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DESCRIPTION:Machine Learning in Python - Neural networks\, data leakage\, t
 he train/test split\n\nOverview: One of the most discussed and perhaps mys
 terious 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 ne
 ural network to recognize handwritten digits\; this is a classification ta
 sk. We will also discuss deep learning and further explore the training st
 ep in the machine learning pipeline.\n\nLearning Goal(s): By the end of th
 e workshop\, participants will be able to:\n\n\n	Given a scaffolded environ
 ment and curated data set\, follow a tutorial that trains a neural network
  to perform classification.\n	Describe in plain English the structure of ne
 ural networks in general.\n	Appreciate the use of backpropagation for train
 ing neural networks.\n	Articulate the common pitfalls in training and valid
 ating machine learning models.\n\n\nPrereqs: Participants should already h
 ave some familiarity with Python programming fundamentals\, e.g. loops\, c
 onditional execution\, importing modules\, and calling functions. Furtherm
 ore\, participants should ideally have attended the first lesson in the “F
 undamentals of Machine Learning in Python” series\, or they should already
  have some background on the general machine learning pipeline.\n\nApproac
 h: 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.\n\nSupporting Resou
 rces: We will refer to many of the materials used previously in our series
  “Computing Workshop” https://computing-workshop.com/\n\nDeliverables: Our
  resources will be made available via our web site.\n\nResources required:
  Participants should have access to a laptop computer. Python should be al
 ready installed with Anaconda.\n\nLocation: HYBRID. McIntyre Medical Build
 ing\, room 325\, and via Zoom.\n	Instructor: Jacob Errington\, Faculty Lect
 urer in Computer Science at ���ϲ����� University. Eric Mayhew\, Computer Scie
 nce professor at Dawson College.\n\nRegistration: Register Here\n
DTSTART:20241114T150000Z
DTEND:20241114T170000Z
SUMMARY:Workshop: Machine Learning in Python - Session 4
URL:/cdsi/channels/event/workshop-machine-learning-pyt
 hon-session-4-360404
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