introduzione deep learning & tensorflow

TensorFlow Dev Summit Ext Roma 2017

16 Febbraio 2017, Talent Garden - Cinecittà

Agenda

18:40-19:00 TensorFlow: one year of excitingbreakthroughs (Simone Scardapane)

19:00-20:30 Keynote streaming

20:30-21:30 Networking

Backpropagation

Backpropagation: how it works [YouTube]

Inception

Gooing Deeper Into Convolutions [Google Research Blog]

TensorFlow

Symbolic model definition

Automatic gradient computation

Efficient CPU/GPU computations

An example (with Keras)

Create a simple model with two layers:

model = Sequential() model.add(Dense(20, input_dim=16, init='uniform', activation='relu'))model.add(Dense(1, init='uniform', activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])model.fit(X, Y, nb_epoch=50, batch_size=100)

Train the model:

Neural Image Captioning

Download TensorFlow Code

Neural Image Captioning (2)

Source: GitHub Repository

Neural Machine Translation

A Neural Network for Machine Translation, at Production Scale

Neural Machine Translation (2)

A Neural Network for Machine Translation, at Production Scale

Generative Adversarial Networks

An introduction to Generative Adversarial Networks (with code in TensorFlow)

AlphaGo

How AlphaGo Mastered the Game of Go with Deep Neural Networks

WaveNet

WaveNet: A Generative Model for Raw Audio

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