Fundamental concepts of Deep Learning

Why take this course

If you want to get to know the world of Deep Learning and the methods of building artificial and deep neural networks, this training is for you. You will learn to implement basic Deep Learning models using the Caffe framework and gain knowledge that will allow you to practically apply these methods in image and audio analysis. You will be able to distinguish Deep Learning methods from other typical AI methods, and you will understand basic concepts and algorithms.

What you'll learn

• Typical Deep Learning methods.
• How to distinguish Deep Learning methods from other typical AI methods.
• Implement basic Deep Learning models using the Caffe framework.
• Gain knowledge about practical applications related to image and audio analysis.
• The basic concepts and methods in Deep Learning.
• How to build artificial and deep neural networks.

Certification & Exam

Upon completion of the training, you will receive a certificate of participation, confirming your skills in Deep Learning. There is no final exam—all you need is active participation in the classes

Who is this course for

This course is intended for people who want to use Deep Learning methods or gain an understanding of their foundations, with an emphasis on the practical application of methods typical for the area of image recognition and sound data analysis.

Topics covered

• 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,
  • Parallelization,
  • Case study – image processing,
  • Case study – spund/voice analysis.