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Introduction To Machine Learning and Deep Learning
Ibrahim AmerTA at FCIS,Ain Shams [email protected]
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Agenda
What is the computer? Introduction to machine learning Applications on machine learning Introduction to deep learning Convolutional Neural Networks How to build a computer to start deep learning Deep learning Tools Companies and deep learning
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What is computer?
A computer is a machine that understands zeros and ones only.
The computer can perform complex mathematical and arithmetic operations very fast.
It performs this using a combination of circuits and logic gates.
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Comment on the following
What is the result?
50 + 30 ?
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Comment on the following
What is the result?
58945 + 78954?
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Comment on the following
What is the result?
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Comment on the following
What do you see?
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Comment on the following
What do you see?
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Comment on the following
Can you write a program to compute the power ?
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Comment on the following
Can you write a program to recognize faces or recognize brands of cars?
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Conclusion
Computers are very powerful and accurate in calculations, they can perform any arithmetic operation faster than any human being.
Humans are faster and more accurate than computers in recognition tasks.
Do you know any one who could compute 787452665 * 75767487 in just seconds?
Are the machines capable recognize and to reach human level accuracy in recognition ?
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What is Machine Learning? “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.” -- Tom Mitchell,
Carnegie Mellon University
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What is Machine Learning?
Machine learning is a discipline of AI It is a series of techniques that are used to make the computer smarter
. ML solves problems that cannot be solved by numerical means alone. Machine learning is empowering a lot of technologies used today. Among the different types of ML tasks, a crucial distinction is drawn
between supervised and unsupervised learning: Supervised machine learning: The program is “trained” on a pre-defined
set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data.
Unsupervised machine learning: The program is given a bunch of data and must find patterns and relationships therein.
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Supervised Machine Learning
In the majority of supervised learning applications, the ultimate goal is to develop a finely tuned predictor function (sometimes called the “hypothesis”)
Given input data about a certain domain (say, square footage of a house), it will accurately predict some interesting value
In practice, x almost always represents multiple data points. So, for example, a housing price predictor might take not only square-footage (x1) but also number of bedrooms (x2), number of bathrooms (x3), number of floors (x4), year built (x5), zip code (x6), and so forth. Determining which inputs to use is an important part of ML design. However, for the sake of explanation, it is easiest to assume a single input value is used.
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So let’s say our simple predictor has this form:
Where and are constants. Our goal is to find the perfect values and to make our predictor work as well as possible.
To make our program learn and we should update them according to error analysis which is called mean square error.
The weights are updated using gradient decent algorithm
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Unsupervised Learning
Unsupervised learning typically is tasked with finding relationships within data.
There are no training examples used in this process.
Instead, the system is given a set data and tasked with finding patterns and correlations therein.
Examples: astronomical data analysis, social network analysis and cocktail party problem and news grouping
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Applications on machine learning
Spam filtering: Classifying emails as spam or non-spam
Weather forecast: Machine learning is applied in weather forecasting software to improve the quality of the forecast.
Anti-virus: Machine learning is used in Anti-virus software's to improve detection of malicious software on computer devices.
Personal Assistants: Siri & Cortana
Classifying a tumor as benign tumor or malignant tumor.
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Deep Learning
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
Andrew Ng: “very large neural networks we can now have and … huge amounts of data that we have access to”
Andrew Ng: “for most flavors of the old generations of learning algorithms … performance will plateau. … deep learning … is the first class of algorithms … that is scalable. … performance just keeps getting better as you feed them more data”
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Deep Learning
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Deep Learning
Deep learning is set of techniques that in some cases could reach human accuracy in recognition !
The most common used and the most famous architecture is convolutional neural network.
There are some other techniques like: LSTM (Long Short Term Memory) Residual Neural Networks Autoencoders Generative Adversarial Networks.
We will focus on convolution neural networks.
A toy ConvNet: X’s and O’sSays whether a picture is of an X or an O
X or OCNN
A two-dimensionalarray of pixels
For example
CNN X
CNN O
Trickier cases
CNN X
CNN Otranslation scaling weightrotation
Deciding is hard
=?
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 -1 -1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 1 -1 -1 -1-1 -1 1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 1 -1 -1-1 -1 -1 1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 -1 -1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
What computers see
=?
