You've landed at the Deep Learning University Lectures Repository, your one-stop-shop for university-level deep learning lecture materials in PDF format. We're here to democratize access to educational resources, making deep learning accessible and understandable for all.
As a deep learning enthusiast, my goal is to enrich the global learning community by curating a diverse array of deep learning lectures. These resources span from basic to advanced topics, allowing learners to delve into the vast applications of deep learning.
The repository is neatly organized by university and course for easy navigation. Each university folder houses PDFs of lectures from various deep learning courses, enabling users to dive into their topics of interest.
We welcome and encourage contributions! If you have deep learning lecture materials to share, please submit a pull request. Together, we can create a comprehensive resource that benefits learners around the globe.
By pooling these resources, we aim to empower individuals worldwide to leverage the power of deep learning for the betterment of society. Whether you're a student, researcher, or hobbyist, this repository is crafted to facilitate learning and inspire innovative deep learning applications. Enjoy your learning journey!
Note: The information provided aligns with the user profile's focus on novelty in research, incorporating advanced probability, statistics, information theory, detection and estimation methods, and advanced deep learning and machine learning techniques.
Dear users,
Before accessing or downloading any PDFs from this repository, we kindly remind you to respect the intellectual property rights of the content creators. The PDFs included here belong to their respective owners, including universities, professors, and other educational institutions.
This repository is created solely for knowledge sharing and fostering a global learning community. It's crucial to adhere to copyright laws and use these materials strictly for educational purposes. If you find any content that infringes upon copyrights, please bring it to our attention, and we will promptly address the concern.
Let's ensure our pursuit of knowledge is conducted with integrity and respect for the hard work and dedication of those who contribute to the field of deep learning. Learn responsibly!
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Deep Learning⬅️⬅️
├─ Applied_DL
├─ 00 - Training.pdf
├─ 01 - Computer Vision
├─ 01 - Image Classification
├─ 01 - Large Networks.pdf
├─ 02 - Small Networks.pdf
├─ 03 - AutoML.pdf
├─ 04 - Robustness.pdf
├─ 05 - Visualizing & Understanding.pdf
└─ 06 - Transfer Learning.pdf
├─ 02 - Image Transformation
