The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
1. Deep learning: what is it
and why it is so powerful?
Dr Natalia Konstantinova
2. Overview
◎ What is Machine Learning?
◎ Ideas behind Neural Networks?
◎ What is Deep Learning? How different is it from NN?
◎ Practical examples of applications.
3. What is ML?
General explanation
ML can refer to:
◎ the branch of artificial intelligence;
◎ the methods used in this field (there are a variety of
different approaches).
Tom Mitchell - “improving performance in some task with
experience”.
“ML deals with systems that can learn from data”.
4. What is ML?
Real world examples
◎ Classification of things, e.g. is it fraud or not
◎ Identification of people in the photos (computer
vision)
◎ Medical diagnosis
◎ Decision to give credit or not
◎ Game playing
◎
5. What is ML?
Paradigms
All ML tasks can be classified in several categories,
the main ones are:
◎ supervised ML;
◎ unsupervised ML;
◎ reinforcement learning.
8. Neural Networks
General info
◎ A family of statistical learning models inspired by biological
neural networks and are used to estimate or approximate
functions that can depend on a large number of inputs and are
generally unknown.
○ composed of a large number of highly interconnected processing
elements (neurones) working in unison to solve specific problems.
10. Deep Learning
What is it?
Deep learning (deep machine learning, or deep
structured learning, or hierarchical learning, or
sometimes DL) is a branch of machine learning
based on a set of algorithms that attempt to model
high-level abstractions in data by using model
architectures, with complex structures or otherwise,
composed of multiple non-linear transformations.
12. Deep Learning
Principles
◎ The underlying assumption behind distributed
representations (are not mutually exclusive) is that
observed data is generated by the interactions of
many different factors on different levels.
◎ Deep learning adds the assumption that these factors
are organized into multiple levels, corresponding to
different levels of abstraction or composition.
◎ Deep learning methods are focused on end-to-end
learning based on raw features.
13. Deep Learning
Why it worked now
Neural Networks are known for a long time (from
80s) - so why now?
◎ Machines became more powerful for training
◎ Got access to more data
What else?
18. Deep Learning
Applications
◎ Speech recognition (language modelling, decomposition
of input)
◎ Image recognition (e.g. cats and dogs - by just watching
youtube, emotion detection)
◎ Natural language processing (word2vec, neural
language model, part of speech tagging, parsing,
named entity recognition, paraphrasing, question
answering)
◎ Drug discovery and toxicology