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
└─ test.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
├─ Architecture
├─ BERT_Slides.pdf
├─ Beyond Fine-Tuning_ LLM Optimization Webinar.pdf
├─ Diagrams_V2.pdf
├─ Mistral.pdf
├─ Slides.pdf
└─ Stable_Diffusion_Diagrams_V2.pdf
├─ Chinese University
└─ pdf
├─ 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
├─ Columbia_pdf
├─ 2009_Notes_LinearAlgebra.pdf
├─ 20201007.pdf
├─ 2022_DLRL_Optim.pdf
├─ EM.pdf
├─ L1.pdf
├─ L10.5.pdf
├─ L10.pdf
├─ L11.pdf
├─ L12.pdf
├─ L13.pdf
├─ L14.pdf
├─ L15.pdf
├─ L16.pdf
├─ L17.5.pdf
├─ L17.pdf
├─ L18.pdf
├─ L19.pdf
├─ L2.pdf
├─ L20.pdf
├─ L21.pdf
├─ L22.pdf
├─ L22a.pdf
├─ L22b.pdf
├─ L23.pdf
├─ L24.pdf
├─ L25.pdf
├─ L26.pdf
├─ L26a.pdf
├─ L26b.pdf
├─ L27.pdf
├─ L28.5.pdf
├─ L28.pdf
├─ L29.pdf
├─ L2b.pdf
├─ L3.pdf
├─ L30.pdf
├─ L31.5.pdf
├─ L31.pdf
├─ L32.pdf
├─ L33.pdf
├─ L34.5.pdf
├─ L34.pdf
├─ L34_AM.pdf
├─ L34_PM.pdf
├─ L34_common.pdf
├─ L35.pdf
├─ L35_AM.pdf
├─ L35_PM.pdf
├─ L36.pdf
├─ L3b.pdf
├─ L4.pdf
├─ L5.pdf
├─ L6.pdf
├─ L7.pdf
├─ L8.pdf
├─ L9.pdf
├─ NP.pdf
├─ S0.pdf
├─ S1.pdf
├─ S12.pdf
├─ S2.pdf
├─ S3.pdf
├─ S4.pdf
├─ S5.pdf
├─ S6.pdf
├─ S7.pdf
├─ S8.5.pdf
├─ S8.pdf
├─ assorted.pdf
├─ bigO.pdf
├─ calculus.pdf
├─ convex.pdf
├─ differentiable.pdf
├─ linearQuadraticGradients.pdf
├─ max.pdf
├─ mirrorMultiLevel.pdf
├─ norms.pdf
├─ notation.pdf
├─ onlineActiveCausal.pdf
├─ overview.pdf
├─ pageRank.pdf
├─ parallelDistributed.pdf
├─ probability.pdf
├─ probabilitySlides.pdf
├─ reinforcementLearning.pdf
├─ semiSupervised.pdf
├─ sequenceMining.pdf
└─ structLearn.pdf
├─ Giant Eagle Auditorium
├─ notebooks
└─ GREEDY_AND_BEAM_DECODING.ipynb
├─ pdf
├─ 04Adaline.pdf
├─ 11785-NetworkOptimization-Fall23.pdf
├─ 1706.03762.pdf
├─ Alexander_Bain_Mind_and_Body_009178a0.pdf
├─ Autograd_RecitationSlides_combined.pdf
├─ F23-HW2P2-BOOTCAMP.pdf
├─ F23_Bootcamp 1_HW1P2.pdf
├─ Fall2023-RNN-Recitation.pdf
├─ HW1P2_F23.pdf
├─ HW2P1_Bootcamp_F23.pdf
├─ HW2P2_F23.pdf
├─ HW3P2_F23_Writeup_Updated.pdf
├─ Hebb_1949_The_Organization_of_Behavior.pdf
├─ How to compute a derivative.pdf
├─ Hw4_Part1_Bootcamp.pdf
├─ LSTM.pdf
├─ Paper_Writing_Workshop.pdf
├─ Perceptrons-Epilogue-r.pdf
├─ Recitation_10.pdf
├─ Recitation_4.pdf
├─ Rosenblatt_1959-09865-001.pdf
├─ Shannon49.pdf
├─ Werbos.pdf
├─ booleancircuits_shannonproof.pdf
├─ c1992artificialneural.pdf
├─ derivatives and influences.pdf
├─ derivatives_and_influences.pdf
├─ duchi11a.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
├─ lec7.stochastic_gradient.pdf
├─ lec8.optimizersandregularizers.pdf
├─ lec9.CNN1.pdf
├─ lec_24_GAN1.pdf
├─ naturebp.pdf
├─ perc.converge.pdf
└─ turing3.pdf
└─ pptx
├─ 11-785_Rec_6_-_Face_Classification_and_Verification.pptx
└─ F23_IDL_ Recitation_5.pptx
├─ Hemath_paper
├─ 2402.08392v1.pdf
├─ 2402.08416v1.pdf
├─ 2402.08467v1.pdf
├─ 2402.08472v1.pdf
├─ 2402.08546v1.pdf
├─ 2402.08562v1.pdf
├─ 2402.08565v1.pdf
├─ 2402.08570v1.pdf
├─ 2402.08577v1.pdf
├─ 2402.08594v1.pdf
