The main objective of this course is to teach concepts and implementation of deep learning techniques for scientific and engineering problems to first year graduate students. This course entails various methods, including theory and implementation of deep leaning techniques to solve a broad range of computational problems frequently encountered in solid mechanics, fluid mechanics, non destructive evaluation of materials, systems biology, chemistry, and non-linear dynamics.
Over the 13 weeks of this course (including reading period), students will spend three hours in class per week (39 hours total). A reasonable estimate to support this course’s learning outcomes is 180 hours total. Project based homework assignments may take ~60 hours, and students are expected to allocate ~80 hours to the final project.
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Dr. Khemraj Shukla, Division of Applied Mathematics, Brown University
Dr. Khemraj Shukla: Friday: 3.00 PM - 5.30 PM
Room No: 308
Division of Applied Mathematics
170 Hope St
Providence RI 02906
Email: khemraj_shukla@brown.edu
Aniruddha Bora
Email: aniruddha_bora@brown.edu
Textbook and Other Reading Materials
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Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
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Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow by Auréliean Géron
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Conceptual Programming with Python by Thorsten Altenkirch and Isaac Triguero
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Reinforcement learning: An Introduction, 2nd edition by Richard S Sutton and Andrew G Barto
Learning curve
Every Friday: 3:00 PM - 5.30 PM
Room No: 118
1st Floor, Division of Applied Mathematics
170 Hope St
Providence RI 02906
Lecture 1 : Introduction Slides: (Jan 27,2025)
Homework_L1 Due Date: 2/10/2025, 11:59 PM ET
Lecture 2 : A primer on Python, NumPy, SciPy, jupyter notebooks and MATLAB Slides: (Feb 3, 2025) Jupyter Notebook MATLAB_Codes
Homework_L2 Due Date: 2/21/2025, 11:59 PM ET
Lecture 3: Deep Learning Networks Slides: (Feb 3, Feb 10, 2025) Jupyter NotebookMATLAB_Codes