In an era where artificial intelligence (AI) is at the forefront of technological and economic advancement, understanding its intricacies has become crucial for professionals across various sectors. This intermediate-level course aims to equip participants with a comprehensive understanding of deep learning, a pivotal branch of AI responsible for significant breakthroughs in the digital world. Targeted at any individuals with a basic understanding of Python programming and mathematics, the training seeks to demystify the scientific and technological foundations of deep learning, including linear algebra, calculus, and software engineering, alongside practical skills in using deep learning frameworks like PyTorch and Keras.

The course covers essential concepts from the basics of neural networks, activation functions, and data handling, to the application of various architectures like ANNs, CNNs and Transformers. It emphasizes hands-on learning through exercises, projects, and interactive discussions, ensuring participants can design, implement, and refine neural networks effectively. Moreover, it addresses the social and ethical dimensions of AI, preparing attendees to make responsible decisions in AI deployment or use. This blend of theoretical knowledge and practical application, set in an on-site format conducive to immersive learning, makes the course an invaluable opportunity for those looking to deepen their expertise in AI or integrate AI solutions into their work, fostering a future-ready skill set in the rapidly evolving landscape of artificial intelligence.

This course will cover:

• Scientific and technological foundations
• Basic concepts of deep learning
• Main architectures of deep learning
• Mini-Project – Applying the learned concepts
• Ethics and society – Organizing to live with AI

• Discovering the Scientific and Technological foundations
  
• Understanding the basic concepts of deep learning
  
• Understanding the main architectures of deep learning
  
• MINI-PROJECT - PART 1 - Apply the learned notions of deep learning in the context of a mini-project
  
• MINI-PROJECT - PART 2 - Apply the learned notions of deep learning in the context of a mini-project
  
• Understanding how societies can organize themselves to live with AI
  

After completion of this training, you will be able to:

• Design, implement, and refine neural networks, including setting and adjusting parameters like weights, biases, learning rates, and understanding the mechanics of forward and backward propagation, dropout, and data augmentation techniques.
  
• Apply various deep learning architectures such as ANNs, LSTMs, Transformers, CNNs, GANs, and Vision Transformers to appropriate domains, such as natural language processing, image recognition, and generative tasks.
  
• Demonstrate expertise in managing datasets, including splitting into training, validation, and test sets, and competently training models while optimizing performance and preventing over-fitting.
  
• Understand the social and ethical implications of AI technologies, ensuring responsible and ethical decision-making in the development and deployment of AI solutions
  
• Use Jupyter Notebooks as a versatile tool for developing, documenting, and testing deep learning models in an interactive environment.
  

Practice based learning of concepts, methods and solutions.