├─ 2402.08631v1.pdf
├─ 2402.08638v1.pdf
├─ 2402.08644v1.pdf
├─ 2402.08657v1.pdf
├─ 2402.08658v1.pdf
├─ 2402.08666v1.pdf
├─ 2402.08670v1.pdf
├─ 2402.08674v1.pdf
├─ 2402.08679v1.pdf
└─ 2402.08680v1.pdf
├─ Hemath_paper1
├─ 1511.01427v1.Turing_Computation_with_Recurrent_Artificial_Neural_Networks.pdf
├─ 1511.08779v1.Multiagent_Cooperation_and_Competition_with_Deep_Reinforcement_Learning.pdf
├─ 1512.01693v1.Deep_Attention_Recurrent_Q_Network.pdf
├─ 1606.02032v1.Human_vs__Computer_Go__Review_and_Prospect.pdf
├─ 1704.05179v3.SearchQA__A_New_Q_A_Dataset_Augmented_with_Context_from_a_Search_Engine.pdf
├─ 1804.01874v1.A_Human_Mixed_Strategy_Approach_to_Deep_Reinforcement_Learning.pdf
├─ 1807.08217v1.Asynchronous_Advantage_Actor_Critic_Agent_for_Starcraft_II.pdf
├─ 1808.03766v2.The_ActivityNet_Large_Scale_Activity_Recognition_Challenge_2018_Summary.pdf
├─ 1903.12328v2.Improved_Reinforcement_Learning_with_Curriculum.pdf
├─ 1905.10863v3.SAI__a_Sensible_Artificial_Intelligence_that_plays_with_handicap_and_targets_high_scores_in_9x9_Go__extended_version_.pdf
├─ 1910.06591v2.SEED_RL__Scalable_and_Efficient_Deep_RL_with_Accelerated_Central_Inference.pdf
├─ 1911.04890v1.Recurrent_Neural_Network_Transducer_for_Audio_Visual_Speech_Recognition.pdf
├─ 2005.07572v3.Participatory_Problem_Formulation_for_Fairer_Machine_Learning_Through_Community_Based_System_Dynamics.pdf
├─ 2010.10864v1.A_Short_Note_on_the_Kinetics_700_2020_Human_Action_Dataset.pdf
├─ 2208.03143v1.Deep_Learning_and_Health_Informatics_for_Smart_Monitoring_and_Diagnosis.pdf
├─ 2211.07357v2.Controlling_Commercial_Cooling_Systems_Using_Reinforcement_Learning.pdf
├─ 2211.15646v4.Beyond_Invariance__Test_Time_Label_Shift_Adaptation_for_Distributions_with__Spurious__Correlations.pdf
├─ 2303.11223v2.Monocular_Cyclist_Detection_with_Convolutional_Neural_Networks.pdf
├─ 2306.05859v2.Bring_Your_Own__Non_Robust__Algorithm_to_Solve_Robust_MDPs_by_Estimating_The_Worst_Kernel.pdf
├─ 2309.03409v2.Large_Language_Models_as_Optimizers.pdf
├─ 2402.00559v3.A_Chain_of_Thought_Is_as_Strong_as_Its_Weakest_Link__A_Benchmark_for_Verifiers_of_Reasoning_Chains.pdf
├─ 2402.05116v2.Quantifying_Similarity__Text_Mining_Approaches_to_Evaluate_ChatGPT_and_Google_Bard_Content_in_Relation_to_BioMedical_Literature.pdf
├─ 2402.05235v1.SPAD___Spatially_Aware_Multiview_Diffusers.pdf
├─ 2402.05799v1.Recent_Breakthrough_in_AI_Driven_Materials_Science__Tech_Giants_Introduce_Groundbreaking_Models.pdf
├─ 2402.06187v2.Premier_TACO_is_a_Few_Shot_Policy_Learner__Pretraining_Multitask_Representation_via_Temporal_Action_Driven_Contrastive_Loss.pdf
├─ 2402.06221v1.ResumeFlow__An_LLM_facilitated_Pipeline_for_Personalized_Resume_Generation_and_Refinement.pdf
├─ 2402.07023v1.Gemini_Goes_to_Med_School__Exploring_the_Capabilities_of_Multimodal_Large_Language_Models_on_Medical_Challenge_Problems___Hallucinations.pdf
├─ 2402.07095v1.Does_ChatGPT_and_Whisper_Make_Humanoid_Robots_More_Relatable_.pdf
