Big Data II Machine Learning and Deep Learning
deep learning definition
Deep learning refers to a type of machine learning that allows computers to learn complex concepts by learning simpler concepts and combining them
K-means
an algorithm in which "k" indicates the number of clusters and "means" represents the clusters' centroids
recurrent neural networks
any network with neurons that send feedback signals to each other s(t)= f(s(t-1), x (t);0theta -one to one, one to many, many to one, many to many, many to many
Support Vector Machine
can extrapolate information from one dimensional data (input space) and some information about weights & correlative relationships to another dimension (feature space) -maximize with of the decision boundary
neural networks
input layer, hidden layer, output layer
deep learning
the most advance machine learning techniques
Convolutional neural networks
use kernels that look at small portions of the image separately first •A kernel is a small matrix used for convolution in image processing -input image, convolutional layer, pooling layer, convolution layer, pooling, conv, pooling, connected layers, software output
linear model =
y= b0+b1x
Together RNNs and CNNs can be used for:
•Image captioning •Video classification •Video generation
RNNs can be used for:
•Modelling the stock market •Natural Language Processing (NLP) •Supply chain forecasting •Machine translation •Speech processing
deep neural network
•Refers to a neural network with more than one hidden layer •Two most common DNNs are: •Convolutional neural network (CNN) - used for image classification •Recurrent neural networks (RNN) - used for natural language processing and for sequential data
deep learning uses layers
•Some layers capture simple concepts •Other layers build on the simpler concepts -input layer, hidden layers output layer
three types of machine learning
•Supervised learning •Unsupervised learning •Reinforcement learning
Deep neural networks are trained through a process called optimization
•This is like regression, where we try to minimize the loss function •The parameters used to tune neural network optimization are called hyperparameters
Convolutional neural networks can be used for:
Image classification •Video classification •Image captioning -Natural language processing
machine learning
In 1959 Arthur Samuel coined the term machine learning •He defined machine learning as a:•Field of study that gives computers the ability to learn without being explicitly programmed. •In 1998 Tom Mitchell proposed a more precise definition: •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.
logistic model=
P= 1/ ( 1+e)^-(b0+b1x)
Advanced RNNs include:
Recursive neural networks (RNN) •Gated recurrent units (GRUs) •Long short-term memories (LSTMs)
reinforcement
-decision process -reward system -learn series of action
supervised
-labeled data -direct feedback -predict future outcome -goes to classification and regression on a squiggly graph with a boundary
unsupervised
-no labels -no feedback -fine hidden structure -goes to clustering