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Introduction to Deep
Learning
Vishwas Lele (@vlele)
Applied Information Sciences
Agenda
•Introduction to Deep Learning
• From ML to Deep Learning
•Neural Network Basics
• Dense, CNN, RNN
• Key Concepts
• CNN Basics
•X-ray Image Analysis
• Optimization (using pre-trained models)
• Visualizing the learning
Introduction to deep learning
Demo
• Why do I need to learn neural networks when I have Cognitive and
other APIs?
What is Deep Learning?
Deep Learning Examples
• Near-human-level image classification
• Near-human-level speech recognition
• Near-human-level handwriting transcription
• Digital assistants Google Now, Cortana
• Near-human-level autonomous driving
Why now? What about past AI winters?
• Hardware
• Datasets and benchmarks
• Algorithmic advances
What about other ML techniques?
• Probabilistic modeling (Naïve Bayes)
• Kernel Methods
• Support Vector Machines
• Decision Trees
• Random Forests
• Gradient Boosting Machines
“Deep” in deep learning
How can a network help us?
0
1
2
3
4
7
5
6
9
8
Digit Classification
Network of layers
• Layers are like LEGO bricks of deep learning
• A data-processing module that takes as input one or more tensors
and that outputs one or more tensors
Tensor
shape (128, 256, 256, 1)
How layers transform data?
output = relu(dot(W, input) + b)
W- Weight
b - Bias
Rectified Linear Unit (RELU)
Power of parallelization
def naive_relu(x):
i in range(x.shape[0]): for j in range(x.shape[1]): x[i, j] = max(x[i, j], 0)
return x
z = np.maximum(z, 0.)
Sigmoid function
How network learns?
Training Loop
• Draw a batch of training samples x and corresponding targets y.
• Run the network on x
• Compute the loss of the network
• Update all weights of the network in a way that slightly reduces the
loss on this batch.
Key Concepts
• Derivative
• Gradient
• Stochastic Gradient
Stochastic gradient descent
• Draw a batch of training samples x and corresponding targets y.
• Run the network on x to obtain predictions y_pred.
• Compute the loss of the network on the batch, a measure of the
mismatch between y_pred and y.
Stochastic gradient descent
• Compute the gradient of the loss with regard to the network’s
parameters (a backward pass).
• Move the parameters a little in the opposite direction from the
gradient—for example W = step * gradient—thus reducing the loss on
the batch a bit.
Back propagation
0
1
2
3
4
7
5
6
9
8
Demo
• Digit Classification
• Keras Library
Training and validation loss
Overfitting
Weight Regularization
• L1 regularization— The cost added is proportional to the absolute
value of the weight coefficients
• L2 regularization— The cost added is proportional to the square of
the value of the weight coefficients
Dropout
• Randomly dropping out (setting to zero) a number of output features
of the layer during training.
Feature Engineering
• process of using your own knowledge to make the algorithm work
better by applying hardcoded (nonlearned) transformations to the
data before it goes into the model
Feature engineering
How network learns?
Enter CNN or (Convolution Neural Networks)
Convolution Operation
Spatial Hierarchy
How Convolution works?
Sliding
Padding
Stride
Max-Pooling
• Extracting windows from the input feature maps and outputting the
max value of each channel.
• It’s conceptually similar to convolution.
• Except that instead of transforming local patches via a learned linear
transformation
Demo
• Digit Classification (using CNN)
• Keras Library
Demo
• X-ray Image Classification
• Dealing with overfitting
• Visualizing the learning
Visualizing the learning
Overfitting
With dropout regularization
With data augmentation
Demo
• Activation heatmap

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Introduction to deep learning