Neural Networks 1. Introduction Spring 2021 1 Logistics: By now you must have… • Already watched lecture 0 (logistics) – Ifnotdosoatonce • Been to the course website – http://deeplearning.cs.cmu.edu – If you have not done so, please visit it at once • Course objectives, logistics, quiz and homework policies, and grading policies, all have been […]
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Introduction to Deep Learning Angelica Sun (adapted from Atharva Parulekar, Jingbo Yang) Overview ● Motivation for deep learning ● Convolutional neural networks ● Recurrent neural networks ● Transformers ● Deep learning tools But we learned multi-layer perceptron in class? Expensive to learn. Will not generalize well. Does not exploit the order and local relations in
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Introduction to Deep Learning Introduction to Deep Learning Angelica Sun (adapted from Atharva Parulekar, Jingbo Yang) Overview Motivation for deep learning Convolutional neural networks Recurrent neural networks Transformers Deep learning tools But we learned multi-layer perceptron in class? Expensive to learn. Will not generalize well. Does not exploit the order and local relations in the
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AWS Educate Student Classrooms Guide AWS Educate members now have access to expanded features! These new features provide a free, secure, no- cost virtual learning environment for students to leverage AWS for classroom projects and assignments. If your educator is using the AWS Educate Classrooms feature, he or she can invite you to join a
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RMIT Classification: Trusted Introduction COSC 2673-2793 | Semester 1 2021 (Computational) Machine Learning Image: Freepik.com RMIT Classification: Trusted Agenda • Introduction – Teaching Team. • Overview of the course. • Foundations of ML. By the end of the lecture, you will: • Understand what the course is about and the assignment structure. • Have a
COSC 2673/2793 | Machine Learning Week 3 Lab Exercises: **Dataset splitting & Pre-Processing** Introduction Last week we learned how to read the data and do exploratory data analysis (EDA). The next step in a typical machine learning pipeline is to split data and transform so that we can feed the data to a learning algorithm.
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COSC 2673/2793 | Machine Learning Week 4 Lab Exercises: **Training a Regression Model** Introduction During the last couple of weeks we learned about how to read data, do exploratory data analysis (EDA) and prepare data for training. The next step in a typical machine learning pipeline is to train the model and to investigate the
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COSC 2673/2793 | Machine Learning Week 1 Lab Exercises: **Introduction to Python** We will be using the Python programming language for all lab exercises & assignments in this course. Python is a great general-purpose programming language on its own, but with the help of a few popular libraries (numpy, matplotlib, scikit-learn) it becomes a powerful
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COSC 2673/2793 | Machine Learning Week 5 Lab Exercises: **Training a Classification Model & Typical ML process** Introduction During the last couple of weeks we learned about how to read data, do exploratory data analysis (EDA) and prepare data for training and training a ML model. However, we did not specifically discuss the typical ML
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COSC 2673/2793 | Machine Learning Week 6 Lab Exercises: **Decision Trees** Introduction During the last couple of weeks we learned about the typical ML model development process. In this weeks lab we will explore decision tree based models. The lab assumes that you have completed the labs for week 2-5. If you havent yet, please