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AI_LLM_Stanford_CS229 | Stanford_CS229_Machine_Learning_I_Introduction_I_2022_I_Lecture_1.txt | So I am Tony Ma. This quarter, we are going to have two instructors-- me and Chris. I am Tony Ma, I work on machine learning, machine learning theory, including the theory for different topics in machine learning, reinforced learning, repetition learning, supervised learning, so on and so forth. I guess I would like Ch... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_10_Generalization_bounds_for_deep_nets.txt | So last time we have talked about covering number. So covering number is a upper bound for the Rademacher complexity. And then our goal is to bound covering numbers because this is a new tool for bounding the Rademacher complexity. And we have discussed what other bounds are linear models-- I didn't show any of the pro... |
AI_LLM_Stanford_CS229 | Stanford_CS330_I_Advanced_MetaLearning_2_LargeScale_MetaOptimization_l_2022_I_Lecture_10.txt | Hi, everyone. My name is Yoonho Lee. I'm a TA for this course. And it's my first time giving a lecture. So hopefully everything goes well. Yeah, we're going to be talking about the second edition of advanced meta learning topics. I think on Monday, Chelsea talked about memoization and task construction in meta learning... |
AI_LLM_Stanford_CS229 | Stanford_CS229_Machine_Learning_I_Supervised_learning_setup_LMS_I_2022_I_Lecture_2.txt | So hello. Welcome to 229. So we're starting a block of three lectures that I get the privilege of spending some time with you and kind of walking you through the building blocks and basics. Before I get into the plan for those three lectures, I want to make sure we understand a couple of logistics. So I posted somethin... |
AI_LLM_Stanford_CS229 | Stanford_CS330_Deep_MultiTask_Meta_Learning_MultiTask_Learning_Basics_I_2022_I_Lecture_2.txt | So the plan for today as I mentioned in the previous lecture, we're going to try to start with the basics. And so this means that today we'll be talking about methods that were starting to be developed in the ancient times of the 1990s. And then starting I think next week, we'll get into more newer stuff. That said, ev... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_7_Challenges_in_DL_theory_generalization_bounds_for_neural_nets.txt | OK, I guess let's get started. So in this lecture, what we're going to do is that at the beginning we're going to talk about deep learning, especially some of the challenges in deep learning theory. And then in the next probably 5 to 10 lectures, we are going to discuss different aspects about deep learning, I guess. Y... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_11_Alllayer_margin.txt | So last time we talked about the generalization bounds. And today we are going to talk about some better generalization bound for deep networks. So recall that last time what we did was that we show something like the Rademacher complexity is bonded by something like this, times some polynomial of the norms of the widt... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_6_Margin_theory_and_Rademacher_complexity_for_linear_models.txt | Well, hello, everyone. So I guess-- so in the next-- I guess, in this lecture, what we're going to do is we're going to bound Rademacher complexity by some concrete formula for concrete models. And by concrete models, I really just mean linear models for this lecture. And in the few lectures later, we're going to talk ... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_1_Overview_supervised_learning_empirical_risk_minimization.txt | OK, so let's get started. So the formulation-- so most of this course will be about supervised learning. So in some part, we're going to talk about unsupervised learning. But I think maybe like 80 of the lectures will be about supervised learning. So this is about supervised learning. OK. So let me just-- so we have so... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_8_Refined_generalization_bounds_for_neural_nets_Kernel_methods.txt | OK, so let's get started. So I think the last time what we were left was on-- I think we covered the weaker generalization bond. And then, today we are going to provide a stronger generalization bound for the neural network. Let me just double check whether I-- sorry. Somehow I got confused where I'm left. OK, cool, co... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_17_Implicit_regularization_effect_of_the_noise.txt | OK, cool. Let's get started. So I guess today we're going to talk about implicit regularization of noise. And the plan today is that because this is a pretty challenging topic and I think the research community is still, in some sense, doing research on this-- so we have some results. It's pretty complicated. So what I... |
AI_LLM_Stanford_CS229 | The_Rise_of_The_Machines_John_Etchemendy_and_FeiFei_Li_on_Our_AI_Future_Uncommon_Knowledge.txt | The year was 1956, and the place was Dartmouth College, in a research proposal, a math professor used a term that was then entirely new and entirely fanciful, artificial intelligence, there's nothing fanciful about AI anymore. The directors of the Stanford Institute for Human Centered Artificial Intelligence, John Etch... |
