This is a two-semester-long course primarily designed for graduate students. However, undergraduate students with demonstrated strong backgrounds in probability, statistics (e.g., linear & logistic regressions), numerical linear algebra and optimization are also welcome to register. We will be pursuing the objective of familiarizing the students with state-of-the-art deep learning techniques employed in the industry. Deep learning is a field that has been witnessing a mini-revolution every few months. It is therefore very important that the students registering for this course are eager to learn new concepts. So much of deep learning is just software engineering. Consequently, the students should be able to write clean code while doing their assignments. Python will be the programming language used in this course. Familiarity with TensorFlow and PyTorch is a plus but is not a requirement. However, it is very important that the students are willing to do the hard work to learn and use these two frameworks as the course progresses.

• Training Deep Neural Networks (Lecture Notes) (YouTube Playlist)
• Computer Vision
  • Image Classification
    • Large Networks (Lecture Notes) (YouTube Playlist)
    • Small Networks (Lecture Notes) (YouTube Playlist)
    • AutoML (Lecture Notes) (YouTube Playlist)
    • Robustness (Lecture Notes) (YouTube Playlist)
    • Visualizing & Understanding (Lecture Notes) (YouTube Playlist)
    • Transfer Learning (Lecture Notes) (YouTube Playlist)
    • Domain Adaptation (Lecture Notes)
    • Few Shot Learning (Lecture Notes)
    • Federated Learning (Lecture Notes)
    • Contrastive Learning (Lecture Notes)
  • Image Transformation
    • Semantic Segmentation (Lecture Notes) (YouTube Playlist)
    • Super-Resolution, Denoising, Colorization, and Depth Estimation (Lecture Notes) (YouTube Playlist)
    • Pose Estimation (Lecture Notes)
  • Object Detection
    • Two Stage Detectors (Lecture Notes) (YouTube Playlist)
    • One Stage Detectors (Lecture Notes) (YouTube Playlist)
  • Face Recognition and Detection (Lecture Notes)
  • Video (Lecture Notes) (YouTube Playlist)
  • 3D (Lecture Notes) (YouTube Playlist)

• Natural Language Processing
  • Word Representations (Lecture Notes) (YouTube Playlist)
  • Text Classification (Lecture Notes) (YouTube Playlist)
  • Neural Machine Translation (Lecture Notes) (YouTube Playlist)
  • Language Modeling (Lecture Notes) (YouTube Playlist)
• Multimodal Learning (Lecture Notes) (YouTube Playlist)
• Generative Networks (Lecture Notes) (YouTube Playlist)
• Speech & Music (Lecture Notes) (YouTube Playlist)
• Reinforcement Learning (Lecture Notes) (YouTube Playlist)
• Graph Neural Networks (Lecture Notes) (YouTube Playlist)
• Recommender Systems (Lecture Notes)