff
  • Spectral Clustering






Bird of Feathers Session
  • Google Deepmind (Used for Air conditioning)
  • Bayesian Probabilistic Learning
  • Deep Learning - Build Hierarchy of features (OCR type of problems)
  • Traditional Neural Network (Fully Connected, lot of degree of freedom)
  • Structural causality (Subsystem appears before, Domain knowledge)
  • Temporal causality - This and then that happened
  • CNN - learning weights
  • Spectral clustering
  • PCA (reduce denser to smaller)
  • Deep Learning - Hidden layers obtained through coarse grained process
Deep Learning workshop Notes
  • Neural Networks
  • Multiple Layers
  • Lots of data
People Involved - Hinton, Andrew Ng, Bengio, Lecuss

Deep Learning now
  • Speech recognition
  • Google Deep Models on Phone
  • Google street view (House numbers)
  • Imagenet
  • Captioning images
  • Reinforcement learning
Neural Networks
  • Simple mathematical units combine into complex functions
  • X-> input, W-> weights, Non linear function of output
Multiple Layers
  • Multiple hidden layers between input and output
  • Training hidden layers is challenge
Gradient Descent
  • Define loss function
  • Minimize by moving along gradient
Backpropagation
  • Move Errors back through the network
  • Chain rule conception
Tools
  • Cafee - Configuration file
  • Torch - Describe network in lue
  • Theano - Describes computation, writes cuda code, runs and gives results
CNN
  • Used for images
  • Images are organized
  • Apply Convolutional filter
  • For Deep Learning GPU is important
Imagenet Competition
  • Convolution (Have all nice features retain them)
  • Pooling (Shrink image)
  • Softmax
  • Other
Simplest RNN - Gradient Descent problem
LSTM (Long Short Term memory)
Interword relationships from corpus (word2vec)

Happy Learning!!!
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