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Deep Learning Class #1 - Go Deep or Go Home

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Holberton School
Holberton School

Slides by Louis Monier (Altavista Co-Founder & CTO) for Deep Learning keynote #1 at Holberton School. The keynote was followed by a workshop prepared by Gregory Renard. If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/

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Louis Monier @louis_monier https://www.linkedin.com/in/louismonier Deep Learning Basics Go Deep or Go Home Gregory Renard @redo https://www.linkedin.com/in/gregoryrenard Class 1 - Q1 - 2016
Supervised Learning cathatdogcatmug … … =? prediction dog (89%) training set… Training (days) Testing (ms)
Single Neuron to Neural Networks
Single Neuron + x w1 xpos x w2 ypos w3 ChoiceScore Linear Combination Nonlinearity 3.4906 0.9981, red Inputs 3.45 1.21 -0.314 1.59 2.65
More Neurons, More Layers: Deep Learning input1 input2 input3 input layer hidden layers output layer circle iris eye face 89% likely to be a face
How to find the best values for the weights? w = Parameter Error a b Define error = |expected - computed| 2 Find parameters that minimize average error Gradient of error wrt w is Update: take a step downhill
Egg carton in 1 million dimensions Get to here or here
Backpropagation: Assigning Blame input1 input2 input3 label=1 loss=0.49 w1 w2 w3 b w1’ w2’ w3’ b’ 1). For loss to go down by 0.1, p must go up by 0.05. For p to go up by 0.05, w1 must go up by 0.09. For p to go up by 0.05, w2 must go down by 0.12. ... prediction p=0.3 p’=0.89 2). For loss to go down by 0.1, p’ must go down by 0.01 3). For p’ to go down by 0.01, w2’ must go up by 0.04
Stochastic Gradient Descent (SGD) and Minibatch It’s too expensive to compute the gradient on all inputs to take a step. We prefer a quick approximation. Use a small random sample of inputs (minibatch) to compute the gradient. Apply the gradient to all the weights. Welcome to SGD, much more efficient than regular GD!
Usually things would get intense at this point... Chain rule Partial derivatives Hessian Sum over paths Credit: Richard Socher Stanford class cs224d Jacobian matrix Local gradient
Learning a new representation Credit: http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/
Training Data
Train. Validate. Test. Training set Validation set Cross-validation Testing set 2 3 41 2 3 41 2 3 41 2 3 41 shuffle! one epoch Never ever, ever, ever, ever, ever, ever use for training!
Tensors
Tensors are not scary Number: 0D Vector: 1D Matrix: 2D Tensor: 3D, 4D, … array of numbers This image is a 3264 x 2448 x 3 tensor [r=0.45 g=0.84 b=0.76]
Different types of networks / layers
Fully-Connected Networks Per layer, every input connects to every neuron. input1 input2 input3 input layer hidden layers output layer circle iris eye face 89% likely to be a face
Recurrent Neural Networks (RNN) Appropriate when inputs are sequences. We’ll cover in detail when we work on text. pipe a not is this t=0t=1t=2t=3t=4 this is not a pipe t=0 t=1 t=2 t=3 t=4
Convolutional Neural Networks (ConvNets) Appropriate for image tasks, but not limited to image tasks. We’ll cover in detail in the next session.
Hyperparameters Activations: a zoo of nonlinear functions Initializations: Distribution of initial weights. Not all zeros. Optimizers: driving the gradient descent Objectives: comparing a prediction to the truth Regularizers: forcing the function we learn to remain “simple” ...and many more
Activations Nonlinear functions
Sigmoid, tanh, hard tanh and softsign
ReLU (Rectified Linear Unit) and Leaky ReLU
Softplus and Exponential Linear Unit (ELU)
Softmax x1 = 6.78 x2 = 4.25 x3 = -0.16 x4 = -3.5 dog = 0.92536 cat = 0.07371 car = 0.00089 hat = 0.00003 Raw scores Probabilities S O F T M A X
Optimizers
Various algorithms for driving Gradient Descent Tricks to speed up SGD. Learn the learning rate. Credit: Alex Radford
Cost/Loss/Objective Functions =?
Cross-entropy Loss x1 = 6.78 x2 = 4.25 x3 = -0.16 x4 = -3.5 dog = 0.92536 cat = 0.07371 car = 0.00089 hat = 0.00003 Raw scores Probabilities S O F T M A X dog: loss = 0.078 cat: loss = 2.607 car: loss = 7.024 hat: loss = 10.414 If label is: =? label (truth)
Regularization Preventing Overfitting
Overfitting Very simple. Might not predict well. Just right? Overfitting. Rote memorization. Will not generalize well.
Regularization: Avoiding Overfitting Idea: keep the functions “simple” by constraining the weights. Loss = Error(prediction, truth) + L(keep solution simple) L2 makes all parameters medium-size L1 kills many parameters. L1+L2 sometimes used.
At every step, during training, ignore the output of a fraction p of neurons. p=0.5 is a good default. Regularization: Dropout input1 input2 input3
One Last Bit of Wisdom
Workshop : Keras & Addition RNN https://github.com/holbertonschool/deep-learning/tree/master/Class%20%230

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Making communications land - Are they received and understood as intended? we...
 

Deep Learning Class #1 - Go Deep or Go Home