├─ 2402.07681v1.Large_Language_Models__Ad_Referendum___How_Good_Are_They_at_Machine_Translation_in_the_Legal_Domain_.pdf
├─ 2402.07837v1.Quantile_Least_Squares__A_Flexible_Approach_for_Robust_Estimation_and_Validation_of_Location_Scale_Families.pdf
├─ 2402.08393v1.NfgTransformer__Equivariant_Representation_Learning_for_Normal_form_Games.pdf
├─ 2402.08431v1.Generating_Java_Methods__An_Empirical_Assessment_of_Four_AI_Based_Code_Assistants.pdf
├─ 2402.08546v1.pdf
├─ 2402.08562v1.pdf
├─ 2402.08565v1.pdf
├─ 2402.08570v1.pdf
├─ 2402.08577v1.pdf
├─ 2402.08594v1.pdf
├─ 2402.08631v1.pdf
├─ 2402.08638v1.pdf
├─ 2402.08644v1.pdf
├─ 2402.08657v1.pdf
├─ 2402.08658v1.pdf
├─ 2402.08666v1.pdf
├─ 2402.08670v1.pdf
├─ 2402.08674v1.pdf
├─ 2402.08679v1.pdf
├─ 2402.08680v1.pdf
└─ 2402.08681v1.Chain_Reaction_of_Ideas__Can_Radioactive_Decay_Predict_Technological_Innovation_.pdf
├─ Hemath_paper2
├─ 2402.07282v2.pdf
├─ 2402.07321v1.pdf
├─ 2402.07401v1.pdf
├─ 2402.07408v1.pdf
├─ 2402.07477v1.pdf
├─ 2402.07610v1.pdf
├─ 2402.07616v1.pdf
├─ 2402.07647v1.pdf
├─ 2402.07658v1.pdf
├─ 2402.07681v1.pdf
├─ 2402.07770v1.pdf
├─ 2402.07776v1.pdf
├─ 2402.07812v1.pdf
├─ 2402.07841v1.pdf
├─ 2402.07844v1.pdf
├─ 2402.07862v1.pdf
├─ 2402.07867v1.pdf
├─ 2402.07876v1.pdf
├─ 2402.07877v1.pdf
├─ 2402.08064v1.pdf
├─ 2402.08073v1.pdf
├─ 2402.08078v1.pdf
├─ 2402.08100v1.pdf
├─ 2402.08113v1.pdf
├─ 2402.08114v1.pdf
├─ 2402.08115v1.pdf
├─ 2402.08164v1.pdf
├─ 2402.08170v1.pdf
├─ 2402.08178v1.pdf
├─ 2402.08189v1.pdf
├─ 2402.08219v1.pdf
├─ 2402.08259v1.pdf
├─ 2402.08277v1.pdf
├─ 2402.08303v1.pdf
├─ 2402.08341v1.pdf
├─ 2402.08416v1.pdf
├─ 2402.08472v1.pdf
├─ 2402.08546v1.pdf
├─ 2402.08631v1.pdf
├─ 2402.08644v1.pdf
├─ 2402.08658v1.pdf
├─ 2402.08674v1.pdf
└─ 2402.08679v1.pdf
├─ Images
├─ 1.jpeg
├─ 123.jpg
├─ 2.png
├─ Designer.png
├─ Designer1.png
├─ Designer2.png
├─ Designer3.png
└─ Designer4.png
├─ 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
├─ 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
├─ PRINCETON
└─ pdf
├─ 1706.03762.pdf
├─ 1802.05365.pdf
├─ 1810.04805.pdf
├─ 1907.11692.pdf
├─ 1909.01066.pdf
├─ 1909.08593.pdf
├─ 1910.10683.pdf
├─ 1910.13461.pdf
├─ 1911.00172.pdf
├─ 1912.02164.pdf
├─ 2001.07676.pdf
├─ 2001.08361.pdf
├─ 2002.08910.pdf
├─ 2002.12327.pdf
├─ 2003.10555.pdf
├─ 2005.14165.pdf
├─ 2008.02637.pdf
├─ 2009.01325.pdf
├─ 2009.06367.pdf
├─ 2009.11462.pdf
├─ 2010.11934.pdf
├─ 2010.14701.pdf
├─ 2012.00955.pdf
├─ 2012.07805.pdf
├─ 2012.15723.pdf
├─ 21-0998.pdf
├─ 2101.00027.pdf
├─ 2101.00190.pdf
├─ 2101.06804.pdf
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├─ 2205.08514.pdf
├─ 2205.11916.pdf
├─ 2205.12674.pdf
├─ 2207.05221.pdf
├─ 2208.01066.pdf
├─ 2208.01448.pdf
├─ 2208.03299.pdf
├─ 2208.03306.pdf
├─ 2208.14271.pdf
├─ 2209.01667.pdf
├─ 2210.07128.pdf
├─ 2210.11416.pdf
├─ Daedalus_Sp22_09_Manning.pdf
├─ Red%20Teaming.pdf
├─ language_models_are_unsupervised_multitask_learners.pdf
├─ language_understanding_paper.pdf
├─ lec01.pdf
├─ lec02.pdf
├─ lec03.pdf
