60.001 Applied Deep Learning

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Course Description

Deep neural networks have revolutionized the landscape of modern day AI. In this course, we will take an applied approach and dive into deep neural networks: concepts, how they work, and how the students can implement them for concrete, practical problems. Students are expected to gain experience in tackling a complete deep learning project in PyTorch, from data gathering and augmentation to implementing and running a deep neural network. They will learn fundamental concepts in machine learning and neural networks such as transfer learning, data loaders, and others.

Learning Objectives

  • Be aware of the main goals of deep learning, its main application domains and current challenges
  • Design and choose appropriate deep models for solving different types of predictive modelling problems
  • Analyse model performance and interpret it to optimize performance
  • Implement cutting edge deep learning models on a range of practical problems in PyTorch
  • Explain the workings of neural networks and deep learning technologies
  • Appreciate the technical skills necessary to be a capable deep learning scientist

Measurable Outcomes

  • Identify important concepts and current challenges in deep learning
  • Design model architectures for different applications with different modalities such as image, text and time series data
  • Implement deep models in PyTorch
  • Evaluate the performance of deep learning models and optimize their performance
  • Explain the workings and pros/cons of different cutting-edge deep learning models
  • Implement a fully working deep learning project from scratch including data processing and model optimization

Prerequisite

Mutually Exclusive Subject

Textbook(s) and/or Other Required Material

  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
  • Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2021). Dive into deep learning. arXiv preprint arXiv:2106.11342.
2022-03-02T12:31:47+00:00