To get familiarized with core concepts and methods within the area of deep learning, along with practical applications related to image and sound analysis.
Participants of the training will learn:
- what kind of methods are typical for deep learning,
- what distinguishes deep learning methods from other typical AI approaches,
- how to implement basic deep learning models using the Caffe framework,
Discussed methods are accompanied by practical examples.
- Introduction to Atrificial Neural Nets
- Machine Learning,
- The concept of an Artificial Neural Network (ANN),
- Building blocks and architecture of an ANN,
- Mathematical formulation of an ANN,
- Learning algorithms for an ANN,
- The concept of overfitting,
- Theory of Vapnik and Chervonenkis – empirical and structural risk,
- Implementation of an ANN.
- Introduction to Deep Networks
- Shallow learning, feature engineering, deep learning,
- Fundamentals of Deep Learning,
- Convolutional Neural Networks,
- Convolutions, filters and the pooling principle,
- Deep Network’s architecture,
- Feed-forward and recurrent networks,
- Region-based convolutional networks for image analysis,
- A Hidden Markov Model for speech analysis,
- Long Short-Term Memory Network (LSTM),
- Long-term Recurrent Convolutional Network (LRCN),
- Deep Denoising Autoencoders,
- Deep Belief Networks, Deep Boltzmann Machines,
- Datasets for image recognition,
- Datasets for sound and voice analysis.
- Introduction to Caffe with applications
- Working with Caffe,
- Implementation of a Deep Network,
- Training a network,
- Fine-tuning a network,
- Using pre-trained models,
- Case study – image processing,
- Case study – spund/voice analysis.
People who want to use deep learning methods or who want to acquire understanding of fundamentals of deep learning methods, with a practical focus on applications of such methods typical for the area of image recognition and sound data analysis.