├─ lec04.pdf
├─ lec05.pdf
├─ lec06.pdf
├─ lec07.pdf
├─ lec08.pdf
├─ lec09.pdf
├─ lec10.pdf
├─ lec11.pdf
├─ lec12.pdf
├─ lec13.pdf
├─ lec14.pdf
├─ lec15.pdf
├─ lec16.pdf
├─ lec17.pdf
├─ lec18.pdf
├─ lec19.pdf
├─ lec20.pdf
└─ lec22.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
├─ Surveys
├─ 2402.07521v1.A_step_towards_the_integration_of_machine_learning_and_small_area_estimation.pdf
├─ 2402.07523v1.Using_Ensemble_Inference_to_Improve_Recall_of_Clone_Detection.pdf
├─ 2402.07527v1.Operating_conditions_and_thermodynamic_bounds_of_dual_radiative_heat_engines.pdf
├─ 2402.07530v1.Reproducibility__Replicability__and_Repeatability__A_survey_of_reproducible_research_with_a_focus_on_high_performance_computing.pdf
├─ 2402.07533v1.Tuning_proximity_spin_orbit_coupling_in_graphene_NbSe__2__heterostructures_via_twist_angle.pdf
├─ 2402.07535v1.Weak_and_strong_law_of_large_numbers_for_strictly_stationary_Banach_valued_random_fields.pdf
├─ 2402.07536v1.BreakGPT__A_Large_Language_Model_with_Multi_stage_Structure_for_Financial_Breakout_Detection.pdf
├─ 2402.07540v1.PKG_API__A_Tool_for_Personal_Knowledge_Graph_Management.pdf
├─ 2402.07543v1.Show_Me_How_It_s_Done__The_Role_of_Explanations_in_Fine_Tuning_Language_Models.pdf
├─ 2402.07548v1.NOMAD_CAMELS__Configurable_Application_for_Measurements__Experiments_and_Laboratory_Systems.pdf
├─ 2402.07550v1.De_Casteljau_s_Algorithm_in_Geometric_Data_Analysis__Theory_and_Application.pdf
├─ 2402.07551v1.Pearcey_integrals__Stokes_lines_and_exact_baryonic_layers_in_the_low_energy_limit_of_QCD.pdf
├─ 2402.07555v1.Thermodynamically_consistent_modelling_of_viscoelastic_solids_under_finite_strain.pdf
├─ 2402.07566v1.The_DarkSide_20k_experiment.pdf
├─ 2402.07570v1.Only_the_Curve_Shape_Matters__Training_Foundation_Models_for_Zero_Shot_Multivariate_Time_Series_Forecasting_through_Next_Curve_Shape_Prediction.pdf
├─ 2402.07571v1.LISA_Definition_Study_Report.pdf
├─ 2402.07577v1.Topic_Modeling_as_Multi_Objective_Contrastive_Optimization.pdf
├─ 2402.07584v1.Privacy_Optimized_Randomized_Response_for_Sharing_Multi_Attribute_Data.pdf
├─ 2402.07585v1.Identifying_architectural_design_decisions_for_achieving_green_ML_serving.pdf
├─ 2402.07591v1.A_Big_Ring_on_the_Sky.pdf
├─ 2402.07594v1.Foundational_Inference_Models_for_Dynamical_Systems.pdf
├─ 2402.07597v1.Trustworthy_SR__Resolving_Ambiguity_in_Image_Super_resolution_via_Diffusion_Models_and_Human_Feedback.pdf
├─ 2402.07599v1.Interactive_singing_melody_extraction_based_on_active_adaptation.pdf
├─ 2402.07600v1.Optical_Routing_with_Binary_Optimisation_and_Quantum_Annealing.pdf
├─ 2402.07610v1.Step_On_Feet_Tuning__Scaling_Self_Alignment_of_LLMs_via_Bootstrapping.pdf
├─ 2402.07616v1.Anchor_based_Large_Language_Models.pdf
├─ 2402.07625v1.AutoMathText__Autonomous_Data_Selection_with_Language_Models_for_Mathematical_Texts.pdf
├─ 2402.07629v1.Logistic_Multidimensional_Data_Analysis_for_Ordinal_Response_Variables_using_a_Cumulative_Link_function.pdf
