Deep learning: download the free course from the University of Geneva

Solutions based onartificial intelligence they are increasingly widespread and have become essential for supporting many professional activities and workflows in the company. The deep learning is a sub-discipline of machine learning (machine learning) which is based on deep artificial neural networks, composed of layers of interconnected nodes. These networks are capable of learning complex representations of data by processing information across multiple levels, called “layers”.

Deep learning excels at processing large amounts of complex data e unstructured, such as images, text and sounds. This allows companies to extract meaningful insights from data that was previously difficult to interpret. The deep neural networks they are particularly effective in specific tasks such as image recognition, speech recognition, machine translation and natural language recognition.

Why deep learning is so useful for professionals and businesses

L’automation is one of the keystones of deep learning: companies and professionals can exploit deep learning algorithms to automate repetitive tasks and reduce the dependence on human interventions, increasing theoperational efficiency.

Thanks to deep learning it is possible to discover patterns in the data which normally escape human observation and analysis. This can lead to new insights and business opportunitiesas well as improvements in decisions data-based.

Deep neural networks are also capable of adapting to evolving data, continually learning and improving with training based on new information. This is a precious advantage for all those business areas where, obviously, data changes continuously over time.

Companies that successfully adopt deep learning can earn a competitive advantage significant. The ability to draw useful information from data can translate into more informed and timely business decisions.

The deep learning course at the University of Geneva is free for everyone

This is why we believe it is crucial to give space to initiatives such as the one promoted by Francois Fleuretprofessor at the University of Geneva (Switzerland).

Feluret has published a detailed course on deep learning by sharing slidehandouts in PDF format, video and example code immediately usable by anyone who wants to get closer to the subject. The corsowhich ranges from the objectives of machine learning to specific deep learning techniques, is implemented using the framework PyTorch.

The “journey” begins with an overview of the fundamental concepts of machine learning and on the main challenges faced in this field. The concepts illustrated by Fleuret allow you to gain an in-depth understanding of the objectives of machine learning and the difficulties that can arise when implementing the machine learning algorithms.

A crucial chapter concerns operations on tensorifundamental data structures in the context of deep learning: it is essential to learn how to manipulate and use them.

The author of the course then delves into the concept of automatic differentiation, a key element for model optimization. The descending gradienta fundamental method for updating model weights, is also analyzed in detail.

Automatic differentiation

Automatic differentiation is a technique that allows you to automatically calculate the derivatives of a function with respect to its variables. In other words, when you have a complex function and you want to know the rate of change of that function with respect to its parameters, automatic differentiation allows you to calculate this rate without having to manually derive the function.

In the context of machine learning, automatic differentiation is critical for training models. During the training process, the goal is adjust model parameters so that theoutput is as close as possible to the correct labels (correct answers or desired output). Automatic differentiation calculates the gradient of the loss function with respect to the model parameters, which indicates the direction and magnitude of change needed to reduce the loss.

Descending gradient

The loss function it is a measure that indicates how much the model’s predictions deviate from the correct labels present in the training dataset. Model parameters are the internal variables that the model adjusts during training to reduce loss.

The descending gradient is a optimization algorithm used to minimize a loss function. The idea behind gradient descent is to iteratively adjust the model parameters to minimize the loss e improve performance during training.

Generative, recurrent, and attention-based models

The course explores the specific techniques of deep learning, providing an in-depth overview of the advanced methodologies used intraining and in the model evaluation. Students can thus immerse themselves in the use of generative modelsrecurring and based on attention (we talk about it in the article on Transformers), acquiring fundamental skills to tackle complex problems in the real world.

Generative models are designed to generate new data that resembles the data in the training set. They can be used to generate images, text, sounds, or other types of data. Examples are Generative Adversarial Networks (GAN) and Conventional Generative Networks (for example, models based on probabilistic distributions such as Gaussian generative networks).

I recurring patterns (RNN, Recurrent Neural Networks) are designed to work with sequential data and take past information into account. They can process sequences of variable length and maintain an internal state (memory) that is updated at each time step.

I models based onAttention, instead, are designed to focus on specific parts of the input during the learning process. Attention allows the model to give more importance to certain “areas” or elements in input. The model thus becomes much more flexible and capable of handling complex relationships.

Practical sessions thanks to the use of a virtual machine

Anyone who decides to take the deep learning course can try their hand at practical sessions which offer the opportunity to directly apply the concepts learned. There virtual machine (VM) that simulates a complete work environment.

Thus, students can immediately use PyTorch and carry out practical exercises through the web browser by accessing a VM preconfigurata con Linux and all the necessary tools. In this way Fleuret wanted to simplify the activities of development and testing exempting students from having to independently prepare a system suitable for the purpose.

The downside is that, obviously, the VM does not allow you to take full advantage of the hardware resources available on the host system, such as GPU.

PyTorch is an open source framework for machine learning and deep learning that offers a wide range of tools for developers and researchers. Created by Facebook AI Research Lab (FAIR), PyTorch has become one of the most popular frameworks for building andmodel training.

The requirements to follow the course

The course requires a solid understanding of some key concepts that have their roots in linear algebra, differential calculus, and Python programming, in basic probability and statistics, optimization and fundamentals of algorithms and signal processing. It is also recommended to consult the Python, Jupyter Notebook and PyTorch guides in order to acquire all the basic notions.

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