Day #17 - Python Basics

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Neural Networks Basics

Notes from Session

• Neurons - Synapses. Model brain at high level
• Machine Learning  - Algorithms for classification and prediction
• Mimic brain structure in technology
• Recommender engines use neural networks
• With more data we can increase accuracy of models
• Linear Regression, y = mx + b. Fit data set with little error possible.

Neural Network

• Equation starts from neuron
• Multiply weights to inputs (Weights are coefficients)
• Apply activation function (Depends on problem being solved)

Basic Structure

• Input Layer
• Hidden Layer (Multiple hidden layers) - Computation done @ hidden layer
• Output Layer
• Supervised learning (Train & Test)
• Loss function determines how error looks like
• Deep Learning - Automatic Feature Detection

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Basics - SUPPORT VECTOR MACHINES

Good Reading from link

Key Notes

• Allow non-linear decision boundaries
• SVM - Out of box supervised learning technique
• Feature Space - Finite dimensional vector space
• Each dimension represents feature
• Goal of SVN - Train a model that assigns unseen objects into particular category
• Creates linear partition of feature space
• Based on features it places above or below separation linear
• No stochastic element involved (No involvement of any previous state status)
• support vector classifiers or soft margin classifiers - allows some observations to be on in-correct side of hyperplane allowing soft margin

Advantage

• High Dimensionality, Memory Efficiency, Versatility

Disadvantages

• Non probabilistic

More Reads

Guide to Machine Learning

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Day #16 - Python Basics

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Probability Tips

• Discrete random variables are things we count
• A discrete variable is a variable which can only take a countable number of values
• Probability mass function (pmf) is a function that gives the probability that a discrete random variable is exactly equal to some value.
• Continuous random variables are things we measure
• A continuous random variable is a random variable where the data can take infinitely many values.
• Probability density function (PDF), or density of a continuous random variable, is a function that describes the relative likelihood for this random variable to take on a given value
• Bernoulli process is a finite or infinite sequence of binary random variables
• Markov Chain - stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event

Let's Continue Learning!!!

Day #15 - Data Science - Maths Basics

Day #15 - Mathematics Basics

Sets Basics

• Cardinality - Number of distinct elements in Set (For a Finite Set)
• For Real numbers cardinality infinite

Rational Numbers - Made of Ratio of two numbers
Fibonacci series was introduced in 1201 - Amazing :)

Functions

• Represents relationship between mathematical variables
• Spread of all possible output is called range
• Function that maps from A to B. A is referred as (Domain), B is referred as co-domain

Matrix

• Rows and columns define matrix 
• 2D array of numbers 
• Eigen Values - Scalars, Eigen Vector - Vectors special set of values associated with Matrix M
• Eigen Vectors - Those directions remain unchanged by action of matrix M
• Trace - Sum of diagonal elements
• Rank of Matrix - Number of linearly independent vectors

Determinant

• Can be computed only for square matrix

Vector

• Vectors have magnitude, length and direction
•  Magnitude and cost of angle will give you direction
• Vector product non-commutative
•  Dot product commutative
•  Vector is linearly independent if none of vectors can be written as sum of multiple of other vectors

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Day #14 - R Working Tips

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Day #13 - Maths and Data Science

• Recommender Systems - Pure matrix decomposition problem
• Deep Learning - Matrix Calculus
• Google Search - Page Rank, Social Media Graph Analysis - Eigen Decomposition

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Ensemble

• Combine many predictors and provide weighted average
• Use single kind of learner but multiple instances
• Collection of "Ok" predictors and combine them making them powerful
• Learn Predictors and combine them using another new model 
• One layer of predictors providing features for next layer 

Happy Learning!!!