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Deep Learning Tutorial

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Ligeng Zhu

A deep learning tutorial for machine learning beginners.

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Ligeng Zhu lykenz@linux.com Deep Learning Live
● What is deep learning (DL)? ● In short, DL is about works driven by super deep neural networks. Overview of Deep Learning
Overview of Deep LearningFus e Creat e Prisma Paints Chainer Understand
Meaning of Deep Learning ● What is the necessity of having such a giant (millions of parameters)? ● Solve non-well-defined problem ● Handle ambiguous features ● Perhaps, the giant is not so scary?
Meaning of Deep Learning ● Solve non-well-defined problem How to describe this mapping? 32 * 32
Meaning of Deep Learning ● Handle ambiguous features
Meaning of Deep Learning ● Handle ambiguous features
Meaning of Deep Learning ● Perhaps, the giant is not so scary? ● There are many excellent open source frameworks. ● Caffe/Tensorflow/MXnet/Torch <- My favorite ● End-to-end fashion doesn’t involve much stuff. ● The state-of-the-art model can be implemented in just 30 lines! ● Model can easily be generalized to other cases. ● Design once, deploy everywhere.
Prerequisites ● Math ● Calculus (derivate / maxima / maxium) ● Linear algebra ( basic vector / matrix operation) ● Coding ● Have taken any course about C/C++/Java ● Python basics
The First Step — Linear Regression Given data (x1, y1), (x2, y2), … (xn, yn), to find a line which minimize the overall distance Example: You now are going to rent a apartment and worrying about leasing a overpriced one. Therefore you write a web crawl to collect apartment information nearby. Linear regression can help you to find proper parameters to estimate apartment price.
Linear Regression Now write it in matrix form Data (x1, y1), (x2, y2), … (xn, yn)
Linear Regression Previous Updated
Linear Regression Mathematical approach Code Monkeys’ approach WTF ? Repeat n times gradient = get_gradient(W) W = W - learning_rate * gradient
Linear Regression Gradient Descent Repeat n times gradient = get_gradient(W) W = W - learning_rate * gradient Why it works?
Linear Regression
Linear Regression Repeat n times gradient = get_gradient(W) W = W - lr * gradient
Limits of Linear Regression ● How to deal with big data? ● Stochastic Gradient Descent (A small batch per loop) ● How to choose a proper learning rate(step size)? ● Cross-Validation ● Advanced algorithm — Adagrad ● What if stuck at local optimal? ● A good initialization ● Advanced algorithm — Momentum, Nestrov
Limits of Linear Regression ● How to deal with non-linear data?
Limits of Linear Regression ● Normalization ● Normalize all data into [-1, 1] range ● Activation ● Bound update range by threshold
Linear Regression ● Activation ● Bound update range by threshold
Linear Regression E.O.F — Linear Regression Part
Neural Network ● The history of Neural Network ● Created in late 1940s by a psychologist (Hebbian Learning). ● Short resurgence in 1975 powered by back propagation. ● Extremely popular after 2012 (The AlexNet outperforms any other traditional models)
Multi-Layer Perceptron ● Neural Unit
Multi-Layer Perceptron ● Stacking (Hidden) Layers
Multi-Layer Perceptron ● Activation brings non-linearity
Propagation and Back Propagation W_ij : the weight of edge from previous Jth node to Ith node a_k : the value of Kth node z_k : the value after activation of Kth node
Propagation and Back Propagation
Propagation and Back Propagation If we have computed all deltas, then derivates on each edge can be easily computed by chain rule.
Propagation and Back Propagation If we have computed all deltas, then derivates on each edge can be easily computed by chain rule.
Going Deeper! Deep Neural Network ● Why previous neural network cannot go deeper? ● Too much parameters! ● Gradient vanish ● Backward of tons of parameters ● take weeks to train ● easily to overfit ● stuck at local optimal
Deep Neural Network ● Image Processing Basics — convolution
Deep Neural Network ● Image Processing Basics — convolution
Deep Neural Network ● Convolutional operation ● Different kernel matrices brings different results ● Can be view as a powerful preprocess tool ● But how to choose a proper kernel matrix? ● Let the matrix learn parameters by itself ! ● Convolutional layers!
Deep Neural Network ● Convolutional Layer
Deep Neural Network
Deep Neural Network ● AlexNet — Champion of ImageNet competition 2012
Deep, deeper, deepest!
Coding ● PyTorch Tutorial ● Don’t waste time on choosing framework! Just pick one, and start coding!

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Deep Learning Tutorial