Deep Learning Essentials
Gain a good understanding of what Deep Learning is, what types of problems it resolves, and what are the fundamental concepts and methods it entails. The course developed by IVADO, Mila and Université de Montréal offers diversified learning tools for you to fully grasp the extent of this ground-breaking cross-cutting technology, a critical need in the field.
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Gain a good understanding of what Deep Learning is, what types of problems it resolves, and what are the fundamental concepts and methods it entails. The course developed by IVADO, Mila and Université de Montréal offers diversified learning tools for you to fully grasp the extent of this ground-breaking cross-cutting technology, a critical need in the field.
Gain a good understanding of what Deep Learning is, what types of problems it resolves, and what are the fundamental concepts and methods it entails. The course developed by IVADO, Mila and Université de Montréal offers diversified learning tools for you to fully grasp the extent of this ground-breaking cross-cutting technology, a critical need in the field.
IVADO, a scientific and economic data science hub bridging industrial, academic and government partners with expertise in digital intelligence designed the course, and the world-renowned Mila, rallying researchers specialized in Deep Learning, created the content.
This course is based on presentations from an event held in Montreal, from September 9 to 13, 2019. It was adapted to an online course (MOOC) format and was released, for the first time, in March 2020. The tutorials' material was updated on Colab Notebook in Spring of 2021.
Mila’s founder and IVADO’s scientific director, Yoshua Bengio, also a professor at Université de Montréal, is a world-leading expert in artificial intelligence and a pioneer in deep learning as well as the scientific director of this course. He is also a joint recipient of the 2018 A.M. Turing Award, “the Nobel Prize of Computing”, for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
Deep Learning is an extension of Machine Learning where machines can learn by experience without human intervention. It is largely influenced by the human brain in the fact that algorithms, or artificial neural networks, are able to learn from massive amounts of data and acquire skills that a human brain would. Thus, Deep learning is now able to tackle a large variety of tasks that were considered out of reach a few years ago in computer vision, signal processing, natural language processing, robotics, and sequential decision-making. Because of these recent advances, various industries are now deploying deep learning models that impact various economic sectors such as transport, health, finance, energy, as well as our daily life in general.
If you are a professional, a scientist or an academic with basic knowledge in mathematics and programming, this MOOC is designed for you! Atop the rich Deep Learning content, discover issues of bias and discrimination in machine learning and benefit from this sociotechnical topic that has proven to be a great eye-opener for many.
What's inside
Learning objectives
- At the end of the mooc, participants should be able to:
- Understand the basics and terminology related to deep learning
- Identify the types of neural networks to use to solve different types of problems
- Get familiar with deep learning libraries through practical and tutorial sessions
- At the end of the mooc, participants should be able to:
- Understand the basics and terminology related to deep learning
- Identify the types of neural networks to use to solve different types of problems
- Get familiar with deep learning libraries through practical and tutorial sessions
Syllabus
MODULE 1 Machine Learning (ML) and Experimental Protocol
Introduction to ML
ML Tools
MODULE 2 Introduction to Deep Learning
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Syllabus
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Activities
Review Machine Learning
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Deep Learning extends the principles and methods of classical ML. Review fundamental ML concepts for a more solid foundational base for understanding DL.
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Machine Learning
Show steps
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Review sklearn and TensorFlow resources
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Implement a basic ML model using a library
-
Practice using supervised and unsupervised learning methods
Exercises on Backpropagation
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Practice is essential for a deep understanding of the backpropagation algorithm, which is used in DL to efficiently update weights.
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Backpropagation
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-
Solve backpropagation exercises
-
Implement backpropagation in code
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Apply backpropagation to a small dataset
Develop a Convolutional Neural Network
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CNN is a core neural network type for processing data with grid-like structure, like images. Guided tutorials on CNN can greatly assist in understanding how images can be processed and understood by machines.
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Convolutional Neural Network
Show steps
-
Follow a CNN tutorial
-
Study the architecture of a CNN
-
Implement a CNN using a library
-
Apply a pre-trained CNN model to an image dataset
Show all three activities
Review Machine Learning
Show steps
Deep Learning extends the principles and methods of classical ML. Review fundamental ML concepts for a more solid foundational base for understanding DL.
Browse courses on
Machine Learning
Show steps
- Review sklearn and TensorFlow resources
- Implement a basic ML model using a library
- Practice using supervised and unsupervised learning methods
Exercises on Backpropagation
Show steps
Practice is essential for a deep understanding of the backpropagation algorithm, which is used in DL to efficiently update weights.
Browse courses on
Backpropagation
Show steps
- Solve backpropagation exercises
- Implement backpropagation in code
- Apply backpropagation to a small dataset
Develop a Convolutional Neural Network
Show steps
CNN is a core neural network type for processing data with grid-like structure, like images. Guided tutorials on CNN can greatly assist in understanding how images can be processed and understood by machines.
Browse courses on
Convolutional Neural Network
Show steps
- Follow a CNN tutorial
- Study the architecture of a CNN
- Implement a CNN using a library
- Apply a pre-trained CNN model to an image dataset
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