├─ 01 - Semantic Segmentation.pdf
├─ 02 - Super-Resolution, Denoising, and Colorization.pdf
├─ 03 - Pose Estimation.pdf
└─ 04 - Optical Flow and Depth Estimation.pdf
├─ 03 - Object Detection
├─ 01 - Two Stage Detectors.pdf
└─ 02 - One Stage Detectors.pdf
├─ 04 - Face Recognition and Detection.pdf
├─ 05 - Video.pdf
└─ 06 - 3D.pdf
├─ 02 - Natural Language Processing
├─ 01 - Word Representations.pdf
├─ 02 - Text Classification.pdf
├─ 03 - Neural Machine Translation.pdf
└─ 04 - Language Modeling.pdf
├─ 03 - Multimodal Learning.pdf
├─ 04 - Generative Networks
├─ 01 - Variational Auto-Encoders.pdf
├─ 02 - Unconditional GANs.pdf
├─ 03 - Conditional GANs.pdf
└─ 04 - Diffusion Models.pdf
├─ 05 - Advanced Topics
├─ 01 - Domain Adaptation.pdf
├─ 02 - Few Shot Learning.pdf
├─ 03 - Federated Learning.pdf
├─ 04 - Semi-Supervised Learning.pdf
└─ 05 - Self-Supervised Learning.pdf
├─ 06 - Speech & Music
├─ 01 - Recognition.pdf
├─ 02 - Synthesis.pdf
└─ 03 - Modeling.pdf
├─ 07 - Reinforcement Learning
├─ 01 - Games.pdf
├─ 02 - Simulated Environments.pdf
├─ 03 - Real Environments.pdf
└─ 04 - Uncertainty Quantification & Multitask Learning.pdf
├─ 08 - Graph Neural Networks.pdf
├─ 09 - Recommender Systems.pdf
├─ 10 - Computational Biology.pdf
└─ README.md
├─ Images
└─ 1.jpeg
├─ MIT
├─ 6S191_MIT_DeepLearning_L1.pdf
├─ 6S191_MIT_DeepLearning_L2.pdf
├─ 6S191_MIT_DeepLearning_L3.pdf
├─ 6S191_MIT_DeepLearning_L4.pdf
├─ 6S191_MIT_DeepLearning_L5.pdf
├─ 6S191_MIT_DeepLearning_L6.pdf
└─ DeepLearningBook.pdf
├─ MLSP
├─ 04Adaline.pdf
├─ 11785-NetworkOptimization-Fall23.pdf
├─ 11_785_HW2P2_S23_v2.pdf
├─ 11_785_hw3p2_S23-2.pdf
├─ 1706.03762.pdf
├─ Autograd_RecitationSlides_combined.pdf
├─ Bidirectional%20Recurrent%20Neural%20Networks.pdf
├─ F23-HW2P2-BOOTCAMP.pdf
├─ F23_Bootcamp 1_HW1P2.pdf
├─ Fall2023-RNN-Recitation.pdf
├─ HW1P1_F23.pdf
├─ HW1P2_F23.pdf
├─ HW2P1_Bootcamp_F23.pdf
├─ HW2P2_F23.pdf
├─ HW3P2_F23_Writeup_Updated.pdf
├─ HW4P2_S23.pdf
├─ How to compute a derivative.pdf
├─ Hw4_Part1_Bootcamp.pdf
├─ IDL_S23_Recitation_8__RNN_Basics.pdf
├─ LSTM.pdf
├─ Paper_Writing_Workshop.pdf
├─ Perceptrons-Epilogue-r.pdf
├─ Recitation_10.pdf
├─ Recitation_10_s23.pdf
├─ Recitation_4.pdf
├─ S23_Bootcamp 1_HW1P2.pdf
├─ Shannon49.pdf
├─ Werbos.pdf
├─ Your First MLP - S23.pdf
├─ booleancircuits_shannonproof.pdf
├─ c1992artificialneural.pdf
├─ derivatives and influences.pdf
├─ derivatives_and_influences.pdf
├─ duchi11a.pdf
├─ hw3p1_bootcamp_s23.pdf
├─ hw3p2_bootcamp_s23.pdf
├─ icml_2006.pdf
├─ lec0.logistics.pdf
├─ lec1.intro.pdf
├─ lec10.CNN2.pdf
├─ lec11.CNN3.pdf
├─ lec12.CNN4.pdf
├─ lec13.recurrent.pdf
├─ lec14.recurrent.pdf
├─ lec15.recurrent.pdf
├─ lec16.recurrent.pdf
