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Machine Learning in Python - Neural networks, data leakage, the train/test split

翱惫别谤惫颈别飞:听One of the most discussed and perhaps mysterious machine learning models is the neural network. Neural networks are a kind of machine learning model inspired by biological processes taking place in the brain. This lesson will demystify neural networks and provide you with a plain-English explanation of how they work. We will train a neural network to recognize handwritten digits; this is a classification task. We will also discuss deep learning and further explore the training step in the machine learning pipeline.

Learning Goal(s): By the end of the workshop, participants will be able to:

• Given a scaffolded environment and curated data set, follow a tutorial that trains a neural network to perform classification.
• Describe in plain English the structure of neural networks in general.
• Appreciate the use of backpropagation for training neural networks.
• Articulate the common pitfalls in training and validating machine learning models.

笔谤别谤别辩蝉:听Participants should already have some familiarity with Python programming fundamentals, e.g. loops, conditional execution, importing modules, and calling functions. Furthermore, participants should ideally have attended the first lesson in the 鈥淔undamentals of Machine Learning in Python鈥� series, or they should already have some background on the general machine learning pipeline.

Approach:聽Our approach is primarily student-centered. Students will work in pairs and small groups on worksheets and Jupyter notebooks, interspersed with brief lecture and instructor-led live-coding segments.

Supporting Resources:聽We will refer to many of the materials used previously in our series 鈥淐omputing Workshop鈥�

Deliverables:聽Our resources will be made available via our web site.

Resources required:聽Participants should have access to a laptop computer. Python should be already installed with Anaconda.

Location:听贬驰叠搁滨顿.听,听room 325, and via Zoom.
滨苍蝉迟谤耻肠迟辞谤:听, Faculty Lecturer in Computer Science at McGill University. Eric Mayhew, Computer Science professor at Dawson College.

Registration: