1) The document discusses artificial intelligence and machine learning, including different types of machine learning problems like supervised learning, unsupervised learning, and reinforcement learning.
2) It provides examples of applications of these machine learning techniques, such as spam filtering for classification, clustering similar news items for unsupervised learning, and AlphaGo for reinforcement learning.
3) Deep learning and artificial neural networks are described as techniques that use a multi-level network approach to capture abstractions in data without requiring feature engineering.
17. Clustering applications
Grouping similar news items Kharinov, M. "Hierarchical pixel clustering for image segmentation." arXiv preprint (2014).
Pixel clustering for segmentation
18. Reinforcement Learning
Teaching a machine by ‘rewarding’ it
for good ‘actions’ and ‘punishing’ it
for bad ones
Attempt is to explore the entire state
space for a problem and get the best
actions corresponding to each state,
also known as ‘policy’
20. Deep Learning
Capturing abstractions using a multi-
level or ‘network’ approach
Each level or ‘layer’ composed of many
simple processing units
The internal abstractions are often the
best features to use for the problem,
so no feature engineering is required
21. Artificial Neural Networks (ANNs)
Deep networks composed of artificial
neurons
Inspired by biological neurons
Activation function is typically
sigmoid, can be tanh or ReLu
The method used to train a network is
called ‘backpropagation’
Traditional neural networks with all
signals propagating in one direction
are called ‘feedforward’ networks
Structure of a typical biological neuron
Typical artificial neuron
24. Recurrent Neural Networks (RNNs)
Hidden layers feed back into
themselves
Can be used to model sequences and
for use as associative memory
Can take input sequences of arbitrary
length using the concept of
‘attention’
25. RNN applications (with links)
Automatic music generation (Site has source code link)
Handwriting synthesis (Site has paper and source code links)
Intelligent personal assistants like Siri, Google Now, Cortana
Automatic image captioning
Sunspring
LSTM that generates poems
26. Learning Resources
Good courses or tutorials for ML
Coursera ML by Andrew Ng
Datacamp ML course
Udacity Deep Learning
Learning by doing
Kaggle
Topcoder Data science
Good video lectures for ML
Gilbert Strang lectures on Linear Algebra
Nando de Freitas Deep Learning
Some people I follow in ML
Andrej Karpathy Peter Norvig
Alex Graves Fei Fei Li
Andrew Ng
Some good blogs on ML
WildML
IAmTrask
Karpathy’s blog
And finally there’s Google
Scholar. Read lots of
research papers and try to
implement them!