This document provides an overview of deep learning presented by Khaled Amirat at the University of Souk Ahras in Algeria in 2017. It defines artificial intelligence and machine learning, and explains that deep learning is a type of machine learning that uses neural networks with multiple layers to automatically learn representations of raw data. The document contrasts deep learning with traditional machine learning approaches that require manual feature engineering, and outlines different deep learning architectures like convolutional neural networks and recurrent neural networks. Examples are given of applications in areas like computer vision, natural language processing, and topic modeling from documents. The training process of neural networks using forward propagation and backpropagation is also summarized.

1. A presentation on
Deep Learning
Presented by Khaled Amirat
Supervised by Dr Faiz Maazozi
Department of Mathematics and Computer Science
University of Souk Ahras, Algeria
2017
1

2. What is Artificial Intelligence?
Input
Sensors
data
Artificial
Intelligence
Output:
Movement
Text
● A cota of data will be sensred by sensors like webCam …
● An algorithm will be executed to train those data based on interference motor .
● Finally we will got an intelligent system.

3. What is Deep Learning
● Part of the machine learning field of learning representations of data.
Exceptional effective at learning patterns.
● Utilizes learning algorithms that derive meaning out of data by using a
hierarchy of multiple layers that mimic the neural networks of our brain.
● If you provide the system tons of information, it begins to understand it
and respond in useful ways.

4. Difference Between Machine learning & AI
● AI is the study of how to create intelligent agents.
● It is how to program a computer to behave as an intelligent agent.
● This does not have involve learning or induction at all.
● AI uses model built by machine learning.

5. Introductions
Machine Learning is a type of Artificial Intelligence that provides
computers with the ability to learn without being explicitly programmed.
Labled
Data
Machine
Learning Algorithm
Training Data
Data
Learned
Model
Prediction
Prediction phase

6. Why deep learning ?
● A cota of data will be sensred by sensors like webCam …
● An algorithm will be executed to train those data based on interference motor .
● Finally we will got an intelligent system.
Speach
Recognition
Computer
Vision
Natural
Language
processingOptical
Character recognition
It give good result in :

7. No more feature engineering
Feature engineering is the key — and time-consuming — parts of the machine
learning workflow. They are about transforming training data and augmenting it
with additional features in order to make machine learning algorithms more
effective. Deep learning is changing that, according to its promoters. With deep
learning, one can start with raw data, as features will be automatically created by the
neural network when it learns
Input
Data
Input
Data
Deep
Learning
Algorithm
Future
engineering
Traditional
Learning
Algorithm
Cost a lot of time

8. Architecture
A deep neural network consists of a hierarchy of layers, whereby each layer
transforms the input data into more abstract representations (e.g. edge -> nose ->
face). The output layer combines those features to make predictions

9. What did it learn?

10. Artificial Neural Networks
Consists of one input, one output and multiple fully-connected hidden layers in- between.
Each layer is represented as a series of neurons and progressively extracts higher and
higher-level features of the input until the final layer essentially makes a decision about what
the input shows. The more layers the network has, the higher- level features it will learn.

11. Artificial Neural Networks
An artificial neuron contains a nonlinear activation function and has several
incoming and outgoing weighted connections.
Neurons are trained to filter and detect specific features or patterns (e.g. edge, nose)
by receiving weighted input, transforming it with the activation function und passing it to
the outgoing connections.

12. The Training Process
Learns by generating an error signal that measures the difference between the
predictions of the network and the desired values and then using this error signal to
change the weights (or parameters) so that predictions get more accurate.
Simple
labeled
data
Forward it to
the network to
get prediction
Update the
connection
weights
Backpropagate
the errors

13. Deep learning could handle a lot of
algorithms such as
1. Deep Autoencoders
2. Long Short-Term Memory RNN (LSTM)
3. Convolutional Neural Nets (CNN)
4. Recurrent Neural Nets (RNN)

14. Deep Autoencoders
Topic Modeling: Document in a collection is converted to a Bag-of- Words and
transformed to a compressed feature vector using an autoencoder. The distance from
every other document-vector can be measured and nearby document-vectors fall under
the same topic.
Composed of two symmetrical
deep-belief networks. The encoding
network learns to compresses the
input to a condensed vector
(dimensionality reduction). The
decoding network can be used to
reconstruct the data.

15. Long Short-Term Memory RNN (LSTM)
The LSTM units give the network memory cells with read, write and reset
operations. During training, the network can learn when output sequences it
should remember data and when it should throw it away.
A Long Short-Term Memory
(LSTM) network is a particular type of
recurrent network that works slightly
better in practice, owing to its more
powerful update equation and some
appealing back propagation dynamics.

16. Convolutional Neural Nets (CNN)

17. Example
Activation number
More Lighter if in the
front of 1

18. Example
28 x 28 =
Neurons
Hidden Layers

19. Example The
component of
number

20. Example
Now it’s
better if we
look after the
edge

21. Example

22. How does the weight work’s is like we
have seen it in the lecture before
Going to see an example on matlab