Deep Learning in 20 Minutes (or less!)
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1 1/19
2 2/19 Deep Learning in 20 Minutes (or less!) Group meeting May 23, 2016
3 3/19 Outline 1 Intro to Deep Learning 2 RNN 3 LSTM 4 Tools 5 Some useful links 6 Some useful (or maybe not) tips
4 4/19 Deep learning Figure : Artificial Neural Network (source: MLP (Multi Layer Perceptron) is just one type of deep learning Deep Neural Network is neural network with more layers
5 5/19 Deep learning Figure : Deep hierarchical representation of the input data learning feature hierarchies automatically learning features
6 6/19 Some good tutorials to start Machine Learning course by Prof. Andrew Ng (coursera). A tutorial on Deep Learning (Part 1) quocle/tutorial1.pdf How the Backpropagation works. An Introduction to Deep Learning. dwcorne/teaching/introdl.ppt
7 7/19 What are RNN The idea is to make use of sequential information. Some tasks: i.e. next word prediction, language model, POS tagging need information from the previous words. Recurrent: perform the same task for every element of sequence, with the output being depended on the previous computations. It has memory which captures information about what has been calculated so far. Can solve long dependency problems.
8 Function f is commonly a nonlinear function such as tanh or ReLU. 8/19 Recurrent Neural Network (RNN) Figure : RNN architecture (source: ) where x t is the input at time step t s t is the hidden state at time step t which acts as memory of the network o t is the output of at step t s t = f (Ux t + Ws t 1 + b) o t = softmax(vs t ) (1)
9 9/19 Recurrent Neural Network (RNN) Figure : Different RNN architecture types. From left to right: (1) Vanilla model(i.e. image classification). (2) Sequence output (i.e. image captioning takes an image and output a sentence of words). (3) Sequence input (i.e. sentiment analysis). (4) Sequence input and sequence output (i.e. Machine translation). (5) Synced sequence input and output (i.e. pos tagging) source:
10 Recurrent Neural Network (RNN) Figure : An example RNN with 4-dimensional input and output layers, and a hidden layer of 3 units (neurons). This diagram shows the activations in the forward pass when the RNN is fed the characters hell as input. The output layer contains confidences the RNN assigns for the next character (vocabulary is h,e,l,o ); source: 10/19
11 11/19 Backpropagation through Time (BPTT)(1) Figure : Training RNN using BPTT example E 3 δw = 3 k=0 E 3 δŷ 3 ŷ 3 δs 3 s 3 s k s k W (2)
12 12/19 Vanishing Gradient Problem Figure : Training RNN using BPTT problems E 3 δw = 3 k=0 E 3 δŷ 3 ŷ 3 δs 3 s 3 s k s k W Gradient contributions from far away steps become zero, and the state at those steps doesnt contribute to what you are learning: You end up not learning long-range dependencies. (3)
13 13/19 Long Short Term Memory (LSTM) Figure : Modules in LSTM LSTM has three gates: input gate i t : control what new information will be added to the cell state. forget gate f t : decide to what extent information from the previous state is forgotten output gate o t : filter the exposure of the internal memory cell of current state
14 14/19 Long Short Term Memory (LSTM) Figure : Modules in LSTM LSTM The gating mechanism is what allows LSTMs to explicitly model long-term dependencies. By learning the parameters for its gates, the network learns how its memory should behave. Each of the gate produces an output number between 0 and 1. A 1 represents completely keep this while a 0 represents completely get rid of this.
15 15/19 Deep Learning tools Comparison of deep learning software: of deep learning software Popular tools: 1 Theano ( python based; there are lots of Theano based DL framework including Keras ( and Lasagne ( 2 Torch ( implemented in LuaJIT 3 Caffe ( written in C++ and python.
16 16/19 Example Code-Keras print ( Build_model... ) model = Sequential () model. add ( Embedding ( max_features,128, input_length = maxlen, dropout =0.2)) model. add ( LSTM (128, dropout_w =0.2, dropout_u =0.2)) model. add ( Dense (1)) model. add ( Activation ( sigmoid ))
17 17/19 Tutorials, Courses and Blogs (CNN for Visual Recognition by Andrej Karpathy) (Deep Learning for NLP by Richard Socher) (Machine Learning course by Nando de Freitas) (by LISA Lab Univ of Montreal) (Deep Learning blog by Christopher Olah) (
18 18/19 Tips Start doing experiment! dive into the code To train a Neural Network: 1 Gradient check your implementation with a small batch of data 2 During training, monitor the loss, the training/validation accuracy. Implement early stopping if necessary. 3 Tuning the hyperparameters (i.e. learning rate, number of hidden layers, hidden layer size, updater algorithm) 4 Use regularization and dropout to prevent overfitting
19 19/19 Thank you!
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