Day #55 - Markov chains Basics

This post is from my notes. I had bookmarked some interesting answers on understanding Markov chains.

What is a Markov chain?
The simplest example is a drunkard's walk (also called a random walk). The drunk might stumble in any direction but will move only 1 step from the current position.

The ink drop in a glass of water example

How do I explain Markov chains to a 10-year-old?

Imagine a traffic light with three states: yellow, green, red; however, instead of going Green-> Yellow-> Red at "fixed intervals", it would go at any color at any time.(randomly - Imagine a dice with 3 color and you throw it and decide what color it will be next).   Alternatively, imagine you are in certain color, say green. If you don't allow to be in the same color again, flip a coin. If it is heads go to red, and if tails go to yellow.

So to make a "chain" we just feed tomorrows result back into today. Then we can get a long chain like rain rain rain no rain no rain no rain rain rain rain no rain no rain no rain a pattern will emerge that there will be long "chains" of rain or no rain based on how we setup our "chances" or probabilities.

Markov Chain - Khan Academy

• Hidden blue prints of nature / objects around us
• Once you begin each sequence will converge to one ratio
• First order and second order model defined by Claude Shannon

Happy Learning!!!

Day #54 - Fundamental Concepts - Artificial Neural Networks

Referenced Articles - Link

One liner definitions

• Image - Represented as RGB Matrix with Height and width = 3 color channels X Height X width
• Color represented in [0,255] Range
• Kernel - Small Sized matrix consists of real-valued entries
• Activation Region - Region where features specific to kernel detected in input
• Convolution - Calculated by taking dot product of corresponding values of kernel and input matrix certain selected coordinates
• Zero Padding - Systematically adding inputs to adjust size based on requirements
• Hyperparameter- Properties pertaining to the structure of layers and neurons (spatial arrangement, receptive field values called hyperparameters). Main CNN hyperparameters are R - Receptive Field, Zero Padding - P, input volume dimension ( Width X Height X Depth) and Stride Length (S)
• Convolutional Layer - Convolution operation with input filters and identifying the activation region. Convolutiuon Layer output - ReLu (Activation Values)
• ReLu - Rectified Linear Unit Layer. Most commonly deployed activation function for output of CNN neurons. max(0,x)
• ReLu is not differentiable with origin so we use Softplus function ln(1+e^x). Derivative of Softplus function is sigmoid function
• Pooling - Placed after convolution. Objective is downsampling (reduce dimensions)
• Advantages of downsampling
• Decreased size of input for upcoming layers
• Works against overfitting
• Pooling takes sliding window across input transforming into representative values. Transformation performed by taking maximum value in observable window (max pooling)

Happy Learning!!!

Day #53 - Tech Talk - Nikhil Garg - Building a Machine Learning Platform at Quora - MLconf SF 2016

Keynotes from Session

Machine Learning Platform - Collection of systems to sustainable increase the business impact of ML at scale

Build or Buy
1. Degree of Integration with the product. Delegation of components
2. Support for Production Systems (cannot outsource business logic to outside platforms)
3. Blurry line between experimentation & production
4. Leverage Open source in an open manner
5. Commercial platforms are not super valuable - Can often train most models in single multi-core machine
6. Blurry line between ML & Product Development (Inhouse tools for monitoring/training / deploying etc..)
7. ML is Quora's core competency

Machine Learning Models Deployed

Machine Learning Use Cases

Happy Analytics!!!

Machine Learning Quotes

Quote #1 - "In Markov model our assumption is future state depends on only current state, not any other previous states"

Quote #2 - "In Bayes, we have naive assumption the current term is independent of the previous term - Naive assumption"

Happy Learning!!!