├─ 2402.07630v1.G_Retriever__Retrieval_Augmented_Generation_for_Textual_Graph_Understanding_and_Question_Answering.pdf
├─ 2402.07640v1.Synthesizing_Sentiment_Controlled_Feedback_For_Multimodal_Text_and_Image_Data.pdf
├─ 2402.07642v1.A_Flow_based_Credibility_Metric_for_Safety_critical_Pedestrian_Detection.pdf
├─ 2402.07645v1.Detecting_the_Clinical_Features_of_Difficult_to_Treat_Depression_using_Synthetic_Data_from_Large_Language_Models.pdf
├─ 2402.07647v1.GRILLBot_In_Practice__Lessons_and_Tradeoffs_Deploying_Large_Language_Models_for_Adaptable_Conversational_Task_Assistants.pdf
├─ 2402.07658v1.The_Sound_of_Healthcare__Improving_Medical_Transcription_ASR_Accuracy_with_Large_Language_Models.pdf
├─ 2402.07673v1.A_Computational_Model_of_the_Electrically_or_Acoustically_Evoked_Compound_Action_Potential_in_Cochlear_Implant_Users_with_Residual_Hearing.pdf
├─ 2402.07680v1.AYDIV__Adaptable_Yielding_3D_Object_Detection_via_Integrated_Contextual_Vision_Transformer.pdf
├─ 2402.07681v1.Large_Language_Models__Ad_Referendum___How_Good_Are_They_at_Machine_Translation_in_the_Legal_Domain_.pdf
├─ 2402.07682v1.Auxiliary_Tasks_to_Boost_Biaffine_Semantic_Dependency_Parsing.pdf
├─ 2402.07685v1.Contrastive_Multiple_Instance_Learning_for_Weakly_Supervised_Person_ReID.pdf
├─ 2402.07688v1.CyberMetric__A_Benchmark_Dataset_for_Evaluating_Large_Language_Models_Knowledge_in_Cybersecurity.pdf
├─ 2402.07689v1.OrderBkd__Textual_backdoor_attack_through_repositioning.pdf
├─ 2402.07694v1.Cosmology_at_the_Field_Level_with_Probabilistic_Machine_Learning.pdf
├─ 2402.07708v1.Signed_Distance_Field_based_Segmentation_and_Statistical_Shape_Modelling_of_the_Left_Atrial_Appendage.pdf
├─ 2402.07712v1.Model_Collapse_Demystified__The_Case_of_Regression.pdf
├─ 2402.07715v1.Assembly_bias_in_eBOSS.pdf
├─ 2402.07718v1.Local_Centrality_Minimization_with_Quality_Guarantees.pdf
├─ 2402.07721v1.LoRA_drop__Efficient_LoRA_Parameter_Pruning_based_on_Output_Evaluation.pdf
├─ 2402.07722v1.Path_Integral_Monte_Carlo_Study_of_a_Doubly_Dipolar_Bose_Gas.pdf
├─ 2402.07726v1.Unsupervised_Sign_Language_Translation_and_Generation.pdf
├─ 2402.07729v1.AIR_Bench__Benchmarking_Large_Audio_Language_Models_via_Generative_Comprehension.pdf
├─ 2402.07733v1.Tuning_Structural_and_Electronic_Properties_of_Metal_Organic_Framework_5_by_Metal_Substitution_and_Linker_Functionalization.pdf
├─ 2402.07736v1.Multimodal_Learned_Sparse_Retrieval_for_Image_Suggestion.pdf
├─ 2402.07739v1.Task_conditioned_adaptation_of_visual_features_in_multi_task_policy_learning.pdf
├─ 2402.07742v1.Asking_Multimodal_Clarifying_Questions_in_Mixed_Initiative_Conversational_Search.pdf
├─ 2402.07744v1.Towards_Unified_Alignment_Between_Agents__Humans__and_Environment.pdf
├─ 2402.07748v1.The_GALAH_survey__Elemental_abundances_in_open_clusters_using_joint_effective_temperature_and_surface_gravity_photometric_priors.pdf
├─ 2402.07754v1.Diffusion_of_Thoughts__Chain_of_Thought_Reasoning_in_Diffusion_Language_Models.pdf
