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Title |
Date |
Slides(HTML) |
Slides(Code) |
Recording |
0 |
Lecture 0 - PyTorch Intro, tensors, views, indexing |
Jan 21 |
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1 |
Lecture 1 - PyTorch storage, indexing, cpu/gpu data types, functions |
Jan 23 |
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2 |
Lecture 2 - Linear algebra I |
Jan 28 |
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3 |
Lecture 3 - Linear algebra II & Matrix algebra |
Jan 30 |
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4 |
Lecture 4 - Matrix calculus II and computation graphs |
Feb 04 |
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5 |
Lecture 5 - Gradient descent |
Feb 06 |
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6 |
Lecture 6 - Primal optimization |
Feb 11 |
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7 |
Lecture 7 - Linear regression I |
Feb 13 |
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8 |
Lecture 8 - Linear regression II |
Feb 18 |
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9 |
Lecture 9 - Logistic regression |
Feb 20 |
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10 |
Lecture 10 - Multi-class logistic regression |
Feb 25 |
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11 |
Lecture 11 - PyTorch optimizers, datasets, dataloaders I |
Feb 27 |
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12 |
Lecture 12 - PyTorch optimizers, datasets, dataloaders II |
Mar 04 |
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13 |
Lecture 13 - Review for MT1 |
Mar 06 |
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14 |
Lecture 14 - Deep Nets I - Simple, multi-layer networks |
Mar 25 |
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15 |
Lecture 15 - Deep Nets II - Convolutional networks |
Mar 27 |
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16 |
Lecture 16 - Deep Nets III - Recurrent networks |
Apr 01 |
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17 |
Lecture 17 - Principal component analysis |
Apr 03 |
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18 |
Lecture 18 - K-means clustering, gaussian mixture models |
Apr 08 |
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19 |
Lecture 19 - Generative adversarial networks |
Apr 10 |
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20 |
Lecture 20 - Object detection, semantic segmentation |
Apr 15 |
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21 |
Lecture 21 - Words and attention |
Apr 17 |
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22 |
Lecture 22 - Transformer models |
Apr 22 |
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23 |
Lecture 23 - Large language models I - architectures and training |
Apr 24 |
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24 |
Lecture 24 - Large language models II - fine-tuning methods |
Apr 29 |
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25 |
Lecture 25 - Course recap and celebration |
May 06 |
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