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MIT_508J_Biological_Chemistry_II_Spring_2016 | 2_Protein_Synthesis_1.txt | The following content is provided under a Creative Commons license. Your support will help MIT Open Courseware continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT Open Courseware at ocw.mit.edu. ELIZABETH NOLAN: What we'l... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 24_Cholesterol_Homeostasis_4.txt | The following content is provided under a Creative Commons license. Your support will help in MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JOANNE STUBBE: So anyhow,... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 34_Reactive_Oxygen_Species_4_Nucleotide_Metabolism_1.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JOANNE STUBBE: What I want t... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 31_Metal_Ion_Homeostasis_7_Reactive_Oxygen_Species_1.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JOANNE STUBBE: --iron homeos... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | R5_Overview_of_CrossLinking_Including_PhotoReactive_CrossLinking_Methods.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high-quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. ELIZABETH NOLAN: What we're ... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 14_Protein_Degradation_3.txt | NARRATOR: The following content is provided under a Creative Commons license. Your support will help MIT Open Courseware continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseware at ocw.MIT.edu. PROFESSOR: So we ... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 19_Cholesterol_Biosynthesis_1.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JOANNE STUBBE: What I want t... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 23_Cholesterol_Homeostasis_3.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high-quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JOANNE STUBBE: --that Brown ... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 15_PK_and_NRP_Synthases_1.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. ELIZABETH NOLAN: --by talkin... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | R6_Macromolecular_Electron_Microscopy_Applied_to_Fatty_Acid_Synthase.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. EDWARD BRIGNOLE: My name's E... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 35_Nucleotide_Metabolism_2.txt | NARRATOR: The following content is provided under a Creative Commons license. Your support will help MIT Open Courseware continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JOANNE STUBBE: So... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 36_Nucleotide_Metabolism_3.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation, or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JOANNE STUBBE: We're talkin... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 22_Cholesterol_Homeostasis_2.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JOANNE STUBBE: Where we were... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 3_Protein_Synthesis_2.txt | The following content is provided under a Creative Commons license. Your support will help MIT Open Courseware continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT Open Courseware at ocw.mit.edu. ELIZABETH NOLAN: We're goi... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 16_PK_and_NRP_Synthases_2.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high-quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. ELIZABETH NOLAN: We're going... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 30_Metal_Ion_Homeostasis_6.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high-quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JOANNE STUBBE: OK, so what I... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | R1_Determining_Analyzing_and_Understanding_Protein_Structures.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. SHIVA MANDALA: And so just a... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 17_PK_and_NRP_Synthases_3.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high-quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. ELIZABETH NOLAN: Last time w... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 6_Protein_Synthesis_5.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high-quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit Mit OpenCourseWare at ocw.mit.edu. ELIZABETH NOLAN: So where we... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 11_Protein_Folding_4.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. ELIZABETH NOLAN: So where we... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | R12_Mass_Spectrometry_of_the_Cysteine_Proteome.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high-quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JOANNE STUBBE: OK, so I thin... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 32_Reactive_Oxygen_Species_2.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. PROFESSOR: What I want to do... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | R8_Application_of_CRISPR_to_Study_Cholesterol_Regulation.txt | The following content is provided under a Creative Commons license. Your support will help MIT Open Courseware continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT Open Courseware at ocw.mit.edu. JOANNE STUBBE: Because we ... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | R10_MetalBinding_Studies_and_Dissociation_Constant_Determination.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high-quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. ELIZABETH NOLAN: ...going to... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 26_Metal_Ion_Homeostasis_2.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JOANNE STUBBE: So that's whe... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 4_Protein_Synthesis_3.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high-quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. ELIZABETH NOLAN: So last tim... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | 18_PK_and_NRP_Synthases_4.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. ELIZABETH NOLAN: We're going... |