├─ lec17.recurrent.pdf
├─ lec18.attention.pdf
├─ lec19.transformersLLMs.pdf
├─ lec2.universal.pdf
├─ lec20.representations.pdf
├─ lec21.VAE_1.pdf
├─ lec22.VAE_2.pdf
├─ lec23.diffusion.updated.pdf
├─ lec25.GAN2.pdf
├─ lec26.hopfieldBM.pdf
├─ lec3.learning.pdf
├─ lec4.learning.pdf
├─ lec5.pdf
├─ lec6.pdf
├─ lec8.optimizersandregularizers.pdf
├─ lec9.CNN1.pdf
├─ lec_24_GAN1.pdf
├─ naturebp.pdf
├─ perc.converge.pdf
├─ recitation12-slides.pdf
├─ s23_hw1_hackathon.pdf
├─ s23_hw1_hackathon2.pdf
└─ turing3.pdf
├─ New folder
├─ Notes
├─ LLM Architectures_8.8.2023.pdf
├─ decision theory
├─ Problem Session 1 -- Probability Review.pdf
├─ Problem Session 2 -- Bayesian Networks w solns.pdf
├─ Problem Session 2 -- Bayesian Networks.pdf
├─ Problem Session 4 -- Exact Solution Methods w solns.pdf
├─ Problem Session 4 -- Exact Solution Methods.pdf
├─ Problem Session 5 -- Policy Search w solns.pdf
├─ Problem Session 5 -- Policy Search.pdf
└─ Problem Session 7 -- Reinforcement Learning.pdf
├─ dm.pdf
├─ llmintro.pdf
├─ main_notes.pdf
└─ tuebingen
├─ lec_01_introduction.pdf
├─ lec_02_computation_graphs.pdf
├─ lec_03_deep_networks_1.pdf
├─ lec_04_deep_networks_2.pdf
├─ lec_05_regularization.pdf
├─ lec_06_optimization.pdf
├─ lec_07_convolutional_neural_networks.pdf
├─ lec_08_sequence_models.pdf
├─ lec_09_natural_language_processing.pdf
├─ lec_10_graph_neural_networks.pdf
├─ lec_11_autoencoders.pdf
└─ lec_12_generative_adversarial_networks.pdf
├─ RAG
├─ RAG_Slide_ENG.pdf
└─ ollamainference.py
├─ README.md
├─ Surveys
├─ Beyond Efficiency_2024_jan_4.pdf
├─ CMMMU_2024_jan_22 surveys_textbook.pdf
├─ LLMs_survey_software_23_dec_2023.pdf
├─ Large Language Models for Generative Information Extraction_2023_dec.pdf
├─ RL_survey_2023_22.pdf
├─ Video Understanding with Large Language Models_2024_jan_4.pdf
├─ lec1.pptx
└─ self_reqardining_language_model.pdf
├─ berkeley_deep learning
├─ hw1.pdf
├─ hw2.pdf
├─ hw3.pdf
├─ hw4.pdf
├─ hw5.pdf
├─ lec-1.pdf
├─ lec-10.pdf
├─ lec-11.pdf
├─ lec-12.pdf
├─ lec-13.pdf
├─ lec-14.pdf
├─ lec-15.pdf
├─ lec-16.pdf
├─ lec-17.pdf
├─ lec-18.pdf
├─ lec-19.pdf
├─ lec-2.pdf
├─ lec-20.pdf
├─ lec-21.pdf
├─ lec-22.pdf
├─ lec-23.pdf
├─ lec-3.pdf
├─ lec-4.pdf
├─ lec-5.pdf
├─ lec-6.pdf
├─ lec-7.pdf
├─ lec-8.pdf
├─ lec-9.pdf
└─ project_assignment.pdf
├─ chinese university of HONG kong
└─ LLMS
├─ 2005.11401.pdf
├─ 2012.07805.pdf
├─ 2101.03961.pdf
├─ 2103.00020.pdf
├─ Lecture 8_ Multimodal_LLMs.pdf
├─ Lecture-10-Vertical-LLMs.pdf
├─ Lecture-5-Efficiency.pdf
├─ Lecture-7-Knowledge-and-Reasoning.pdf
├─ Lecture-9-LLM-Agents.pdf
├─ Lecture4-TrainingLLMs.pdf
├─ Tutorial1-1-ChatgptAPI.pdf
├─ lecture-1-introduction.pdf
├─ lecture-2-language-model.pdf
├─ lecture-3-architecture.pdf