├─ 2402.07757v1.Towards_an_Understanding_of_Stepwise_Inference_in_Transformers__A_Synthetic_Graph_Navigation_Model.pdf
├─ 2402.07759v1.Robust_and_accurate_simulations_of_flows_over_orography_using_non_conforming_meshes.pdf
├─ 2402.07760v1.The_Strength_and_Shapes_of_Contact_Binary_Objectcts.pdf
├─ 2402.07762v1.Scalable_Structure_Learning_for_Sparse_Context_Specific_Causal_Systems.pdf
├─ 2402.07767v1.Text_Detoxification_as_Style_Transfer_in_English_and_Hindi.pdf
├─ 2402.07769v1.Observations_of_the_new_meteor_shower_from_comet_46P_Wirtanen.pdf
├─ 2402.07770v1.Quantitative_knowledge_retrieval_from_large_language_models.pdf
├─ 2402.07773v1.Relativistic_corrections_to_prompt_double_charmonium_hadroproduction_near_threshold.pdf
├─ 2402.07776v1.TELLER__A_Trustworthy_Framework_for_Explainable__Generalizable_and_Controllable_Fake_News_Detection.pdf
├─ 2402.07777v1.Novel_Low_Complexity_Model_Development_for_Li_ion_Cells_Using_Online_Impedance_Measurement.pdf
├─ 2402.07779v1.Finding_product_sets_in_some_classes_of_amenable_groups.pdf
├─ 2402.07788v1.Multi_Intent_Attribute_Aware_Text_Matching_in_Searching.pdf
├─ 2402.07792v1.Empowering_Federated_Learning_for_Massive_Models_with_NVIDIA_FLARE.pdf
├─ 2402.07793v1.Tuning_Free_Stochastic_Optimization.pdf
├─ 2402.07797v1.Computing_Nash_Equilibria_in_Potential_Games_with_Private_Uncoupled_Constraints.pdf
├─ 2402.07812v1.Retrieval_Augmented_Thought_Process_as_Sequential_Decision_Making.pdf
├─ 2402.07817v1.Injecting_Wiktionary_to_improve_token_level_contextual_representations_using_contrastive_learning.pdf
├─ 2402.07818v1.Differentially_Private_Zeroth_Order_Methods_for_Scalable_Large_Language_Model_Finetuning.pdf
├─ 2402.07819v1.A_Benchmark_Grocery_Dataset_of_Realworld_Point_Clouds_From_Single_View.pdf
├─ 2402.07824v1.Uranus_s_influence_on_Neptune_s_exterior_mean_motion_resonances.pdf
├─ 2402.07825v1.Random_optimization_problems_at_fixed_temperatures.pdf
├─ 2402.07827v1.Aya_Model__An_Instruction_Finetuned_Open_Access_Multilingual_Language_Model.pdf
├─ 2402.07835v1.Carrier_Mobility_and_High_Field_Velocity_in_2D_Transition_Metal_Dichalcogenides__Degeneracy_and_Screening.pdf
├─ 2402.07838v1.2D_MoS2_under_switching_field_conditions__study_of_high_frequency_noise_from_velocity_fluctuations.pdf
├─ 2402.07840v1.Creating_pair_plasmas_with_observable_collective_effects.pdf
├─ 2402.07841v1.Do_Membership_Inference_Attacks_Work_on_Large_Language_Models_.pdf
├─ 2402.07844v1.Mercury__An_Efficiency_Benchmark_for_LLM_Code_Synthesis.pdf
├─ 2402.07859v1.Lissard__Long_and_Simple_Sequential_Reasoning_Datasets.pdf
├─ 2402.07861v1.TOI_1199__b_and_TOI_1273__b__Two_new_transiting_hot_Saturns_detected_and_characterized_with_SOPHIE_and_TESS.pdf
├─ 2402.07862v1.AI_Augmented_Predictions__LLM_Assistants_Improve_Human_Forecasting_Accuracy.pdf
├─ 2402.07865v1.Prismatic_VLMs__Investigating_the_Design_Space_of_Visually_Conditioned_Language_Models.pdf
├─ 2402.07867v1.PoisonedRAG__Knowledge_Poisoning_Attacks_to_Retrieval_Augmented_Generation_of_Large_Language_Models.pdf
