deep_learning_lectures
This repository contains a miscellaneous collection of lectures and lab lectures on deep learning prepared for teaching activity during my PhD @ University of Modena and Reggio Emilia.
2017 - Lectures on Deep Learning @ UniMoRe, Italy
Crash course on Deep Learning and Temporal Data Processing.
THEORY
- 0_gradient_descent.pdf - slides on gradient descent optimization.
- 1_deep_neural_networks.pdf - slides on neural network and deep neural networks.
- 2_conv_neural_networks.pdf - slides on convolutional neural networks.
- 3_recurrent_neural_networks.pdf - slides on recurrent neural networks.
PRACTICE
SLIDES
- tensorflow_basics.pdf - slides on TensorFlow basics.
- linear_regression.pdf - Implementing a linear regression model in TensorFlow.
- neural_network.pdf - Implementing a fully connected network to classify MNIST digits.
- convnets.pdf - Implementing a convolutional network for classification on MNIST and segmentation on TilesDataset.
- lstm.pdf - Implementing a LSTM to count ones in binary sequences.
CODE
- lab00 - linear_regression
- lab01 - neural_networks
- lab02 - convnets for classification on MNIST
- lab03 - convnets for segmentation on TilesDataset
- lab03 - lstm_sequence_counting
LaTeX source
- Whole LaTeX source is available in repository in 2017_MASTER