└─ lecture-6-mid-review.pdf
├─ cs131-class-notes.pdf
├─ data-08-00141-v2.pdf
├─ nono
├─ 1106.1813.pdf
├─ 5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
├─ homework1-spring-2019.pdf
├─ homework2-spring-2019.pdf
├─ homework3-spring-2019.pdf
├─ homework4-spring-2019.pdf
├─ homework5-spring-2019.pdf
└─ tsmcb09.pdf
├─ princeton
├─ 1706.03762.pdf
├─ 1802.05365.pdf
├─ 1810.04805.pdf
├─ 1907.11692.pdf
├─ 1910.10683.pdf
├─ 1910.13461.pdf
├─ 2002.12327.pdf
├─ 2003.10555.pdf
├─ 2005.14165.pdf
├─ 2010.11934.pdf
├─ 2108.07258.pdf
├─ 2208.01448.pdf
├─ Daedalus_Sp22_09_Manning.pdf
├─ language_understanding_paper.pdf
├─ lec01.pdf
├─ lec02.pdf
└─ lec03.pdf
├─ southern califorina
├─ deep learnings.pdf
├─ lec1.pdf
├─ lec10.pdf
├─ lec11.pdf
├─ lec12.pdf
├─ lec13.pdf
├─ lec14.pdf
├─ lec15.pdf
├─ lec16.pdf
├─ lec2.pdf
├─ lec3.pdf
├─ lec4.pdf
├─ lec5.pdf
├─ lec6.pdf
├─ lec7.pdf
├─ lec8.pdf
├─ lec9.pdf
└─ pptx
├─ lec1.pptx
├─ lec10.pptx
├─ lec11.pptx
├─ lec12.pptx
├─ lec13.pptx
├─ lec14.pptx
├─ lec15.pptx
├─ lec16.pptx
├─ lec2.pptx
├─ lec3.pptx
├─ lec4.pptx
├─ lec5.pptx
├─ lec6.pptx
├─ lec7.pptx
├─ lec8.pptx
└─ lec9.pptx
├─ stanford
├─ Computer Vision
├─ 1206.5533v2.pdf
├─ 4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
├─ derivatives.pdf
├─ lecture_10.pdf
├─ lecture_11.pdf
├─ lecture_12.pdf
├─ lecture_13.pdf
├─ lecture_14.pdf
├─ lecture_15.pdf
├─ lecture_16.pdf
├─ lecture_1_part_1.pdf
├─ lecture_1_part_2.pdf
├─ lecture_2.pdf
├─ lecture_3.pdf
├─ lecture_4.pdf
├─ lecture_5.pdf
├─ lecture_6.pdf
├─ lecture_7.pdf
├─ lecture_8.pdf
├─ lecture_9.pdf
├─ lecun-98b.pdf
├─ linear-backprop.pdf
├─ section_2.pdf
├─ section_3.pdf
├─ section_5.pdf
└─ tricks-2012.pdf
├─ DeepGenerativeModels
├─ annrev.pdf
├─ cs229-linalg.pdf
├─ cs229-prob.pdf
├─ cs236_lecture10.pdf
├─ cs236_lecture11.pdf
├─ cs236_lecture12.pdf
├─ cs236_lecture17.pdf
├─ cs236_lecture18.pdf
├─ cs236_lecture2.pdf
├─ cs236_lecture3.pdf
├─ cs236_lecture4.pdf
├─ cs236_lecture5.pdf
├─ cs236_lecture6.pdf
├─ cs236_lecture7.pdf
├─ cs236_lecture8.pdf
├─ cs236_lecture9.pdf
├─ lecture15.pdf
└─ pptx
├─ cs236_lecture1_2023.pptx
├─ lecture 13.pptx
├─ lecture16-2023-comp.pptx
└─ lecture_14_comp.pptx
├─ Machine Learning with Graphs
├─ 01-intro.pdf
├─ 02-nodeemb.pdf
├─ 03-GNN1.pdf
├─ 04-GNN2.pdf
├─ 05-GNN3.pdf
├─ 06-theory.pdf
├─ 07-hetero.pdf
├─ 08-kg.pdf
├─ 09-reasoning.pdf
├─ 10-motifs.pdf
├─ 11-recsys.pdf
├─ 12-deep-generation.pdf
├─ 13-advanced_gnns.pdf
├─ 14-graph-transformer.pdf
├─ 1403.6652.pdf
├─ 1412.6575.pdf
├─ 15-scalable.pdf
├─ 1506.01094.pdf
├─ 16-snap.pdf
├─ 1606.06357.pdf
├─ 1607.00653.pdf
├─ 1609.02907.pdf
├─ 17-linkpred.pdf
├─ 1703.06103.pdf
├─ 1705.07874.pdf
├─ 1706.02216.pdf
├─ 1710.02971.pdf
