75 lines
3.8 KiB
Markdown
75 lines
3.8 KiB
Markdown
1. flowchart application -> ios mobile find symbol through gestures.
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2. matrimony portal profiles -> finding matching profiles
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3. swipe input keyboard for indian languages.
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4. mnist hand-written digit database -> build application for recognizing full phone numbers(10 digit).
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5. live cricket feed -> generating highlights of the match.
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6. designing a chatbot for getting cricket scores.
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# general approach to machine-learning
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1. model, objective and learning algo
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3. ml is technique for learning from examples
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-> recommending smart-phone to a friend. price, branch, cam quality, screen size, processing speed. ==> model
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-> objective ( why is he buying the phone) should be able to be boiled-down to a single number.
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-> learning algo
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binary features -> camera 0|1
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-> screen small|large
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-> battery good|bad
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-> memory high|low
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-> processing fast|slow
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-> audio good|bad
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prior probability -> probability of an event occuring without any knowledge of conditions
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P(A)*P(B|A) = P(B)*P(A/B) = P(A,B)
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P(+ve|[x1,x2,x3...xn]) = P([x1,x2...xn]|+ve)*P(+ve)/P([x1,x2...xn])
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= P(x1|+ve)*P(x2|+ve)*P(x3|+ve)*P(+ve)/P([x1,x2...xn])
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= Pi (i=1 to n) P(xi|+ve) * P(+ve)(Class variable)/ Sum (C=+ve to -ve) (P([x1,x2....xn],C))
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= Pi (i=1 to n) P(xi|+ve) * P(+ve)(Class variable) / (( Pi (i=1 to n ) P(xi|+ve)*P(+ve))+..+ Pi (i=1 to n) P(xi|-ve)*P(-ve))
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P(X,Y) = Sum (y=y1...yn) P(X,Y=y)
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W2 = P(+ve|xi=1) (human approach) (Naive bayes) (naively thinking that all features are independent)
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Regression : output contains real values -> (predicting the position of joints of a body given an image of a person)
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Classification : output classifies to discrete set of classes
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-> predicting the posture of a person (sitting,walking,standing,running) given an image of a person
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(Numerical/Categorical)
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Representation Learning: embedding
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Deeplearning is all about hierarchical representation learning.
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Metric Learning: distance( of facial features)/similarity(of fashion apparels)/relevance( of search document)
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Structured Output(Models): auto-corrects dependent outputs based on output on the upper hierarchy outputs.
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Types of input:
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Bag of features,bag of words: ( finding whether a feature is present/not without caring where the feature occurs in the input)
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eg: Using unsupervized learning to convert the input to a given set of classes(clusters) and use them as bag of features.
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Spatial data(sequential data): if there local dependencies use CNN(convolutional nn) if there are near past dependencies in the data use RNN(Recurrent NN-LSTM)
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eg: stock market temporal data / speech data/ image data
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Non-Parametric models : k-NN(K-nearest neighbor), Decision Trees, Random Forests (independent of parameters)
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-> very inaccurate because doesn't know much about the data
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Parametric Models: based on fixed set of parameters,SVM
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-> more accurate coz the knows more about the parameters from the data
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Types of Learning:
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supervized learning -> labeled data
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unsupervized learning -> unlabeled data
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exercise: *take 3s from mnist data *create a gmm model with them and *cluster them with 5/3/10 gaussians.
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*take all images and cluster them to 10 gaussians.
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semi-supervized learning -> combination of supervized and unsupervized models
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Auto Encoder:
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finding a low dimensional representation of a high dimensional data.
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eg. image of 200x200 pixels create a fingerprint of image of 128 dimensions.
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exercise: use the 128 dimensional data to reconstruct the 200x200 image(using inverse of the model).
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Reinforcement learning:
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eg:playing chess -> using the final result of the game to assign weights/score for moves that were made upto the final result. and training the model to predict based on those scores.
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