├─ 2402.07871v1.Scaling_Laws_for_Fine_Grained_Mixture_of_Experts.pdf
├─ 2402.07872v1.PIVOT__Iterative_Visual_Prompting_Elicits_Actionable_Knowledge_for_VLMs.pdf
├─ 2402.07874v1.Factorizating_the_Brauer_monoid_in_polynomial_time.pdf
├─ 2402.07876v1.Policy_Improvement_using_Language_Feedback_Models.pdf
├─ 2402.07877v1.WildfireGPT__Tailored_Large_Language_Model_for_Wildfire_Analysis.pdf
├─ 2402.07879v1.3D_physical_structure_and_angular_expansion_of_the_remnant_of_the_recurrent_nova_T_Pyx.pdf
├─ 2402.07891v1.Label_Efficient_Model_Selection_for_Text_Generation.pdf
├─ 2402.07893v1.The_TESS_Keck_Survey_XXI__13_New_Planets_and_Homogeneous_Properties_for_21_Subgiant_Systems.pdf
├─ 2402.07896v1.Suppressing_Pink_Elephants_with_Direct_Principle_Feedback.pdf
├─ 2402.07897v1.A_holographic_mobile_based_application_for_practicing_pronunciation_of_basic_English_vocabulary_for_Spanish_speaking_children.pdf
└─ 2402.07899v1.A_systematic_investigation_of_learnability_from_single_child_linguistic_input.pdf
├─ Video Understanding with Large Language Models_2024_jan_4.pdf
├─ lec1.pptx
└─ self_reqardining_language_model.pdf
├─ University_of_Pittsburgh
├─ class1.pdf
├─ class10.pdf
├─ class11.pdf
├─ class12.pdf
├─ class13.pdf
├─ class14.pdf
├─ class15.pdf
├─ class16.pdf
├─ class17.pdf
├─ class18.pdf
├─ class19.pdf
├─ class2.pdf
├─ class20.pdf
├─ class21.pdf
├─ class22.pdf
├─ class4.pdf
├─ class5.pdf
├─ class6.pdf
├─ class7.pdf
├─ class8.pdf
└─ class9.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
├─ chunk
└─ pdf
├─ 1706.03762.pdf
├─ 1810.04805.pdf
├─ 2207.09238.pdf
├─ L10_Train2.pdf
├─ L11_Train3.pdf
├─ L12_RNN.pdf
├─ L13_Transformer.pdf
├─ L14_LLM.pdf
├─ L15_Software.pdf
├─ L16_Vision.pdf
├─ L17_Generative1.pdf
├─ L18_Generative2.pdf
├─ L19_SSL.pdf
├─ L1_Intro.pdf
├─ L20_VLM.pdf
├─ L21_Fei.pdf
├─ L22_GNN.pdf
├─ L23_RL1.pdf
├─ L24_RL2.pdf
├─ L26_Final.pdf
├─ L2_LinearClassifiers.pdf
├─ L3_LossFunctions.pdf
├─ L4_GradientDescent_NNs.pdf
├─ L5_AutoDiff_DNN_Jacobians.pdf
├─ L6_Project.pdf
├─ L7_Jacobian_Conv.pdf
├─ L8_CNN.pdf
├─ L9_CNN_Train1.pdf
├─ RLbook2018.pdf
└─ nature14539.pdf
├─ colorado
├─ Deeplearning
├─ 01-Introduction.pdf
├─ 02-ArtificialNeurons.pdf
├─ 03-Feedforward_NN.pdf
├─ 04-NN_Training.pdf
├─ 05-NN_Training.pdf
├─ 06-ConvolutionalNeuralNetworks.pdf
├─ 07-CV_and_ImageClassification.pdf
├─ 08-Regularization.pdf
├─ 09-PretrainedFeaturesAndFineTuning.pdf
├─ 10-DetectionAndSegmentation.pdf
├─ 11-RecurrentNeuralNetworks.pdf
├─ 12-WordEmbeddings.pdf
├─ 13-Attention.pdf
├─ 14-Transformers.pdf
├─ 15-PopularTransformers.pdf
├─ 16-VisualQuestionAnswering.pdf
├─ 17-ImageCaptioning.pdf
├─ 18-VisualDialog.pdf
├─ 19_TransferLearning.pdf
├─ 20_TransferLearning.pdf
├─ 21-ResponsibleDL.pdf
├─ 22_SpeechAndNeuralSearch.pdf
├─ 23_ModelCompression.pdf
├─ 24-EfficientLearning.pdf
└─ 25-ReinforcementLearning.pdf
├─ deep_learning_2019
├─ 01-introduction.pdf
├─ 02-introduction.pdf