├─ 1710.10903.pdf
├─ 18-algo-reasoning-gnns.pdf
├─ 1802.08773.pdf
├─ 1805.07984.pdf
├─ 1806.01445.pdf
├─ 1806.01973.pdf
├─ 1806.02473.pdf
├─ 1806.08804.pdf
├─ 1810.00826.pdf
├─ 19-conclusion.pdf
├─ 1902.07153.pdf
├─ 1902.10197.pdf
├─ 1903.03894.pdf
├─ 1905.07953.pdf
├─ 1905.08108.pdf
├─ 1905.13211.pdf
├─ 1906.04817.pdf
├─ 1cecc7a77928ca8133fa24680a88d2f9-Paper.pdf
├─ 2002.02126.pdf
├─ 2002.05969.pdf
├─ 2003.01332.pdf
├─ 2007.03092.pdf
├─ 2009.11848.pdf
├─ 2011.08843.pdf
├─ 2012.15445.pdf
├─ 2101.10320.pdf
├─ 2106.05234.pdf
├─ 2202.13013.pdf
├─ 2205.07424.pdf
├─ 2206.09677.pdf
├─ 2302.04181.pdf
├─ CS_224W_Fall_2023_HW1.pdf
├─ CS_224W_Fall_2023_HW2.pdf
├─ CS_224W_Fall_2023_HW3.pdf
├─ Intro_Causality.pdf
└─ aaai2015_transr.pdf
├─ NLP
├─ NLP
├─ Been-Kim-StanfordLectureMarch2023.pdf
├─ Danqi-QA-slides-2022.pdf
├─ Multimodal-Deep-Learning-CS224n-Kiela.pdf
├─ Vinodkumar_Prabhakaran_Socially_Responsible_NLP.pdf
├─ cs224n-2021-lecture01-wordvecs1.pdf
├─ cs224n-2021-lecture02-wordvecs2.pdf
├─ cs224n-2021-lecture03-neuralnets.pdf
├─ cs224n-2021-lecture04-dep-parsing-annotated.pdf
├─ cs224n-2021-lecture04-dep-parsing.pdf
├─ cs224n-2021-lecture05-rnnlm.pdf
├─ cs224n-2021-lecture06-fancy-rnn.pdf
├─ cs224n-2021-lecture07-nmt.pdf
├─ cs224n-2021-lecture08-final-project.pdf
├─ cs224n-2021-lecture09-transformers.pdf
├─ cs224n-2021-lecture10-pretraining.pdf
├─ cs224n-2021-lecture11-qa-v2.pdf
├─ cs224n-2021-lecture11-qa.pdf
├─ cs224n-2021-lecture12-generation.pdf
├─ cs224n-2021-lecture13-coref.pdf
├─ cs224n-2021-lecture14-t5.pdf
├─ cs224n-2021-lecture15-lm.pdf
├─ cs224n-2021-lecture16-ethics.pdf
├─ cs224n-2021-lecture17-analysis.pdf
├─ cs224n-2021-lecture18-future.pdf
├─ cs224n-2022-lecture-editing.pdf
├─ cs224n-2022-lecture-knowledge.pdf
├─ cs224n-2022-lecture01-wordvecs1.pdf
├─ cs224n-2022-lecture02-wordvecs2.pdf
├─ cs224n-2022-lecture03-neuralnets.pdf
├─ cs224n-2022-lecture04-dep-parsing.pdf
├─ cs224n-2022-lecture05-rnnlm.pdf
├─ cs224n-2022-lecture06-fancy-rnn.pdf
├─ cs224n-2022-lecture07-nmt.pdf
├─ cs224n-2022-lecture08-final-project.pdf
├─ cs224n-2022-lecture09-transformers.pdf
├─ cs224n-2022-lecture10-pretraining.pdf
├─ cs224n-2022-lecture12-generation-final.pdf
├─ cs224n-2022-lecture15-guu.pdf
├─ cs224n-2022-lecture16-CNN-TreeRNN.pdf
├─ cs224n-2022-lecture18-coref.pdf
├─ cs224n-2023-lecture01-wordvecs1.pdf
├─ cs224n-2023-lecture02-wordvecs2.pdf
├─ cs224n-2023-lecture03-neuralnets.pdf
├─ cs224n-2023-lecture04-dep-parsing.pdf
├─ cs224n-2023-lecture05-rnnlm.pdf
├─ cs224n-2023-lecture06-fancy-rnn.pdf
├─ cs224n-2023-lecture07-final-project.pdf
├─ cs224n-2023-lecture08-transformers.pdf
├─ cs224n-2023-lecture10-nlg.pdf
├─ cs224n-2023-lecture11-prompting-rlhf.pdf