AI_LLM_Stanford_CS229 | Stanford_CS330_I_Unsupervised_PreTrainingContrastive_Learning_l_2022_I_Lecture_7.txt | So to start to get into today's content, so far, we've been talking a lot about few-shot learning by using meta learning. And the problem setup for this was we were given data from some number of training tasks. And we wanted to quickly solve a new task more quickly, more proficiently, or more stably. And we reviewed a... |
AI_LLM_Stanford_CS229 | Stanford_CS330_Deep_MultiTask_Meta_Learning_What_is_multitask_learning_I_2022_I_Lecture_1.txt | So today, we will jump into the goals of the course and the logistics of the course and also talk a little bit about what multi-task and meta-learning is and why we might want to study it. Before we get started, I want to make some introductions. So my name is Chelsea. I am the main instructor for the course. And we al... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_2_Asymptotic_analysis_uniform_convergence_Hoeffding_inequality.txt | OK, cool. Let's get started. OK, so it's kind of complicated, right? It's kind of amazing, right? This technology is so advanced. So you can do all of these things together. But I still have to do them one by one. I have 10 action items-- maybe more than 10. I need to also connect with Wi-Fi. That's actually something ... |
AI_LLM_Stanford_CS229 | Stanford_CS330_Deep_MultiTask_Meta_Learning_Frontiers_and_Open_Challenges_I_2022_I_Lecture_16.txt | A couple of logistics. First, the project poster session is on Wednesday next week. We'll be posting details on Ed very soon. It'll actually be broken up. Because we have so many students in the class, it'll actually be broken up into two different sessions, and you'll be assigned to one of the two sessions. And so, it... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_16_Implicit_regularization_in_classification_problems.txt | OK. Hi, everyone. Yeah, let's get started. So I guess today, we're going to talk about-- continue to talk about the implicit regularization. So last time we have talked about the implicit regularization of initialization, and today-- this is last lecture. Actually, last week we-- in the last two lectures, we have talke... |
AI_LLM_Stanford_CS229 | Stanford_CS330_Deep_MultiTask_Meta_Learning_Lifelong_Learning_I_2022_I_Lecture_15.txt | So today, we're going to be talking about lifelong learning. And lifelong learning is an area that-- actually, I feel like the term lifelong learning means a lot of different things. And so we're going to talk a little bit about how the problem statement isn't super well defined in some ways. We'll also talk about some... |
AI_LLM_Stanford_CS229 | Stanford_CS229_I_Societal_impact_of_ML_Guest_lecture_by_Prof_James_Zou_I_2022_I_Lecture_18.txt | So I'll be telling you about some of the applications of machine learning, especially in health care settings. So I'm assistant professor here at Stanford. My name is James Zou, and a lot of my group works on actually developing and deploying the AI systems for biomedical and for health care applications. So feel free ... |
AI_LLM_Stanford_CS229 | Stanford_CS330_I_Unsupervised_Pretraining_for_Fewshot_Learning_l_2022_I_Lecture_8.txt | I'm going to be revisiting some unsupervised pre-training stuff today. This is reconstruction-based methods as opposed to the contrastive methods that we looked at on Monday. All right, so the plan for today, we're going to do a little recap on what Chelsea talked about on Monday. So we're going to talk about the unsup... |
AI_LLM_Stanford_CS229 | Gemini_15_Pro_UNLIKE_Any_Other_AI_Fully_Tested.txt | today I'm going to be testing Gemini 1.5 Pro with a million token context window it is Google's Cutting Edge Frontier large language model apparently it's really good but what makes it special is that massive context window so we're going to see how good it really is let's get into the test so I'm going to be using AI ... |
AI_LLM_Stanford_CS229 | 10_ML_algorithms_in_45_minutes_machine_learning_algorithms_for_data_science_machine_learning.txt | if you have an interview coming up and you want to revise 10 most important machine learning algorithms real quick you will not find a better video than this let's go ahead and do the revision of 10 most frequent used ml algorithms these are the 10 algorithms I am going to explain you how they work and what are their p... |
AI_LLM_Stanford_CS229 | Stanford_CS330_Deep_MultiTask_Meta_Learning_Transfer_Learning_Meta_Learning_l_2022_I_Lecture_3.txt | Before we get started, a couple of logistical things. Homework Zero is due tonight at 11:59. Homework One has also been posted now and it will be due on Wednesday next week. We are also going to be posting a number of resources for your project today. First, we're going to be posting a number of project ideas. So if yo... |
AI_LLM_Stanford_CS229 | Stanford_CS330_Deep_MultiTask_Meta_Learning_Domain_Adaptation_l_2022_I_Lecture_13.txt | So today we're going to be talking about domain adaptation. And we'll get into what that means, as well as a few different algorithms for doing that. And the goal for the end of the lecture is to understand different domain adaptation methods and when you might use one method versus another method. Now, what is domain ... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_20_Spectral_clustering.txt | OK. I guess let's get started. This is the last lecture of this course. I guess we're going to continue with the spectral approach for clustering. So I'll provide some of the reviews of the last lectures. So last lecture, I think we did the stochastic block model, and one of the main findings is that if you do eigendec... |