├─ 03-learning.pdf
├─ 04-learning.pdf
├─ 05-autodiff.pdf
├─ 06-optimization.pdf
├─ 07-regularization.pdf
├─ 08-convolutional-networks.pdf
├─ 09-convolutional-networks.pdf
├─ 10-recurrent-networks.pdf
├─ 11-recurrent-networks.pdf
├─ 12-unsupervised.pdf
├─ 13-generative-models.pdf
├─ 14-generative-models.pdf
├─ 16-autoregression-and-density-estimation.pdf
├─ 16-odenets.pdf
├─ 17-deep-learning-and-nlp.pdf
├─ 17-transformer.pdf
├─ 18-deep-learning-software.pdf
├─ 19-deep-learning-software.pdf
├─ 20-graph-convolutional-networks.pdf
├─ 21-bacon-of-the-wisdom-of-the-ancients-daedalus.pdf
├─ 21-deep-learning-and-society.pdf
├─ 21-technological-challenges-to-liberalism.pdf
├─ 22-graph-convolutional-networks.pdf
├─ autodiff1.pdf
├─ backprop1.pdf
├─ language-models.pdf
├─ mikolov_interspeech2010_IS100722.pdf
└─ worksheet3.pdf
└─ machine_learning
├─ 01a.pdf
├─ 01b.pdf
├─ 01c.pdf
├─ 02a.pdf
├─ 02b.pdf
├─ 02c.pdf
├─ 03a.pdf
├─ 03b.pdf
├─ 04.pdf
├─ 05a.pdf
├─ 05b.pdf
├─ 06a.pdf
├─ 06b.pdf
├─ 07a.pdf
├─ 07b.pdf
├─ 08a.pdf
├─ 08b.pdf
├─ 09a.pdf
├─ 09b.pdf
├─ 09c.pdf
├─ 10a.pdf
├─ 10b.pdf
├─ 11a.pdf
├─ 11b.pdf
├─ 12a.pdf
├─ 13a.pdf
├─ 13b.pdf
├─ 13c.pdf
├─ 14a.pdf
├─ 15a.pdf
├─ 16a.pdf
├─ 16b.pdf
├─ 17a.pdf
├─ 17b.pdf
├─ 18a.pdf
├─ 18b.pdf
├─ 19a.pdf
├─ 19b.pdf
├─ 20a.pdf
├─ 20b.pdf
├─ 21a.pdf
├─ 21b.pdf
├─ 22a.pdf
├─ 22b.pdf
├─ LeastAngle_2002.pdf
├─ Rocha-TNNLS-2013.pdf
├─ ciml-v0_9-ch13.pdf
├─ lazysgdregression.pdf
├─ logreg.pdf
├─ mitchell-theory.pdf
├─ neal_sampling.pdf
├─ nips01-discriminativegenerative.pdf
├─ smo-book.pdf
└─ svmtutorial.pdf
├─ cs131-class-notes.pdf
├─ data-08-00141-v2.pdf
├─ illinois
└─ pdf
├─ 1.pdf
├─ 10.pdf
├─ 11.pdf
├─ 13.pdf
├─ 14.pdf
├─ 15.pdf
├─ 16.pdf
├─ 17.pdf
├─ 18.pdf
├─ 2.pdf
├─ 22.pdf
├─ 23.pdf
├─ 24.pdf
├─ 26.pdf
├─ 27.pdf
├─ 3.pdf
├─ 4.pdf
├─ 5.pdf
├─ 6.pdf
├─ 7.pdf
├─ 8.pdf
├─ 9.pdf
├─ Lecture01.pdf
├─ Lecture02.pdf
├─ Lecture03.pdf
├─ Lecture04.pdf
├─ Lecture05.pdf
├─ Lecture06.pdf
├─ Lecture07.pdf
├─ Lecture08.pdf
├─ Lecture09.pdf
├─ Lecture10.pdf
├─ Lecture11.pdf
├─ Lecture12.pdf
├─ Lecture13.pdf
├─ Lecture14.pdf
├─ Lecture15.pdf
├─ Lecture16.pdf
├─ Lecture17.pdf
├─ Lecture18.pdf
├─ Lecture19.pdf
├─ Lecture20.pdf
├─ Lecture21.pdf
├─ Lecture22.pdf
├─ Lecture23.pdf
├─ Lecture24.pdf
├─ Lecture25.pdf
├─ Lecture26.pdf
├─ Lecture27.pdf
├─ Lecture29.pdf
└─ SteedmanBaldridgeNTSyntax.pdf
├─ lec22.pdf
├─ mlfs_tutorial_nlp_transformer_ssl_updated.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
├─ 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
├─ stable_diffusion_a_tutorial.pdf
├─ stable_diffusion_a_tutorial.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
├─ cs239
└─ pdf
├─ hw1_introduction.pdf
├─ lecture_1.pdf
├─ lecture_10.pdf
├─ lecture_11.pdf
├─ lecture_12.pdf
├─ lecture_16_1.pdf
├─ lecture_16_2.pdf
├─ lecture_2.pdf
├─ lecture_3.pdf
├─ lecture_4.pdf
├─ lecture_5.pdf
├─ lecture_6.pdf
├─ lecture_9.pdf
└─ llm_attacks.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