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 X -1 -1 -1 -1 X X -1-1 X X -1 -1 X X -1 -1-1 -1 X 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 X -1 -1-1 -1 X X -1 -1 X X -1-1 X X -1 -1 -1 -1 X -1-1 -1 -1 -1 -1 -1 -1 -1 -1
What computers see
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 -1 -1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 1 -1 -1 -1-1 -1 1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 1 -1 -1-1 -1 -1 1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 -1 -1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Computers are literal
=x
ConvNets match pieces of the image =
=
=
1 -1 -1-1 1 -1-1 -1 1
-1 -1 1-1 1 -11 -1 -1
1 -1 1-1 1 -11 -1 1
Features match pieces of the image
1 -1 -1-1 1 -1-1 -1 1
-1 -1 1-1 1 -11 -1 -1
1 -1 1-1 1 -11 -1 1
1 -1 -1-1 1 -1-1 -1 1
-1 -1 1-1 1 -11 -1 -1
1 -1 1-1 1 -11 -1 1
1 -1 -1-1 1 -1-1 -1 1
-1 -1 1-1 1 -11 -1 -1
1 -1 1-1 1 -11 -1 1
1 -1 -1-1 1 -1-1 -1 1
-1 -1 1-1 1 -11 -1 -1
1 -1 1-1 1 -11 -1 1
1 -1 -1-1 1 -1-1 -1 1
-1 -1 1-1 1 -11 -1 -1
1 -1 1-1 1 -11 -1 1
1 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
Filtering: The math behind the match
1. Line up the feature and the image patch.2. Multiply each image pixel by the corresponding feature pixel.3. Add them up.4. Divide by the total number of pixels in the feature.
1 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
1 x 1 = 1
11 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
1 x 1 = 1
1 11 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
-1 x -1 = 1
1 1 11 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
-1 x -1 = 1
1 1 11
1 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
-1 x -1 = 1
1 1 11 1
1 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
1 x 1 = 1
1 1 11 1 1
1 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
-1 x -1 = 1
1 1 11 1 11
1 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
-1 x -1 = 1
1 1 11 1 11 1
1 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
-1 x -1 = 1
1 1 11 1 11 1 1
1 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
1 x 1 = 1
1
1 1 11 1 11 1 1
1 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
1+1+1+1+1+1+1+1+19 =1
11 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
1 x 1 = 1
1 1 -11 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
-1 x 1 = -1
1 1 -11 1 1-1 1 1
1 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Filtering: The math behind the match
1
1 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
1 1 -11 1 1-1 1 1
Filtering: The math behind the match
1+1−1+1+1+1−1+1+19 =.55
.55
1 1 -11 1 1-1 1 1
1 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Convolution: Trying every possible match
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
1 -1 -1-1 1 -1-1 -1 1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Convolution: Trying every possible match
=
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
1 -1 -1-1 1 -1-1 -1 1
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
=
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
-1 -1 1-1 1 -11 -1 -1
1 -1 1-1 1 -11 -1 1
0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
-0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55
0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11
-0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11
0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11
-0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55
0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
=
=
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Convolution layerOne image becomes a stack of filtered images
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
1 -1 -1-1 1 -1-1 -1 1
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
-1 -1 1-1 1 -11 -1 -1
1 -1 1-1 1 -11 -1 1
0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
-0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55
0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11
-0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11
0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11
-0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55
0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Convolution layerOne image becomes a stack of filtered images
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
-0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55
0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11
-0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11
0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11
-0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55
0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Pooling: Shrinking the image stack1. Pick a window size (usually 2 or 3).2. Pick a stride (usually 2).3. Walk your window across your filtered images.4. From each window, take the maximum value.
1.00
Pooling
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
maximum
1.00 0.33
Pooling
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
maximum
1.00 0.33 0.55
Pooling
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
maximum
1.00 0.33 0.55 0.33
Pooling
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
maximum
1.00 0.33 0.55 0.33
0.33
Pooling
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
maximum
1.00 0.33 0.55 0.33
0.33 1.00 0.33 0.55
0.55 0.33 1.00 0.11
0.33 0.55 0.11 0.77
Pooling
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
max pooling
1.00 0.33 0.55 0.33
0.33 1.00 0.33 0.55
0.55 0.33 1.00 0.11
0.33 0.55 0.11 0.77
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
-0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55
0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11
-0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11
0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11
-0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55
0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
0.33 0.55 1.00 0.77
0.55 0.55 1.00 0.33
1.00 1.00 0.11 0.55
0.77 0.33 0.55 0.33
0.55 0.33 0.55 0.33
0.33 1.00 0.55 0.11
0.55 0.55 0.55 0.11
0.33 0.11 0.11 0.33
Pooling layerA stack of images becomes a stack of smaller images.