AI_LLM_Stanford_CS229 | A_Hackers_Guide_to_Language_Models.txt | hi I am Jeremy Howard from fast.ai and this is a hacker's guide to language models when I say a hacker's guide what we're going to be looking at is a code first approach to understanding how to use language models in practice so before we get started we should probably talk about what is a language model I would say th... |
AI_LLM_Stanford_CS229 | Stanford_CS330_Deep_MultiTask_Meta_Learning_Percy_Liang_Guest_Lecture_I_2022_I_Lecture_17.txt | OK, hi, everyone. It's my pleasure to introduce Percy, who's giving a guest lecture. For those of you who are in the course, just a reminder that the poster session is on Wednesday next week, and your final project is due the following, two weeks from two days ago, or on Monday. But I'm really excited to introduce Perc... |
AI_LLM_Stanford_CS229 | Large_Language_Models_from_scratch.txt | hello everyone in this video you'll learn all about large language models let's start with that autocomplete feature on your mobile phone did you ever wonder how it works the suggested word here is the it's the most used word in the english language let's type y next there are a number of words that start with t y if y... |
AI_LLM_Stanford_CS229 | Leonard_Susskind_ER_EPR_or_Whats_Behind_the_Horizons_of_Black_Holes_1_of_2.txt | [Music] Stanford University Let's uh begin I'm going to begin with my interests and this is very rapidly going to get into what I'm interested in and what I've been working on but let's uh let's talk in a little bit of generalities first the subject is quantum mechanics and the subject is gravity and the real relations... |
AI_LLM_Stanford_CS229 | Stanford_CS330_Deep_MultiTask_Meta_Learning_NonParametric_FewShot_Learning_l_2022_I_Lecture_6.txt | So our plan for today is primarily we're going to focus on what I'll refer to as non-parametric few-shot short learning methods. This is a pretty cool class of methods that actually seems to work really, really well for few-shot classification problems. And it will also be part of homework 2, in addition to some of the... |
AI_LLM_Stanford_CS229 | Statistics_for_Data_Science_Probability_and_Statistics_Statistics_Tutorial_PhD_Stanford.txt | data science and machine learning is the hardest job of the 21st century with an average salary of $120,000 per year according to LinkedIn the data science job profile is among the top five jobs in the entire world now if you want to for into the world of data science you need to have good command over statistics as it... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_18_Unsupervised_learning_mixture_of_Gaussians_moment_methods.txt | OK. So I guess let's get started. So today this lecture, we are going to discuss a few small stuff that are remained-- that are kind of left from previous lectures, and then we're going to move on to unsupervised learning. So I guess the first thing is recall that last time, we talked about implicit regularization of t... |
AI_LLM_Stanford_CS229 | Stanford_CS330_Deep_MultiTask_Meta_Learning_Bayesian_MetaLearning_l_2022_I_Lecture_12.txt | On Monday, we talked a lot about variational inference and how do you optimize for complex distributions of data? And today, we're going to actually put some of that into practice in the context of meta-learning algorithms. And so, specifically, we'll, again, try to motivate why we might want Bayesian meta-learning alg... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_13_Neural_Tangent_Kernel.txt | OK, guys, let's get started. So I think last week I spent some time reading the feedback from the survey. I've been going through all of them. So I guess I'm not going to discuss every points there. All the points are well taken. And thanks for all the very helpful feedback. And for some of those, I'm going to improve.... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_15_Implicit_regularization_effect_of_initialization.txt | OK, let's get started. I guess everything's working now. OK, cool. So last time we talked about the-- we started talking about this so-called implicit regularization effect of the optimizers, and last time we discussed the very basic one, which is that if you use initialization zero and then you see gradient descent an... |
AI_LLM_Stanford_CS229 | Stanford_CS330_Deep_MultiTask_Meta_Learning_Domain_Generalization_l_2022_I_Lecture_14.txt | OK. Hello, everyone. Welcome to this lecture. So this is Huaxiu I'm a postdoc in Chelsea's lab. Today, I will give a lecture about domain generalization. So before we start, I will first go through some logistical things. So the project milestone is on Wednesday, and the homework four, which is a optional homework, is ... |
AI_LLM_Stanford_CS229 | How_AI_Could_Empower_Any_Business_Andrew_Ng_TED.txt | When I think about the rise of AI, I'm reminded by the rise of literacy. A few hundred years ago, many people in society thought that maybe not everyone needed to be able to read and write. Back then, many people were tending fields or herding sheep, so maybe there was less need for written communication. And all that ... |
