Practical introduction to deep learning
Deep Learning has become a widely used term in the world of artificial intelligence, thanks to rapid and significant advances in voice recognition, computer vision, and natural language processing. This branch of machine learning has attracted significant investment from the internet giants such as Google, Microsoft, Facebook and IBM.
The AlphaGo program that could beat the champion Lee Sedol in March 2016 included a significant Deep Learning component.
Deep Learning algorithms attempt to model high level abstractions in data using an artificial neural networks. Thanks to their hierarchical structure, these networks automatically construct increasingly abstract representations of data.
The advent of Deep Learning was made possible by Big Data – large amounts of data for learning; rapid growth of computing power thanks to GPUs; and finally by the better understanding of the neural network optimization techniques.
Program
This course covers practical techniques of optimization deep neural networks. Students will be able study and implement advanced learning models on complex data, through the following techniques and tools:
- Libraries Numpy, TensorFlow, Keras
- optimization techniques, transfer and regularization
- Understanding of classical model architecture and state of the art
In particular, students will implement methods for the following applications:
- Image analysis through deep convolutional networks;
- language analysis by unsupervised learning of representations of words and recurrent networks;
- other applications such as recommendation engines, generative models …
Audience and prerequisites
This course is for students who have already studied Machine Learning. It consists of many practical sessions (laptop required).
The technical prerequisites are Python language (especially in jupyter, concepts and numpy scikit-learn).
General understanding of machine learning concepts (linear and logistic regression, maximum likelihood estimation, cross-validation, and overfitting) and experience with numerical methods for linear algebra and convex optimization.
Methods of control
Continuous assessment at the beginning of practical work session, and final evaluation (coding session).