1.00 0.33 0.55 0.33
0.33 1.00 0.33 0.55
0.55 0.33 1.00 0.11
0.33 0.55 0.11 0.77
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
-0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55
0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11
-0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11
0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11
-0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55
0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
0.33 0.55 1.00 0.77
0.55 0.55 1.00 0.33
1.00 1.00 0.11 0.55
0.77 0.33 0.55 0.33
0.55 0.33 0.55 0.33
0.33 1.00 0.55 0.11
0.55 0.55 0.55 0.11
0.33 0.11 0.11 0.33
NormalizationKeep the math from breaking by tweaking each of the values just a bit.Change everything negative to zero.
Rectified Linear Units (ReLUs)
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
0.77
0.77 0
Rectified Linear Units (ReLUs)
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
0.77 0 0.11 0.33 0.55 0 0.33
Rectified Linear Units (ReLUs)
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
0.77 0 0.11 0.33 0.55 0 0.33
0 1.00 0 0.33 0 0.11 0
0.11 0 1.00 0 0.11 0 0.55
0.33 0.33 0 0.55 0 0.33 0.33
0.55 0 0.11 0 1.00 0 0.11
0 0.11 0 0.33 0 1.00 0
0.33 0 0.55 0.33 0.11 0 0.77
Rectified Linear Units (ReLUs)
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
ReLU layerA stack of images becomes a stack of images with no negative values.
0.77 0 0.11 0.33 0.55 0 0.33
0 1.00 0 0.33 0 0.11 0
0.11 0 1.00 0 0.11 0 0.55
0.33 0.33 0 0.55 0 0.33 0.33
0.55 0 0.11 0 1.00 0 0.11
0 0.11 0 0.33 0 1.00 0
0.33 0 0.55 0.33 0.11 0 0.77
0.33 0 0.11 0 0.11 0 0.33
0 0.55 0 0.33 0 0.55 0
0.11 0 0.55 0 0.55 0 0.11
0 0.33 0 1.00 0 0.33 0
0.11 0 0.55 0 0.55 0 0.11
0 0.55 0 0.33 0 0.55 0
0.33 0 0.11 0 0.11 0 0.33
0.33 0 0.55 0.33 0.11 0 0.77
0 0.11 0 0.33 0 1.00 0
0.55 0 0.11 0 1.00 0 0.11
0.33 0.33 0 0.55 0 0.33 0.33
0.11 0 1.00 0 0.11 0 0.55
0 1.00 0 0.33 0 0.11 0
0.77 0 0.11 0.33 0.55 0 0.33
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33
-0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11
0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55
0.33 0.33 -0.33 0.55 -0.33 0.33 0.33
0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11
-0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11
0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
-0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55
0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11
-0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11
0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11
-0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55
0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
Layers get stackedThe output of one becomes the input of the next.
Conv
olut
ion
ReLU
Pool
ing-1 -1 -1 -1 -1 -1 -1 -1 -1
-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
1.00 0.33 0.55 0.33
0.33 1.00 0.33 0.55
0.55 0.33 1.00 0.11
0.33 0.55 0.11 0.77
0.33 0.55 1.00 0.77
0.55 0.55 1.00 0.33
1.00 1.00 0.11 0.55
0.77 0.33 0.55 0.33
0.55 0.33 0.55 0.33
0.33 1.00 0.55 0.11
0.55 0.55 0.55 0.11
0.33 0.11 0.11 0.33
Deep stackingLayers can be repeated several (or many) times.
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Conv
olut
ion
ReLU
Pool
ing
Conv
olut
ion
ReLU
Conv
olut
ion
ReLU
Pool
ing
1.00 0.55
0.55 1.00
0.55 1.00
1.00 0.55
1.00 0.55
0.55 0.55
Fully connected layerEvery value gets a vote
1.00 0.55
0.55 1.00
0.55 1.00
1.00 0.55
1.00 0.55
0.55 0.55
1.00
0.55
0.55
1.00
1.00
0.55
0.55
0.55
0.55
1.00
1.00
0.55
Fully connected layerVote depends on how strongly a value predicts X or O
XO
1.00
0.55
0.55
1.00
1.00
0.55
0.55
0.55
0.55
1.00
1.00
0.55
Fully connected layerVote depends on how strongly a value predicts X or O
XO
0.55
1.00
1.00
0.55
0.55
0.55
0.55
0.55
1.00
0.55
0.55
1.00
Fully connected layerFuture values vote on X or O
XO
0.9
0.65
0.45
0.87
0.96
0.73
0.23
0.63
0.44
0.89
0.94
0.53
Fully connected layerFuture values vote on X or O
XO
0.9
0.65
0.45
0.87
0.96
0.73
0.23
0.63
0.44
0.89
0.94
0.53
Fully connected layerFuture values vote on X or O
XO
0.9
0.65
0.45
0.87
0.96
0.73
0.23
0.63
0.44
0.89
0.94
0.53
.92
Fully connected layerFuture values vote on X or O
XO
0.9
0.65
0.45
0.87
0.96
0.73
0.23
0.63
0.44
0.89
0.94
0.53
.92
Fully connected layerFuture values vote on X or O
XO
0.9
0.65
0.45
0.87
0.96
0.73
0.23
0.63
0.44
0.89
0.94
0.53
.92
.51
Fully connected layerFuture values vote on X or O
XO
0.9
0.65
0.45
0.87
0.96
0.73
0.23
0.63
0.44
0.89
0.94
0.53
.92
.51
Fully connected layerA list of feature values becomes a list of votes.