AI_LLM_Stanford_CS229 | Stanford_CS330_Deep_MultiTask_Meta_Learning_Black_Box_Meta_Learning_l_2022_I_Lecture_4.txt | So the plan for today, We're going to be talking about meta-learning. And first I'm going to recap a little bit of what we talked about on Monday with regard to the problem formulation and the general recipe of meta-learning algorithms, and then we're going to actually get into approaches for solving few-shot learning ... |
AI_LLM_Stanford_CS229 | 5분_전_새로운_AI_로봇_인텔리전스_방법_공개_MIT_스탠포드.txt | Introducing diffusion KSP, a brand new AI framework that just unleashed robots decision making skills to reach unprecedented heights in manipulation and generalization. Now layer this on top of the next frontier in computer vision with AI doppelgangers, and you don't have just another incremental improvement, but a bre... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_4_Advanced_concentration_inequalities.txt | So last time, in the last three lectures, we have talked about the basics of uniform convergence. I guess just a very quick review. So I think we have proved that the excess risk, this is lecture 2, is bounded by this. This is a difference between empirical and population. Can I share your screen to the Zoom? Oh, right... |
AI_LLM_Stanford_CS229 | Stanfords_FREE_data_science_book_and_course_are_the_best_yet.txt | I'm going to share with you what is one of the best books on data science that I have ever read it's free and it's important that you see it because well it's getting more and more difficult to find high quality free content I'm going to show you the book tell you why I like it and show you how to use it to get a job s... |
AI_LLM_Stanford_CS229 | Stanford_CS330_Deep_MultiTask_Meta_Learning_OptimizationBased_MetaLearning_l_2022_I_Lecture_5.txt | For today, we're going to recap a little bit from what we talked about last week, from the meta-learning problem set up and black-box meta-learning. And then we're going to get into optimization-based meta-learning where we're actually going to be embedding an optimization process inside another optimization process. A... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_19_Mixture_of_Gaussians_spectral_clustering.txt | OK. I guess, let's get started. Let's see. Is this working? Yes. So I guess last time, we have talked about unsupervised learning. And today, we're going to continue with unsupervised learning. And first, we're going to continue with the moment method. And here we're going to talk about higher order moments. And then, ... |
AI_LLM_Stanford_CS229 | FeiFei_Li_Demis_Hassabis_Using_AI_to_Accelerate_Scientific_Discovery.txt | My name is Fei-Fei Li. I'm the co-director of Stanford Institute of Human-Centered AI. So welcome to Stanford HAI's headquarter space. It's truly an honor to be here. First of all, welcome President Marc Tessier-Lavigne for attending this. And our guest of honor doesn't really need any introduction. But nevertheless, I... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_9_Covering_number_approach_Dudley_Theorem.txt | OK, I guess let's get started. This is working, right? Yeah. So I guess last time where we end up with was-- you view the function class F in some sense as equivalent to a set Q, right? So if you have a function class F, and you can define this Q to be the set of vectors of this form, basically the output vector, which... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_2023_Lec_19_Model_Interpretability_Editing_Been_Kim.txt | today I'm delighted to introduce us our final guest speaker um Bean Kim um being Kim is a staff research scientist at Google brain if you're really into googleology you know those funny words the beginning like staff sort of says how senior you are um and that means that being's a good research scientist um um so uh I ... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_Winter_2021_Lecture_17_Model_Analysis_and_Explanation.txt | Welcome to CS224N, lecture 17, Model Analysis and Explanation. OK, look at us. We're here. Let's start with some course logistics. We have updated the policy on the guest lecture reactions. They're all due Friday, all at 11:59 PM. You can't use late days for this, so please get them in. Watch the lectures. They're awes... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_w_DL_Winter_2021_Lecture_4_Syntactic_Structure_and_Dependency_Parsing.txt | OK. So for today, we're actually going to take a bit of a change of pace from what the last couple of lectures have been about, and we're going to focus much more on linguistics and natural language processing. And so in particular, we're going to start looking at the topic of dependency parsing. And so this is the pla... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_2023_Lecture_16_Multimodal_Deep_Learning_Douwe_Kiela.txt | So today, I'm to introduce our first invited speaker who's Douwe Kiela. Douwe has also been-- as well as being invited and I'll tell his background, he's also in the symbolic systems program, has been an adjunct professor, and has been involved with some students in that role as well. But in his invited role, he's orig... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_Winter_2021_Lecture_7_Translation_Seq2Seq_Attention.txt | Hello, everyone, and welcome back into week four. So for week four, it's going to come in two halves. So today, I'm going to talk about machine translation related topics. And then in the second half of the week, we take a little bit of a break from learning more and more on neural network topics, and talk about final ... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_2023_Hugging_Face_Tutorial_Eric_Frankel.txt | SPEAKER 1: Hi, everyone. Welcome to the 224N Hugging Face Transformers tutorial. So this tutorial is just going to be about using the Hugging Face library. It's really useful and it's a super effective way of being able to use some off-the-shelf NLP models, specifically, models that are kind of transformer-based. And b... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_2023_Lecture_9_Pretraining.txt | Hello, welcome to CS224N. Today we'll be talking about pretraining, which is another exciting topic on the road to modern natural language processing. OK. How is everyone doing? Thumbs up, thumbs side, thumbs down. Wow! No response bias there. All thumbs up. Oh, the side. Nice. I like that honesty. That's good. Well, O... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_2023_Lecture_15_Code_Generation.txt | So this is lecture 15, and today we'll be talking about code generation. So a little bit unusual since we'll be generating unnatural languages this time, but it will connect in a number of ways to natural language generation. So before we start, just a few announcements. The project milestone is due this Thursday. You ... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_Winter_2021_Lecture_18_Future_of_NLP_Deep_Learning.txt | good afternoon folks uh welcome to lecture 18. today we'll be talking about some of the latest and greatest developments in neural nlp where we've come and where we're headed uh chris just to be sure uh are my present and what's visible from this part is it fine you're visible okay uh but none of my presenters right co... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_Winter_2021_Lecture_1_Intro_Word_Vectors.txt | um hi everybody um welcome to stanford cs224 n also known as ling 284 natural language processing with deep learning i'm christopher manning and i'm the main instructor for this class so what we hope to do today is to dive right in so i'm going to spend about 10 minutes talking about the course and then we're going to ... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_2023_Lecture_14_Insights_between_NLP_and_Linguistics.txt | Cool. Hi, everyone Hi, I'm Isabel. I'm a PhD student in the NLP group. It's about connecting insights between NLP and linguistics. Yeah. So hopefully, we're going to learn some linguistics and think about some cool things about language. Some logistics-- we're in the project part of the class, which is cool. We're so e... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_2023_PyTorch_Tutorial_Drew_Kaul.txt | SPEAKER: And so today I kind of just want to cover the fundamentals of PyTorch, really just see what are the similarities between PyTorch and NumPy and Python, which you guys are used to at this point, and see how we can build up a lot of the building blocks that we'll need in order to define more complex models. So sp... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_2023_Lecture_8_SelfAttention_and_Transformers.txt | Hi, everyone. Welcome to CS224N we're about two minutes in. So let's get started. So today, we've got what I think is quite an exciting lecture topic. We're going to talk about self-attention and transformers. So these are some ideas that are the foundation of most of the modern advances in natural language processing ... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_2023_Python_Tutorial_Manasi_Sharma.txt | SPEAKER: All right. Hi, everyone. Welcome to the 224N Python review session. The goal of the session really will be to give you the basics of Python and NumPy in particular that you'll be using a lot in your second homework and the homeworks that will come after that as well. We're sort of taking this tutorial from the... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_Winter_2021_Lecture_15_Add_Knowledge_to_Language_Models.txt | Welcome to CS224N lecture 15. So I'm Megan. And I'm one of the CAs in this course. And I'm also a PhD student working at [INAUDIBLE].. And today I'll be talking about integrating knowledge in language models. So some quick reminders. Your project milestones were due today. So hopefully, you turned those in already or w... |
Stanford_CS224N_Natural_Language_Processing_with_Deep_Learning_2023 | Stanford_CS224N_NLP_with_Deep_Learning_Winter_2021_Lecture_3_Backprop_and_Neural_Networks.txt | hi everyone i'll get started okay so we're now i'm back for the second week of cs224n um on natural language processing with deep learning okay so um for today's lecture what we're going to be looking at is all the math details of doing neural net learning first of all looking at how we can work out by hand um gradient... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_18_3D_Vision_Survey_Part_2.txt | it's literally at the top all right uh yeah perfect okay um so let's see with that I'm going to jump into 3D Vision part two uh for which Mel and I have decided we love Nerfs the most so we're just gonna cover nurse today uh if if you want we do we did make a couple of slides on like monocular depth estimation if you w... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_16_Advanced_Object_Detection_and_Semantic_Segmentation.txt | three you know all right uh we can probably get started um those of you guys that were here for the last two lectures uh Vision Transformers um and the one before that was on like attention uh how that's used in Transformers kind of uh the first part might be like a little bit of an overview uh so yeah we can kind of g... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_12_Diffusion_Models.txt | recording um okay it's 7 10 it's probably a good time to start so today we will be talking about something called diffusion models and this is going to be the last lecture in the series of image generation uh lectures that we have seen in the last few weeks and hopefully this will cap out everything that you've learned... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_10_GANs.txt | okay um so I think let's get started here so today we're going to talk about Gans but before we get started I was wondering if people had uh more questions or if we can run a little bit of review uh from the lecture on Tuesday um if people like questions comments or concerns about um like this idea of like a latent spa... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_6_Advanced_Computer_Vision_Architectures.txt | foreign and I should turn off the zoom background blur Ry options like this oh it does show up uh yeah there's like speaker notes on your screen but there's be careful because I accidentally just put something else in the first longer okay I don't have too many but yeah there's an interview oh this is where it has spea... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_1_Intro_to_Machine_Learning.txt | I didn't see is there oh what I don't know if this works hey out of curiosity can y'all hear me in the back or do I need to figure out the microphone situation are we okay or all right what was that yeah I've never had to use it before though I don't know should I give it a shot do [Music] okay system works I don't kno... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_17_3D_Vision_Survey_Part_1.txt | yeah definitely interrupt just ask questions uh yes yes uh so yes this week I think you guys already got an announcement but we will be talking about 3D instead of self-supervising that's all supervisor will be moved to next week uh cool if you have any questions at any point just like interrupt me uh and I'll be more ... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_3_Intro_to_Deep_Learning_Part_2.txt | oh it's on the website oh it's not linked oh my God I'm so sorry I oh it's at the bottom it's at the bottom yeah um uh come here come here come here uh down at the bottom sorry yeah the slides are the slides have a hyperlink attached to them but not this I apologize um but yeah it's down here it's down here at the bott... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_7_Object_Detection.txt | there it is okay recording in progress also spread all right can you guys hear me I don't know if the mic's really working but it's working all right all right y'all oh let me clear this out of the way all right yeah uh so today we're gonna be oh my God why is it there we go Zoo give me one second you all right there w... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_20_Stylizing_Images.txt | okay let's get started um thanks for coming everyone we'll keep this pretty chill and today should be pretty fun lecture it's on stylizing images um so just one brief announcement so we're planning to release the last homework pretty soon so keep your eye on that and um hopefully we'll send out an ad post very soon for... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_5_Intro_to_Computer_Vision.txt | foreign hey um and welcome to lecture five of the introducibility cow so this lecture is going to be on introductions introduction to cnns we have already given this lecture in person but due to some technical difficulties on our end the video of the lecture did not record so this is a recording of a lecture that we ha... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_4_Intro_to_Pretraining_and_Augmentations.txt | presenting okay share your screen that's what I'm doing oh you are okay hopefully it is yeah stop sharing yeah you should be sharing my screen under your camera until I can decide if I click on slideshow this is still show my camera uh it does I guess I can minimize it do screen sharing are you recording too yeah great... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_11_Advanced_GANs.txt | foreign I think we can get started here um so today we're going to be talking about Gans but specifically in the context of computer vision um just kind of talking about a couple of interesting papers that have come down the line um about like four years ago-ish some scans have sort of fallen out of out of Vogue a litt... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_8_Semantic_Segmentation.txt | progress Ive interesting all right all right uh are you guys can just guess right all right all right y'all uh today we're going to be kind of covering a continuation of Tuesday's lecture um so we're going to be going over ulcers if I could it's a good all right perfect we're going to be going over uh image segmentatio... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_14_Transformers_and_Attention.txt | three you get the previous recording I sent you by the way oh yeah whatever so many reasons thank you foreign it is yeah but alrighty so we can just about get started now um the kind of goal of today is to uh ease into the motivation behind attention um and then kind of how that relates to Transformer models um which a... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_21_Generative_Audio.txt | all right uh today we're going to be talking about intro to generative audio um this kind of deviates from what we've been talking about before um but we're going to kind of tie it into the different kinds of models that we've been talking about um as well as how they can be used for um generative audio um this lesson ... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_9_Autoencoders_VAEs_Generative_Modeling.txt | thank you so I'll work with you today but if you're Amazed by their review as honest as possible and the end of this morning so um all right hey guys so you guys have probably not seen me yet my name is soham I'm a another person involved with machine learning at Berkeley I created some of the content for this course b... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_2_Intro_to_Deep_Learning_Part_1.txt | because foreign wow thank you yeah and I got everyone's attention for a minute here is this on maybe all right I got everyone's attention for too many here uh does anyone have like questions from like the last lecture we kind of like brushed it pretty quick um to try and to try and get as fast into into deep learning a... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_15_Vision_Transformers.txt | medication okay um yeah so I don't know well I might want to shower for it so that's why um okay so uh this lecture is just gonna be about uh Vision Transformers um I think so one thing I think someone on SM was wondering a bit about um about Transformers so just sort of like clearing up some of the specifics and like ... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_13_Intro_to_Sequence_Modeling.txt | foreign okay So today we're going to be talking about sequence models um yeah so like a little bit of motivation for like why we care about sequence models um technically there's there's a lot of different things um that relate to the sequences you have things you know like the weather um the second image on the left i... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_22_Multimodal_Learning.txt | all right so today we're going to be talking about multimodal learning talk a little bit about what multimodal learning is uh and why we do it um I'll talk a little bit about clip and that specific flavor of multimodal representation learning and at the end we'll talk a little bit about the downstream uses of clip what... |
CS_198126_Modern_Computer_Vision_Fall_2022_UC_Berkeley | CS_198126_Lecture_19_Advanced_Vision_Pretraining.txt | good yeah uh so today's lecture is going to be on salt supervised free training for CV I kind of hinted at this lecture when we covered the intro to pre-training lecture in like VQ I mentioned that there will be a dedicated lecture that is entirely going to be covering models that use SSL and CV so since that intro lec... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_18_Weierstrasss_Example_of_a_Continuous_and_Nowhere_Differentiable_Function.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIQUEZ: So we're going to continue with our discussion of the derivative. So now, let me recall the definition we introduced at the end of last time of the derivative. So let I be an interval, meaning it could be open, closed, it could go out to plus infinity, it could go out ... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_7_Convergent_Sequences_of_Real_Numbers.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUEZ: OK, so last time, we were talking about sequences, and I introduced the notion of a limit of a sequence. So we say that x n converges to x if for all epsilon positive there exists an M, a natural number, such that for all little n bigger than or equal to capital M we h... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_5_The_Archimedian_Property_Density_of_the_Rationals_and_Absolute_Value.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUEZ: Last time, so we're talking about a set of real numbers. And last time, I stated the following theorem about the existence and properties of R that make it special. So the theorem is, there exists a unique ordered field with the least upper bound property containing th... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_17_Uniform_Continuity_and_the_Definition_of_the_Derivative.txt | CASEY RODRIGUEZ: So last time we finished by proving Bolzano's intermediate value theorem, which states that a continuous function achieves all values in between the function evaluated at the endpoint. So if I have a continuous function, neither-- I take a value y between f of a and f of b, so either f of a is less tha... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_13_Limits_of_Functions.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUEZ: So, all right, so let's start talking about some math. So there was a last result I was going to prove for series, which is that if you have an absolutely convergent series, and you rearrange it, then that series is also absolutely convergent and converges to the same ... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_9_Limsup_Liminf_and_the_BolzanoWeierstrass_Theorem.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUEZ: All right, so we proved these two theorems last time, and we used them for-- and we had a couple of applications of them. So the first theorem, simple theorem, was that a sequence converges to x if and only if the limit as n goes to infinity of the absolute value of xn... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_21_The_Riemann_Integral_of_a_Continuous_Function.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUEZ: OK. So we're going to continue our discussion of-- is it 1M or 2? It's 1. Of the Riemann integral, which, remember, from the discussion at the end of the last lecture, is a theory of area underneath the graph of a function. So what is that theory? The theory is built u... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_23_Pointwise_and_Uniform_Convergence_of_Sequences_of_Functions.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUEZ: All right. So last time, we proved the fundamental theorem of calculus. And as a consequence, integration by parts of formula and the change of variables formula or use substitution. So let me just recall, for integration by parts, it was that if I have two continuousl... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_20_Taylors_Theorem_and_the_Definition_of_Riemann_Sums.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUEZ: --differentiable as many times as you like. And the derivative at 0 equals 0 for all n. Why do I bring this up? Because then the Taylor polynomial for this function that I've written here at 0-- so this is the Taylor polynomial at 0-- just equals, again, the sum of the... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_16_The_MinMax_Theorem_and_Bolzanos_Intermediate_Value_Theorem.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUEZ: OK, so let's continue with our discussion about continuous functions, no pun intended. First, let me recall a few facts. Well, this is not a definition. But this was a theorem we proved last time. So I won't recall the definition of continuity. But we proved this theor... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_3_Cantors_Remarkable_Theorem_and_the_Rationals_Lack_of_the_Least_Upper_Bound_Property.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUEZ: So here we are again. So I'm going to finish real quick proof of a theorem that I stated last time due to Cantor. So let me recall the setting. So last time, we were finishing up what I had to say about cardinality, which remember, is a notion of size of sets. And at t... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_10_The_Completeness_of_the_Real_Numbers_and_Basic_Properties_of_Infinite_Series.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUEZ: OK, so let's continue our study of sequences of real numbers. So we've seen special types of sequences, monotone sequences, before. And then in the previous lecture, we looked at sequences obtained from sequences, namely the sequences that give you the lim sup and lim ... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_1_Sets_Set_Operations_and_Mathematical_Induction.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUEZ: OK. So I have to admit this is extremely awkward, lecturing to an empty room. So I have to imagine there's somebody on the other end actually listening to me at some point. Perhaps this is what YouTube stars have to go through at some point in their career. So what is ... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_14_Limits_of_Functions_in_Terms_of_Sequences_and_Continuity.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUEZ: Last lecture, we introduced the notion of the limit of a function as x goes to c, which we write limit x arrow c, f of x equals L. What does this mean? This means for all epsilon positive, there exists a delta positive such that for all x in S satisfying 0 is less than... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_15_The_Continuity_of_Sine_and_Cosine_and_the_Many_Discontinuities_of_Dirichlets_Function.txt | CASEY RODRIGUEZ: OK. So let's continue our discussion of continuity, which we began last time, which is-- which I defined last time, and I wrote again here, which intuitively says that if you want to be-- that if x is sufficiently close to number c, then f of x will be very close to f of c. So it connects how the funct... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_2_Cantors_Theory_of_Cardinality_Size.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUEZ: So last time we spoke about-- we covered sets and induction. This time, I want to ask a question about sets, which turns out is actually quite a deep question. I mean, I didn't come up with it myself. This question is at least 150 years old probably. So the question th... |
MIT_18100A_Real_Analysis_Fall_2020 | Lecture_8_The_Squeeze_Theorem_and_Operations_Involving_Convergent_Sequences.txt | [SQUEAKING] [RUSTLING] [CLICKING] CASEY RODRIGUES: So I'm going to prove a few theorems about limits, which will allow us to compute limits, or at least we can use to prove that other non-trivial limits exist using these theorems, rather than using the definition directly. So this first theorem is the easiest theorem i... |
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