Chapter 7: Machine Learning and Deep Learning

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1998 Tom Mitchell made a more precise definition for machine learning

A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

1959 Arthur Samuels coined "machine learning" as

Field of study that gives computers the ability to learn without being explicitly programmed.

deep learning

The most advanced machine learning methods

Recurrent Neural Network (RNN)

a cyclical type of neural network -one to one, one to many, many to one, many to many -h(t-1), h(t), h(t+1)

Kernal

a small matrix used for convolution in image processing -look at small portions of the image separately first

Support Vector Machine

can extrapolate information from one dimensional data (input space) and some information about weights & correlative relationships to another dimension (feature space) -maximizes the width of the decision boundary

machine learning algorithms supervised

continuous -regression, linear, polynomial, decision trees, random forests categorical -classification (KNN, trees, logistic regression, naive babes, SVM)

reinforcement learning

decision process, reward system, learn series of actions

Convolutional Neural Networks (CNN)

deep neural network used for image classification ex:GoogleLeNet

Optimization

deep neural networks are trained through this -where we try to minimize the loss function -

Machine Learning algorithms unsupervised

for continuious -clustering and dimensionality, SVD,PCA, Means for categorical -Association analysis, apriori,FP growth, Hidden Markov model

neural networks

have an input layer, hidden layer and output layer

supervised learning

labeled data, direct feedback, predict outcome/future -develop predictive model based on input and output data -link to classification and regression

unsupervised learning

no labels, no feedback, find hidden structure -group and interpret data based ONLY on input data -links to clustering

logistic regression model

p=1/(1+e^-(b0+b1x)

hyperparameter

parameters used to tune neural network optimization

Deep Learning

refers to a type of machine learning that allows computers to learn complex concepts by learning simpler concepts and combining them -does this using layers that capture simple concepts or build on simpler concepts -a lot of hidden layers

Three types of machine learning

supervised, unsupervised, reinforcement

Machine Learning

the extraction of knowledge from data based on algorithms created from training data -AI learns predictive models extracted from data

recurrent neural networks (RNN)

used for natural language processing and for sequential data

Together RNNs and CNNs can be used for:

•Image captioning •Video classification •Video generation

Convolutional neural networks can be used for:

•Image classification •Video classification •Image captioning •Natural language processing

RNNs can be used for...

•Modelling the stock market •Natural Language Processing (NLP) •Supply chain forecasting •Machine translation •Speech processing

Advanced RNNs

•Recursive neural networks (RNN) •Gated recurrent units (GRUs) •Long short-term memories (LSTMs)

deep neural network (DNN)

Refers to a neural network with more than one hidden layer


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