├─ cs224n-2023-lecture12-QA.pdf
├─ cs224n-2023-lecture13-CNN-TreeRNN.pdf
├─ cs224n-2023-lecture14-insights-linguistics.pdf
├─ cs224n-2023-lecture15-code-generation.pdf
├─ cs224n-2023-lecture17-coref.pdf
├─ cs224n-2023-lecture18-analysis.pdf
├─ cs224n-2023-lecture9-pretraining.pdf
└─ cs224n-lecture-09-anna-goldie-2022-02-01.pdf
└─ eisenstein-nlp-notes.pdf
├─ RL
├─ Reinforcement lectures
├─ CS234 2023 Batch Policy Evaluation.pdf
├─ DL-Pytorch.pdf
├─ DQNNaturePaper.pdf
├─ MBIEEB.pdf
├─ PACnotes.pdf
├─ Pg2post.pdf
├─ Problem_Sessions_CS234_Feb10.pdf
├─ Problem_Sessions_CS234_Feb10_solutions.pdf
├─ Problem_Sessions_CS234_Feb17.pdf
├─ Problem_Sessions_CS234_Feb17_solutions.pdf
├─ Problem_Sessions_CS234_Feb24.pdf
├─ Problem_Sessions_CS234_Feb24_solutions.pdf
├─ Problem_Sessions_CS234_Feb3.pdf
├─ Problem_Sessions_CS234_Feb3_solutions.pdf
├─ Problem_Sessions_CS234_Jan13.pdf
├─ Problem_Sessions_CS234_Jan13_solutions.pdf
├─ Problem_Sessions_CS234_Jan20.pdf
├─ Problem_Sessions_CS234_Jan20_solutions.pdf
├─ Problem_Sessions_CS234_Jan27.pdf
├─ Problem_Sessions_CS234_Jan27_solutions.pdf
├─ Problem_Sessions_CS234_Mar10.pdf
├─ Problem_Sessions_CS234_Mar10_solutions.pdf
├─ batch_learning_post.pdf
├─ batch_nosol.pdf
├─ batch_policy_learning.pdf
├─ book.pdf
├─ cs229-linalg.pdf
├─ cs229-prob.pdf
├─ cs235-lecture15-post.pdf
├─ dqn.pdf
├─ imitation-post.pdf
├─ imitation.pdf
├─ imitationpost.pdf
├─ lecture1.pdf
├─ lecture10.pdf
├─ lecture10post.pdf
├─ lecture11-2023.pdf
├─ lecture11post.pdf
├─ lecture12.pdf
├─ lecture12post.pdf
├─ lecture13.pdf
├─ lecture13_post.pdf
├─ lecture15.pdf
├─ lecture15_annotated.pdf
├─ lecture1post.pdf
├─ lecture2.pdf
├─ lecture2post.pdf
├─ lecture3.pdf
├─ lecture3post.pdf
├─ lecture4.pdf
├─ lecture4post.pdf
├─ lecture5.pdf
├─ lecture5post.pdf
├─ lecture6.pdf
├─ lecture6_post.pdf
├─ lecture7.pdf
├─ lecture7_ns.pdf
├─ lecture7_post.pdf
├─ lecture7post.pdf
├─ lecture9post.pdf
├─ lecture_week10.pdf
├─ lnotes11.pdf
├─ lnotes2.pdf
├─ lnotes3.pdf
├─ lnotes4.pdf
├─ lnotes5.pdf
├─ lnotes6.pdf
├─ lnotes7.pdf
├─ lnotes8.pdf
├─ lnotes9.pdf
├─ pg2.pdf
└─ winter2023_lecture_batch_policy_evalclass.pdf
└─ Reinforcement_p
├─ CS234_ProblemSession1.pdf
├─ CS234_ProblemSession1_Solutions.pdf
├─ CS234_ProblemSession2.pdf
├─ CS234_ProblemSession2_Solutions.pdf
├─ CS234_ProblemSession3.pdf
├─ CS234_ProblemSession3_Solutions.pdf
├─ CS234_Win23_ProblemSession1.pdf
├─ CS234_Win23_ProblemSession1_Solutions.pdf
├─ CS234_Win23_ProblemSession2.pdf
├─ CS234_Win23_ProblemSession2_Solutions.pdf
├─ CS234_Win23_ProblemSession3.pdf
├─ CS234_Win23_ProblemSession3_Solutions.pdf
├─ CS234_Win23_ProblemSession4.pdf
├─ CS234_Win23_ProblemSession4_Solutions.pdf
├─ CS234_Win23_ProblemSession5.pdf
