### Valerio Maggio: _PostDoc Data Scientist @ FBK/MPBA_ ### Contacts:

@leriomaggio

vmaggio@fbk.eu

## Installed Versions :::python import keras print('keras: ', keras.__version__) # optional import theano print('Theano: ', theano.__version__) import tensorflow as tf print('Tensorflow: ', tf.__version__) keras: 2.0.4 Theano: 0.9.0 Tensorflow: 1.2.1 ## Outline - **Part I**: **Introduction** - Intro to Artificial Neural Networks - Perceptron and MLP - naive pure-Python implementation - fast forward, sgd, backprop - Introduction to Deep Learning Frameworks - Intro to Theano - Intro to Tensorflow - Intro to Keras - Overview and main features - Overview of the `core` layers - Multi-Layer Perceptron and Fully Connected - Examples with `keras.models.Sequential` and `Dense` - Keras Backend - **Part II**: **Supervised Learning** - Fully Connected Networks and Embeddings - Intro to MNIST Dataset - Hidden Leayer Representation and Embeddings - Convolutional Neural Networks - meaning of convolutional filters - examples from ImageNet - Visualising ConvNets - Advanced CNN - Dropout - MaxPooling - Batch Normalisation - HandsOn: MNIST Dataset - FC and MNIST - CNN and MNIST - Deep Convolutiona Neural Networks with Keras (ref: `keras.applications`) - VGG16 - VGG19 - ResNet50 - Transfer Learning and FineTuning - Hyperparameters Optimisation - **Part III**: **Unsupervised Learning** - AutoEncoders and Embeddings - AutoEncoders and MNIST - word2vec and doc2vec (gensim) with `keras.datasets` - word2vec and CNN - **Part IV**: **Recurrent Neural Networks** - Recurrent Neural Network in Keras - `SimpleRNN`, `LSTM`, `GRU` - LSTM for Sentence Generation - **PartV**: **Additional Materials**: - Custom Layers in Keras - Multi modal Network Topologies with Keras