{"id":19230094,"url":"https://github.com/changkun/ws-18-19-deep-learning-tutorial","last_synced_at":"2025-10-06T01:28:44.798Z","repository":{"id":96324062,"uuid":"148030779","full_name":"changkun/ws-18-19-deep-learning-tutorial","owner":"changkun","description":"Deep Learning and Artificial Intelligence Tutorial @ LMU WS 2018/19","archived":false,"fork":false,"pushed_at":"2019-02-01T16:39:21.000Z","size":25497,"stargazers_count":14,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-01T08:11:43.188Z","etag":null,"topics":["artificial-intelligence","convolutional-neural-networks","deep-learning","generative-adversarial-network","markov-decision-processes","recurrent-neural-networks","reinforcement-learning","representation-learning"],"latest_commit_sha":null,"homepage":"https://changkun.github.io/ws-18-19-deep-learning-tutorial/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/changkun.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-09-09T13:58:33.000Z","updated_at":"2024-11-24T16:30:20.000Z","dependencies_parsed_at":null,"dependency_job_id":"b3f52d51-2542-4006-b8eb-15cee51a10c4","html_url":"https://github.com/changkun/ws-18-19-deep-learning-tutorial","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/changkun%2Fws-18-19-deep-learning-tutorial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/changkun%2Fws-18-19-deep-learning-tutorial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/changkun%2Fws-18-19-deep-learning-tutorial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/changkun%2Fws-18-19-deep-learning-tutorial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/changkun","download_url":"https://codeload.github.com/changkun/ws-18-19-deep-learning-tutorial/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249990815,"owners_count":21357157,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","convolutional-neural-networks","deep-learning","generative-adversarial-network","markov-decision-processes","recurrent-neural-networks","reinforcement-learning","representation-learning"],"created_at":"2024-11-09T15:36:51.639Z","updated_at":"2025-10-06T01:28:39.777Z","avatar_url":"https://github.com/changkun.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# \"Deep Learning and Artificial Intelligence\" Tutorial\n\nDeep Learning and Artificial Intelligence Tutorial @ LMU WS 2018/19 \n\n\u003e University of Munich, Winter Semester 2018/19, [Course Homepage](http://www.dbs.ifi.lmu.de/cms/studium_lehre/lehre_master/deep1819/index.html)\n\u003e\n\u003e - **Responsible Professor**: [Prof. Dr. Matthias Schubert](http://www.dbs.ifi.lmu.de/cms/personen/professoren/schubert/index.html)\n\u003e - **Lecturers**: Dr. Florian Büttner, Dr. Markus Geipel, Pankaj Gupta, Dr. Denis Krompass, [Prof. Dr. Matthias Schubert](http://www.dbs.ifi.lmu.de/cms/personen/professoren/schubert/index.html), Dr. Sigurd Spieckermann, [Prof. Dr. Volker Tresp](http://www.dbs.ifi.lmu.de/cms/personen/professoren/tresp/index.html)\n\u003e - **Assistants**: Sebastian Schmoll, Sabrina Friedl\n\u003e - **Tutor**: [Changkun Ou](https://changkun.de)\n\u003e\n\u003e Time: Monday, 2pm-4pm or 4pm-6pm.\n\n## Tutorial sessions\n\n- 2018.10.22 **Week 1**: Python Introduction\n  - [Jupyter Notebook: Python introduction](week1/py_intro_self.ipynb)\n- 2018.10.29 **Week 2**: Derivative, Jacobian Matrix, Mean Square Error\n  - [Exercise solution: Manuscript](week2/manuscript.pdf)\n- 2018.11.05 **Week 3**: Computational Graph, Computational Gradient Graph, Backpropagation (BP), Gradient Vanishing \u0026 Exploding Problem\n  - [Exercise solution: Manuscript](week3/manuscript.pdf)\n  - [Jupyter Notebook: Computational graph numpy implementation](week3/comp_graph.ipynb)\n- 2018.11.12 **Week 4**: Convolution, Cross-correlation, ConvLayer and ConvNet\n  - [Exercise solution: Manuscript](week4/manuscript.pdf)\n  - [Jupyter Notebook: Cross-correlation numpy implementation](wee4/crosscor.ipynb)\n  - [Jupyter Notebook: Tensorflow Introduction, CNN, Inception](week4/tf_cnn_inception.ipynb)\n- 2018.11.19 **Week 5**: Backpropagation through Time (BPTT), Gradient Vanishing/Exploading in RNN, LSTMs, CIFAR10\n  - [Exercise solution: Manuscript](week5/manuscript.pdf)\n- 2018.11.26 **Week 6**: Statistic Uncertainty, Evidence Lower Bound, Metropolis-Hastings Algorithm, LSTM\n  - [Exercise solution: Manuscript](week6/manuscript.pdf)\n  - [Jupyter Notebook: Metropolis-Hastings Algorithm](week6/mha.ipynb)\n  - [Jupyter Notebook: LSTM tensorflow implementation](week6/lstm.ipynb)\n- 2018.12.03 **Week 7**: Local and distributed representation, Autoencoders, Restricted Boltzmann Machines\n  - [Exercise solution: Manuscript](week7/manuscript.pdf)\n  - [Jupyter Notebook: Autoencoder](autoencoder.ipynb)\n  - [Jupyter Notebook: RBM](rbm.ipynb)\n- 2018.12.10 **Week 8**: Tooling, PyTorch Introduction\n  - [Exercise solution: text](week8/solution.md)\n  - [Jupyter Notebook: PyTorch Introduction](week8/torch.ipynb)\n- 2018.12.17 **Week 9**: Variational Autoencoder, GANs\n  - [Exercise solution: Manuscript](week9/manuscript.pdf)\n  - [Jupyter Notebook: Variational Auto-Encoder](week9/vae.ipynb)\n- 2019.01.07 **Week 10**: Markov Reward Process, Markov Decision Process and Policy Iteration\n  - [Exercise solution: Manuscript](week10/manuscript.pdf)\n  - [Jupyter Notebook: Markov Decision Process](week10/mdp.ipynb)\n- 2019.01.14 **Week 11**: Model-free Reinforcement Learning, Temporal Difference Learning, Q-Learning and SARSA\n  - [Exercise solution: Manuscript](week11/manuscript.pdf)\n  - [Jupyter Notebook: Model-free RL: Q-Learning and SARSA](week12/rl.ipynb)\n- 2019.01.21 **Week 12**: Value Function Approximation, Baird’s Counterexample and Montain Car benchmark\n  - [Exercise solution: Manuscript](week12/manuscript.pdf)\n  - [Exercise solution: Baird's counterexample](week12/ex12-2.ipynb)\n  - [Exercise solution: Q-learning in Montain Car](week12/qlearn.ipynb)\n- 2019.01.28 **Week 13**: Policy Gradients and Actor Critic Learning\n  - [Exercise solution: Manuscript](week13/manuscript.pdf)\n  - [Exercise solution: Policy gradient with baseline](week13/ex13-3.ipynb)\n- 2019.02.04 **Week 14**: Knowledge Graphs in AI\n  - No tutorial\n\n## References\n\n- [LMU \"Machine Learning\" Tutorial Materials](https://github.com/changkun/ss18-machine-learning-tutorial)\n\n## License\n\nMIT \u0026 CC-BY 4.0 Copyright \u0026copy; 2018-2019 [Ou Changkun](https://changkun.de)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchangkun%2Fws-18-19-deep-learning-tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchangkun%2Fws-18-19-deep-learning-tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchangkun%2Fws-18-19-deep-learning-tutorial/lists"}