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Learned knowledge and techniques in Deep Learning and also related tools: Python, Pytorch, Jupyter Notebook, RNN, CNN, Reinforcement Learning, LSTM, BERT, Language Modeling

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CS583_Deep-Learning

General information

  • Course Title: Deep Learning

  • Course Code: CS 583

  • Academic Level: Graduate

  • Instructor: Jia Xu & Abdul Rafae Khan

  • Department: Computer Science

  • University: Stevens Institute of Technology

  • Course Period: Fall Semester in 2023 (Sep 2023 - Dec 2023)

Course description

Deep learning (DL) is a family of the most powerful and popular machine learning (ML) methods and has wide realworld applications such as face recognition, machine translation, self-driving car, recommender system, playing the Go game, etc. This course is designed for students either with or without ML background. The course will cover fundamental ML, computer vision, and natural language problems and DL tools for solving the problems. The students will be able to use DL methods for solving real-world ML problems. Knowledge and skills in Python programming and linear algebra are strictly required. Probability theory, statistics, and numerical analysis are recommended by not required. Knowledge in machine learning and artificial intelligence is helpful but unnecessary. Pre-Req: Undergrad linear algebra and probability.

Skills

  • Programming: Python
  • Libraries: Transformer
  • Software: Jupyter Notebook, Google Colab
  • ML Skills: Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Language Modelling, Long-Short Term Memory (LSTM), Bidirectional Encoders Reporesentations from Transformer (BERT), Reinforcement Learning

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Learned knowledge and techniques in Deep Learning and also related tools: Python, Pytorch, Jupyter Notebook, RNN, CNN, Reinforcement Learning, LSTM, BERT, Language Modeling

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