├─ CS234_Win23_ProblemSession5_Solutions.pdf
├─ Problem_Sessions_CS234_Feb10.pdf
├─ Problem_Sessions_CS234_Feb10_solutions.pdf
├─ Problem_Sessions_CS234_Feb17.pdf
├─ Problem_Sessions_CS234_Feb17_solutions.pdf
├─ Problem_Sessions_CS234_Feb24.pdf
├─ Problem_Sessions_CS234_Feb24_solutions.pdf
├─ Problem_Sessions_CS234_Feb3.pdf
├─ Problem_Sessions_CS234_Feb3_solutions.pdf
├─ Problem_Sessions_CS234_Jan13.pdf
├─ Problem_Sessions_CS234_Jan13_solutions.pdf
├─ Problem_Sessions_CS234_Jan20.pdf
├─ Problem_Sessions_CS234_Jan20_solutions.pdf
├─ Problem_Sessions_CS234_Jan27.pdf
├─ Problem_Sessions_CS234_Jan27_solutions.pdf
├─ Problem_Sessions_CS234_Mar10.pdf
├─ Problem_Sessions_CS234_Mar10_solutions.pdf
├─ Quiz0.pdf
├─ Quiz0_solution.pdf
├─ Quiz1_solution.pdf
├─ Quiz2_solution.pdf
├─ RLbook2018.pdf
└─ talk.pdf
├─ cs231n_standford
├─ 1206.5533v2.pdf
├─ 1701.00160.pdf
├─ 4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
├─ derivatives.pdf
├─ lecture_10.pdf
├─ lecture_11.pdf
├─ lecture_12.pdf
├─ lecture_13.pdf
├─ lecture_14.pdf
├─ lecture_16_Hao.pdf
├─ lecture_17.pdf
├─ lecture_18.pdf
├─ lecture_1_feifei.pdf
├─ lecture_1_ranjay.pdf
├─ lecture_2.pdf
├─ lecture_3.pdf
├─ lecture_4.pdf
├─ lecture_5.pdf
├─ lecture_6.pdf
├─ lecture_7.pdf
├─ lecture_8.pdf
├─ lecture_9.pdf
├─ lecture_HAI.pdf
├─ lecun-98b.pdf
├─ linear-backprop.pdf
├─ section_2_annotated.pdf
├─ section_2_backprop.pdf
├─ section_3_project.pdf
├─ section_5_midterm.pdf
├─ section_7_detection.pdf
├─ section_8_video.pdf
└─ tricks-2012.pdf
└─ standford231
├─ activation_f.pdf
├─ applications.pdf
├─ attention_models.pdf
├─ autoencoders.pdf
├─ backprop.pdf
├─ biblio.pdf
├─ biblio.pdf~
├─ bn_layer.pdf
├─ conv_layer.pdf
├─ data_aug_trans.pdf
├─ data_preprocessing.pdf
├─ dropout.pdf
├─ famous_networks.pdf
├─ fc_layer.pdf
├─ gans.pdf
├─ hw_layer.pdf
├─ hyper_parms_tun.pdf
├─ in_layer.pdf
├─ loss_f.pdf
├─ nn.pdf
├─ others.pdf
├─ params_init.pdf
├─ params_up.pdf
├─ part_Applications.pdf
├─ part_Data.pdf
├─ part_Layers.pdf
├─ part_Learning.pdf
├─ part_Networks.pdf
├─ pool_layer.pdf
├─ recurrent_neural_networks.pdf
├─ region_based_cnn.pdf
├─ rnn_convnet.pdf
├─ spatial_transformer_networks.pdf
├─ title.pdf
├─ tricks.pdf
├─ upsampling_layer.pdf
├─ visualization.pdf
└─ yolo.pdf
└─ toronto
├─ pdf
├─ lec1.pdf
├─ lec10.pdf
├─ lec11.pdf
├─ lec12.pdf
├─ lec13.pdf
├─ lec14.pdf
├─ lec15.pdf
├─ lec16.pdf
├─ lec2.pdf
├─ lec3.pdf
├─ lec4.pdf
├─ lec5.pdf
├─ lec6.pdf
├─ lec7.pdf
├─ lec8.pdf
└─ lec9.pdf
└─ pptx
├─ lec1.pptx
├─ lec10.pptx
├─ lec11.pptx
├─ lec12.pptx
├─ lec13.pptx
├─ lec14.pptx
├─ lec15.pptx
├─ lec16.pptx
├─ lec2.pptx
├─ lec3.pptx
├─ lec4.pptx
├─ lec5.pptx
├─ lec6.pptx
├─ lec7.pptx
├─ lec8.pptx
└─ lec9.pptx