XO
0.9
0.65
0.45
0.87
0.96
0.73
0.23
0.63
0.44
0.89
0.94
0.53
Fully connected layerThese can also be stacked.
XO
0.9
0.65
0.45
0.87
0.96
0.73
0.23
0.63
0.44
0.89
0.94
0.53
Putting it all togetherA set of pixels becomes a set of votes.
-1 -1 -1 -1 -1 -1 -1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 -1 -1 1 -1 -1 -1 -1-1 -1 -1 1 -1 1 -1 -1 -1-1 -1 1 -1 -1 -1 1 -1 -1-1 1 -1 -1 -1 -1 -1 1 -1-1 -1 -1 -1 -1 -1 -1 -1 -1
Conv
olut
ion
ReLU
Pool
ing
Conv
olut
ion
ReLU
Conv
olut
ion
ReLU
Pool
ing
Fully
conn
ecte
d
Fully
conn
ecte
d XO
.92
.51
88
How to build a computer to start deep learning ?
Deep learning techniques’ are resources hungry.
They require huge computing power to give you good performance.
Imagine we have 10 convolution layers of:
256 Conv Filters * Image(1000, 1000)
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How to build a computer to start deep learning ?
This is pretty huge !.
If you ran this on a CPU it would take 1 day to process 60 000 training images on I7 processor!
What if the network is deeper than 20 layers and more complex than 256 conv filters / layer?
The network in this case may take days or even weeks to finish!!
90
So, what is the solution ?
In each layer in a CNN we are convolving the image with different filters.
What if we could convolve the image with different filters parallel in the same time?
The solution is to use GPU!
91
Comment on the following
How many cores inside a CPU ?
How many cores inside a GPU?
92
What are GPUs brands available out there? There are two famous companies:
Unfortunately, AMD GPUs can’t be used for deep learning.
Nvidia GPUs only can be used
That’s because Nvidia provides tools and support for deep learning geeks.
There is a library called NVIDIA Cuda Toolkit
93
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Nvidia GPUs
Almost all Nvidia’s GPUs will do the job.
You must check if it is CUDA enabled or not.
You can check online. Just search for CUDA supported GPUs.
Some of CUDA supported GPUs: TITAN X, Geforce GTX 1080, Geforce GTX 1070, Geforce GTX 1060, Geforce GTX 1050, Geforce GTX 980, Geforce GTX 970, Geforce GTX 960, Geforce GTX 9xx M Series, Geforce GTX 6xx Series, Geforce GTX 6xx M Series.
You can always use your CPU if you don’t have Nvidia GPU or if you don’t have GPU at all!.
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Deep Learning Tools
After building your computer and configuring it for deep learning, you need the right tools and APIs to start coding
There are plenty of languages that can be used: Matlab Python C++ C# Java And so many…
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Deep Learning Tools
Deep learning frameworks: NVIDIA CUDA programming APIs. Tensorflow (Google’s library) for python. Theano for python. Torch (Facebook’s library) for Lua. Caffe for python. CNTK (Microsoft’s library) for python. Keras to simplify coding for tensorflow and theano.
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Deep Learning and Companies
Many Companies use deep learning on a daily basis.
Facebook: facebook auto tagger, videos and photos auto caption, post analyzer,
DeepMind (acquired by google): AlphaGo and many
Amazon
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Questions ?!
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References
Coursera’s Machine Learning course by Andrew Ng CS231n: Convolutional Neural Networks for Visual Recognition Setup a Deep Learning Environment on Windows (Theano & Keras
with GPU Enabled) ConvNets Visualization How do Convolutional Neural Networks work? Rana el Kaliouby, Co-founder, CEO at Affectiva Hussein Mehanna
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Thank you!
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