This document provides an overview and introduction to deep learning including: what deep learning is, how it utilizes neural networks to learn patterns from large amounts of data, a brief history of deep learning, the differences between machine learning and deep learning, common deep learning architectures like artificial neural networks and deep neural networks, applications of deep learning like computer vision and natural language processing, and some of the major companies utilizing deep learning.

1. Deep Learning
Master's degree in Database and Artificial Intelligence 2018 / 2019
Hatim EL-QADDOURY

2. OUTLINE
• Introduction
• Overview
• What is Deep Learning?
• Evolution
• Brief History
• Basics
• ML vs DL
• Architecture
• ANNs
• DNNs
• Applications
• Classes
• Big players
• Conclusion
Deep Learning
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3. Deep Learning - Overview
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.
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4. Deep Learning - Overview
Brief History
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5. Deep Learning - Overview
Evolution
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6. Deep Learning - Basics
ML vs DL
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ML DL
High-Performance
Computing
No Yes
Training dataset Small Large
Choose your own
features
Yes No
# of classifiers
available
Many Few
Training Time Short Long
• What to choose ?

7. Deep Learning – Application
ANNs - Artificial Neural Networks
Biological neural networks ANNs or Artificial neural networks
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8. Deep Learning – Basics
DNNs - Deep Neural Networks
DNNs is an ANN with multiple layers
between the input and output layers.
The more layers the network has, the
higher-level features it will learn.
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9. Deep Learning – Basics
Architecture
A DNN consists of a hierarchy of layers,
whereby each layer transforms the
input data into more abstract
representations .
The output layer combines those
features to make predictions.
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10. Deep Learning – Applications
Classes
• Convolutional Neural Nets (CNN) applied to
analysing Visual Imagery.
• Recurrent Neural Nets (RNN) applicable to tasks
such as : connected handwriting recognition,
speech recognition, Sign Language Translation.
• Image Captioning – Combining CNN and RNN
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11. Deep Learning – Applications
Big players
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THANKS!
Any questions?
Hatim EL-QADDOURY
+33 7 69 00 55 57
hatim-elqaddoury@outlook.fr
You can find me at
Deep Learning