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!!!