2023S-AIKU-D2D is the material (lecture notes, exercises and assignments) repository for 2023 Summer DeepintoDeep (D2D), a deep learning bootcamp for juniors learning and experiencing various deep learning topics and its applications with PyTorch at AIKU.
[2023/08/08] Now we provide links for lecture videos. The curriculum is slightly modified.
[2023/08/08] Slides for Lecture 7 ~ 9 are uploaded. Assignment 3 is uploaded.
[2023/07/28] Slides for Lecture 1 ~ 6 are uploaded. Assignment 1 ~ 2 are uploaded.
- 1. Machine Learning and Multi-layer Perceptrons - Donghwan Chi
- 2. Convolutional Neural Networks - Donghwan Chi
- Assignment #1 - Hello PyTorch, Hello CNN!
- 3. Recurrent Neural Networks - Minseong Kim
- 4. Attention & Transformer - Minseong Kim
- 5. Applications of Transformer - Minseong Bae
- Assignment #2 - Naver Movie Review Sentiment Analysis w/ Transformer
- 6. Techniques for Training Neural Networks - Seongchan Kim
- 7. CV Focus I : Object Detection - Minseong Bae
- 8. CV Focus I : Image Segmentation - Seunghyun Kim
- Assignment #3 - Object Detection w/ YOLO and Faster R-CNN
- 9. NLP Focus I : More about Transformers and Pretraining - Hyejin Jo
- 10. NLP Focus I : Various NLP Tasks (QA, Retrieval, etc.) - Hyejin Jo
- Assignment #4 - Korean Question Answering & Generation
- 11. CV Focus II : Generative Models - Seunghyun Kim
- 12. CV Focus II : 3D Vision - Minseong Bae
- 13. NLP Focus II : Generative Models - Soobin Park
- 14. NLP Focus II : Large Language Models - Soobin Park
- Assignment #5 - Toy Project
More information is available at curriculum.pdf.
- 2023W AIKU DeepIntoDeep
- Stanford cs231n
- Stanford cs224n
- UMich EECS 498.008 / 598.008 Deep Learning for Computer Vision
The class is licensed under the MIT License:
Copyright © 2023 AIKU.
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.