Deep Learning Fundamentals
Master essential deep learning concepts and practical applications in this comprehensive course developed by world-renowned AI experts at IVADO and Mila.
Master essential deep learning concepts and practical applications in this comprehensive course developed by world-renowned AI experts at IVADO and Mila.
This comprehensive course provides a thorough introduction to deep learning fundamentals, developed by leading institutions IVADO and Mila. Students learn core concepts of neural networks, exploring applications in computer vision, natural language processing, and decision-making. The curriculum covers essential topics from basic machine learning principles to advanced neural network architectures, including practical tutorials and important discussions on AI ethics and bias. Designed for professionals and academics with basic programming and math knowledge, the course offers hands-on experience with deep learning libraries and real-world applications.
4.5
(33 ratings)
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What you'll learn
Understand fundamental concepts and terminology of deep learning
Identify appropriate neural network architectures for specific problems
Gain practical experience with deep learning libraries and tools
Master basic concepts of convolutional and recurrent neural networks
Understand sequence-to-sequence models and natural language processing
Learn to address bias and discrimination in machine learning applications
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This course provides a comprehensive introduction to deep learning, covering both theoretical foundations and practical applications. The curriculum includes machine learning fundamentals, neural network architectures, convolutional neural networks for computer vision, recurrent neural networks for sequence processing, and important ethical considerations regarding bias in AI systems. Students gain hands-on experience through tutorials and practical sessions using modern deep learning libraries. The course emphasizes understanding core concepts while providing practical implementation skills.
Machine Learning (ML) and Experimental Protocol
Module 1
Introduction to Deep Learning
Module 2
Intro to Convolutional Neural Networks
Module 3
Introduction to Recurrent Neural Networks
Module 4
Bias and Discrimination in ML
Module 5
Fee Structure
Instructors
1 Course
Distinguished Machine Learning Expert and NLP Researcher
Mirko Bronzi serves as Senior Applied Research Scientist at Mila-Quebec, where he specializes in Natural Language Processing and speech processing technologies. His research journey began with a PhD focused on web information extraction and integration, and has evolved to include significant contributions to speech separation and natural language understanding. His work spans both academic research and practical applications, particularly in virtual assistants and clinical language understanding for doctor-patient interactions. His most cited research includes groundbreaking work on speech separation using attention mechanisms and machine learning benchmarking. His recent publications have advanced the field of self-attention mechanisms in speech processing, while his earlier work established important foundations in web data extraction and integration. At Mila, he contributes to developing AI solutions for businesses and organizations, focusing on natural language processing applications and speech technologies.
3 Courses
Distinguished AI Ethics Pioneer and Fairness Researcher
Golnoosh Farnadi serves as Assistant Professor at McGill University's School of Computer Science and Adjunct Professor at Université de Montréal, while holding a Canada CIFAR AI Chair at Mila. Her research journey began with a PhD in Computer Science from KU Leuven and Ghent University, focusing on user modeling in social media. Her work has evolved to address critical issues in AI ethics, particularly algorithmic fairness and responsible AI. As founder and principal investigator of the EQUAL lab at Mila/McGill, she develops novel algorithmic designs that incorporate fairness, robustness, and privacy considerations. Her contributions have been recognized through numerous awards, including the 2023 Google Excellence Award, 2021 Google Scholar Award, and Facebook Research Award for Privacy Enhancing Technologies. Her recent work includes collaborations with UNESCO on AI governance and development of privacy-preserving fair item ranking systems. As a visiting faculty researcher at Google and co-director of McGill's Collaborative for AI & Society, she continues to advance the field of ethical AI while advocating for more democratic and less centralized approaches to AI development.
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4.5 course rating
33 ratings
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