MIT_508J_Biological_Chemistry_II_Spring_2016 | R9_Cholesterol_Homeostasis_and_Sensing.txt | The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high-quality educational resources for free. To make a donation, or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JOANNE STUBBE: This is the ... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Conclusion_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | Hello. We hope you found this course to be enjoyable and rewarding. Now that we're wrapping up, your thoughts might be turning to how you can build on what you've learned to conduct original research, to develop new technologies, and so much more. The material should position you really well to do this. The models for ... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Matrix_Designs_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Hello, everyone. Welcome to Part 2 of our series of screencasts on distributed word representations. The focus of this screencast will be on matrix designs. Let's start with the word-by-word design that we concentrated on in Part 1. So here again, we have a vocabulary along the rows. That same vocabu... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | SNLI_MultiNLI_and_Adversarial_NLI_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone, to part 2 in our series on natural language inference. We're going to focus on the three data sets that we'll be concentrating on this unit, which are SNLI-- the Stanford Natural Language Inference Corpus-- MultiNLI, and Adversarial NLI. I think they're interestingly different, and... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Practical_Finetuning_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome back, everyone. This is part 6 in our series on Contextual Word Representations. We're going to be talking about practical fine tuning. It's time to get hands-on with these parameters we've been talking about. So here's the guiding idea. Your existing architecture, say, for the current origin... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Feature_Representation_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is part 7 in our series on supervised sentiment analysis. The focus of this screencast is on feature representation of data. There are really two things I'd like to do. First, just explore some ideas for effective feature representation in the context of sentiment analysis. An... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Varieties_of_contextual_grounding_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome to part 4 in our series on grounded language understanding. Our topic is varieties of contextual grounding. What I'd really like to do is make connections with additional tasks as a way of drawing out what I think is one of the central insights behind the work that we're doing, which is that ... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Static_Representations_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Hello, everyone, welcome to our final screencast in our unit on distributed word representations. Our topic is going to be deriving static representations from contextual models. That might sound awfully specific, but as you'll see, I think this could be really empowering for you as you work on your ... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Homework_3_Colors_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome back, everyone. This screencast is an overview of the homework and bakeoff associated with our unit on grounded language understanding. More than any of the other assignments, what we're asking you to do here, is essentially develop a fully integrated system, that addresses our task. So the d... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Neural_RSA_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome back, everyone. This is part six in our series on Grounded Language Understanding. We're going to be talking about neural RSA, which is our combination of the Rational Speech Acts model with the kind of machine learning models that we've been focused on for this unit. And I'm hoping that this... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Adversarial_Training_and_Testing_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone to part 3 in our series on analysis methods in NLP. We're going to be talking about adversarial training as well as testing of systems. This is the second of the behavioral evaluation methods that we're considering. We've previously talked about adversarial testing. Adversarial trai... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Evaluation_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | BILL MACCARTNEY: Last time I introduced the task of relation extraction, I described the corpus and the KB that we're going to use. And I proposed a precise formulation of our prediction problem. So now, let's talk about how we're going to measure success on this problem. We need to define a quantitative evaluation tha... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | The_Rational_Speech_Acts_Model_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Hello, everyone. Welcome to part 5 in our series on grounded language understanding. We're going to be talking about the rational speech acts model or RSA. This is an exciting model that was developed by Stanford researchers Mike Frank and Noah Goodman. And it's a chance for us to connect ideas from ... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Course_Overview_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: So here we go. It's a golden age for Natural Language Understanding. Let's start with a little bit of history. So way back when, John McCarthy had assembled a group of top scientists, and he said of this group, "We think that a significant advance can be made in artificial intelligence in one or more... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Vector_Comparison_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome back, everyone. This is part 3 in our series on distributed word representations. We're going to be talking about vector comparison methods. To try to make this discussion pretty intuitive, I'm going to ground things in this running example. On the left, I have a very small vector space model... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Classifier_Metrics_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome back, everyone. This is part 2 in our series on methods and metrics. We're going to be talking about classifier metrics. I'm sort of assuming that the metrics I'll be discussing are broadly familiar to us. I think that's a chance for us to step back and be reflective about what values these f... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | DynaSent_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRIS POTTS: Hello, everyone. Welcome to part 4 of our series on supervised sentiment analysis. This is the second screencast in the series that is focused on a dataset for sentiment. And that dataset is DynaSent. This video could be considered an optional element in the series. I'm offering it for two reasons really. ... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | General_Practical_Tips_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Hello, everyone, welcome to part 2 in our series on supervised sentiment analysis. The screencast is going to focus on some general practical tips for doing work in this space, especially focused on setting up a project and doing kind of pre-processing of your data. So first I just wanted to give you... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Writing_NLP_papers_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Hello, everyone. Welcome to part 2 in our series on presenting your research. We're going to be talking about writing papers in our fields. To start, let's look at the outline of a typical NLP paper. By and large, these are either four or eight page papers in a two-column format that you get from the... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Overview_of_Methods_and_Metrics_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRIS POTTS: Welcome, everyone. This is the first screencast in our series on methods and metrics. Fundamentally, what we're trying to do with this unit is give you help with your projects, and specifically give you help with the experimental aspects of your projects. And so the kind of highlight topics for us will be ... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | NLP_Conference_Submissions_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRIS POTTS: Welcome, everyone. This is part three in our series on presenting your research. We're going to be talking about the sometimes thrilling and sometimes agonizing process of submitting your work for publication at an NLP conference. To start, I want to review what's known as the anonymity period for ACL conf... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Transformers_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is part two in our series on contextual word representations. We're going to be talking about the transformer architecture, which is the central piece for all the models we'll be exploring in this unit. Let's dive into the model structure, we'll work through this using a simpl... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Simple_Baselines_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | BILL MACCARTNEY: It's good methodological practice whenever you're starting to build new models to start by evaluating very simple models, which establish baselines to which you can then compare the more sophisticated models that you're going to build later on. So to do that, we're going to start by looking at three si... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Introduction_and_Welcome_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | Hi, I'm Chris Potts. I'm a professor in linguistics at Stanford with a courtesy appointment in computer science and I'm the director of Stanford Center for the Study of Language and Information, which is an interdisciplinary research center focused on logic, language, decision-making, human sentence processing, and com... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Natural_Language_Generation_Metrics_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRIS POTTS: Welcome, everyone. This is part three in our series on methods and metrics. We're going to be talking about metrics for assessing natural language generation systems. We previously talked about classifier metrics, and the issues seem relatively straightforward. As you'll see, assessment for NLG systems is ... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | ELECTRA_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is part 5 in our series on contextual word representations. We're going to be talking about the ELECTRA model. ELECTRA stands for efficiently learning an encoder that classifies token replacements accurately, which is a helpfully descriptive breakdown of a colorfully named mod... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Directions_to_Explore_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | BILL MACCARTNEY: OK. We're underway. We have a simple model with reasonable performance. Where do we go from here? Well, to make further gains we need to stop treating the model as a black box. We need to open it up and get visibility into what it's learned and more importantly, where it still falls down. And then we c... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Problem_Formulation_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | BILL AMCCARTNEY: So I now want to turn to the question of how to formulate our prediction problem precisely. I want to be precise about how we're defining the inputs and outputs of our predictions, and that, in turn, is going to have consequences for how we join the corpus and the KB, how we construct negative examples... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Homework_2_Sentiment_Analysis_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | SPEAKER 1 : Hello everyone. This video is an overview of homework 2, which is on supervised sentiment analysis. And I would actually think of it as an experiment in cross domain sentiment analysis. Let's just walk through this notebook, and I'll try to give you a feel for the problem and our thinking behind it. So the ... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Feature_Attribution_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is part 5 in our series on analysis methods in NLP. We're going to be talking about feature attribution methods. This is fundamentally a powerful tool kit for helping you understand how the features in your model contribute to its output predictions. Our fundamental question h... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Neural_IR_part_2_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | OMAR KHATTAB: Hello, everyone. Welcome to part 4 of our series on NLU and IR. This screencast will be the second among three of our videos on neural information retrieval. Just to recap, this is the functional view of neural IR that we left in the previous screencast. Our model will take a query, and a document, and wi... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | RoBERTa_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome back, everyone. This is part four in our series on Contextual Word Representations. We are going to be talking about a robustly optimized BERT approach a.k.a. RoBERTa. So recall that I finished the BERT screencast by listing out some known limitations of the BERT model, most of which were ide... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Grounded_Language_Understanding_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is part 1 in our series on grounded language understanding. I'm just going to give an overview. With grounding, I feel like we're really getting at the heart of what makes NLU so special for NLP and also for artificial intelligence more broadly. So this is exciting. Let's dive... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Homework_1_Word_Relatedness_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | SPEAKER 1: Hello, everyone. This screencast is going to be a brief playthrough of Homework 1 on word relatedness. I hope to give you a sense for the problem that you're tackling, and also our expectations around the homework questions and the bake-off. So let's dive in here. The overview is just explaining the characte... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Adversarial_Testing_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is part two in our series on analysis methods in NLP. We're going to be talking about adversarial testing. This is an exciting mode because as you'll see with a few dozen carefully created examples, you can learn something really interesting about the systems that you're devel... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Basic_Reweighting_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome back, everyone. This is part 4 on our series on distributed word representations. We're going to be talking about basical reweighting schemes. Essentially, I feel like we've been faithful to the underlying counts of our matrices for too long. It's time to start messing with them. Here are som... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Data_Resources_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | BILL MACCARTNEY: So that's the end of the introduction. Let's now begin to drill down on the data resources that we'll need to launch our investigation. And there are two different kinds of data we need to talk about, the corpus and the KB. Just like any other NLP problem, we need to start with a corpus, a large collec... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | NLU_and_Information_Retrieval_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | Welcome, everyone, to the first screencast in our NLU and information retrieval series. The goal of this introductory screencast is twofold. I will first introduce the IR area. Then I will discuss ways in which NLU and IR can interact productively and focus on how retrieval can be an effective component in defining our... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Neural_IR_part_3_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | OMAR KHATTAB: Hello, everyone. Welcome to part five of our series on NLU and IR. This screencast will be the third among three of our videos on neural IR. In the previous screencast we discussed learning term weights as a paradigm for building neural IR models that are both efficient and effective. We mentioned two suc... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Neural_IR_part_1_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | OMAR KHATTAB: Hello, everyone. Welcome to part 3 of the series. This screencast will be the first of two or three on neural IR. And in it, we'll be exploring the inputs, outputs, training and inference in the context of neural IR. Let's quickly start with a reminder of our setup from the previous screencast. Offline, w... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Data_Organization_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is part 4 in our series on methods and metrics. We're going to be talking about how we organize datasets for the purposes of conducting evaluations in NLP. Let's begin with the classic train/dev/test split. This is a very common format for datasets in our field, especially for... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Classical_IR_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | OMAR KHATTAB: Hello, everyone. Welcome to part 2 of our series on NLU and IR. The screen cast will be a crash course in classical IR, as well as evaluation methods and information retrieval. Let us first define the simplest form of our task, namely ranked retrieval. We will be given a large collection of text documents... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Retrofitting_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Hello everyone. Welcome to part 6 in our series on distributed word representations. This can be considered an optional part but it's on the irresistibly cool idea of retrofitting vectors to knowledge graphs. Here are the central goals. On the one hand, as we've seen, distributional representations a... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Sentiment_Analysis_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | Hello, everyone. This video kicks off our series of screencasts on supervised sentiment analysis. I just want to provide you with an overview of the problem and of the kind of work we'll be doing, and also a rationale for why we'll be doing it. So here's an overview of the entire unit. I want to, in this screencast, mo... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Model_Evaluation_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is Part 5 in our series on methods and metrics. We're going to be talking about essential selected topics in model evaluation in our field. Here's our overview. I'd like to start by talking about baselines and their role in experimental comparisons. Then we'll discuss hyperpar... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Highlevel_Goals_Guiding_Hypotheses_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Hello, everyone. Welcome to the very first screencast of the very first unit of our course. We're going to be talking about distributed word representations or vector representations of words. And for this screencast, I'm just going to cover some high-level goals we have for this unit as well as disc... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Attention_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is part 5 in our series on natural language inference. We're going to be talking about attention mechanisms. Attention was an important source of innovation in the NLI literature, and, of course, it's only grown in prominence since then. Let's begin with some guiding ideas. In... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | sstpy_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Hello, everyone. Welcome to part 5 in our series on supervised sentiment analysis. The focus of this screencast is on the module sst.py, which is included in the course code distribution. It contains a bunch of tools that will let you work fluidly, I hope, with the Stanford Sentiment Treebank and con... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Giving_Talks_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is part 4 of our series on presenting your research. We're going to be talking about the possibly thrilling and possibly nerve-wracking process of giving a conference talk in our field. Let's start with the basic structure of a talk. This is pretty easy. It's probably going to... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Probing_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is part 4 in our series on analysis methods in NLP. We're going to be talking about probing. This is the first of the two structural evaluation methods that we're going to consider. It's time to get really introspective about what our models are doing. Here's an overview of th... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Stanford_Sentiment_Treebank_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome back, everyone. This is part 3 in our series on supervised sentiment analysis. This screencast is going to focus on the Stanford Sentiment Treebank. Let me start with a quick project overview. The associated paper is Socher et al. 2013. I think this paper is a kind of model of open science. A... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Listeners_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone to part 3 in our series on grounded language understanding. Recall that in part 2, we focused on speakers, speakers in our sense, taking on linguistic representations as inputs and generate language on that basis. Listeners are the converse of that. They accept linguistic inputs. An... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Dimensionality_Reduction_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Hello, everyone. Welcome back. This is part 5 in our series on distributed word representations. We're going to be talking about dimensionality reduction techniques. We saw in the previous screencast that reweighting is a powerful tool for finding latent semantic information in count matrices. We're ... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Modeling_Strategies_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRIS POTTS: Welcome, everyone. This is part 4 in our series on Natural Language Inference. We're going to be talking about different modeling strategies. You might think of this screencast as a companion to the associated notebook that explores a lot of different modeling ideas and suggests some architectural variatio... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Overview_of_Analysis_Methods_in_NLP_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is the first screencast in our series on Analysis Methods in NLP. This is one of my favorite units in the course because it's directly oriented toward helping you do even better final projects. Now there's a lot we could discuss under the rubric of analysis methods in NLP. I'v... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Speakers_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is part two in our series on grounded language understanding. Our task for this unit is essentially a natural language generation task. And I've called those speakers, the idea is that speakers go from the world, that is some non-linguistic thing that they're trying to communi... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Contextual_Representation_Models_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRIS POTTS: Welcome, everyone, to our first screencast on contextual word representations. My goal here is to give you an overview for this unit and also give you a sense for the conceptual landscape. Let's start with the associated materials. You might think that the name of the game for this unit is to get you to th... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Hyperparameter_Search_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Hello, everyone. Welcome to part 6 in our series on supervised sentiment analysis. This screencast is going to cover two important methods in this space, hyperparameter search and classifier comparisons. So let's begin with hyperparameter search. And first, I'll just offer the rationale. Let's say th... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | RNN_Classifiers_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome back, everyone. This is part 8 in our series on supervised sentiment analysis, the final screencast in the series. We're going to be talking about recurrent neural network or RNA classifiers. I suppose this is officially our first step into the world of deep learning for sentiment analysis. T... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Relation_Extraction_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | BILL MACCARTNEY: Our topic for today and Wednesday is relation extraction. And this is an exciting topic both because it's a great arena to explore a variety of NLU and machine learning techniques. And because it has so many real world applications as we'll see in a moment. So here's an overview of the next two lecture... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Presenting_Your_Work_Final_Papers_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Hello, everyone. Welcome to the first screencast in our series on presenting your research. The purpose of this series is really to help you do outstanding scholarship in the field of NLP. And I'm also going to try to demystify publishing in the field of NLP. To kick it off, I'd like to focus on the ... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | BERT_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is part three in our series on contextual word representations. We're going to be talking about the BERT model. Which is an innovative and powerful application of the transformer architecture which we covered in the previous screencast. Let's dive into the core model structure... |
Stanford_CS224U_Natural_Language_Understanding_Spring_2021 | Natural_Language_Inference_Stanford_CS224U_Natural_Language_Understanding_Spring_2021.txt | CHRISTOPHER POTTS: Welcome, everyone. This is the first screencast in our series on natural language inference, or NLI. This is one of my favorite problems. What I'd like to do is give you a sense for how the task is formulated and then situate the task within the broader landscape of ideas for NLU. As usual, we have a... |
MIT_HST512_Genomic_Medicine_Spring_2004 | Lecture_11_The_Importance_of_Data_Representation.txt | ALVIN THONG-JUAK KHO: Transforming data, as well as modeling and genomics. It's a very broad category. And I'm sure that in the other, lectures the people who are giving them have touched on several aspects of these same topics, as well. The lecture outline is as follows. The first thing we'll do is to go through two v... |
MIT_HST512_Genomic_Medicine_Spring_2004 | Lecture_12_Pharmacogenomics.txt | JEFFREY MARK DRAZEN: So this is how I view pharmacogenomics. It's the use of genetic information to determine who will respond favorably or unfavorably. And I think, actually, unfavorably may be a better phenotype to a given type of treatment. And the reason I say that is that, especially the condition that I study, as... |
MIT_HST512_Genomic_Medicine_Spring_2004 | Lecture_20_Practical_Genomic_Medicine.txt | ISAAC SAMUEL KOHANE: So what we'll talk about today is a very practical genomic medicine, by which I mean not only stuff that we think is going to be imminent in two years but what it means today to do genomic medicine, nothing future, just today. And if you think that I have stepped across a line into the future, call... |
MIT_HST512_Genomic_Medicine_Spring_2004 | Lecture_1_Genomic_Introduction.txt | ISAAC SAMUEL KOHANE: So the overview of today's class-- I'm going to try to convince you that the future is now, that all this talk about the geniculate revolution is not pie in the sky stuff for venture capitalists for the next 10 years, but it's stuff that is of immediate practicality today. I'll spend a little bit o... |
MIT_HST512_Genomic_Medicine_Spring_2004 | Lecture_3_Measurement_Techniques.txt | ATUL BUTTE: So last week I talked about-- what did I talk about? I talked about an introduction to molecular biology for about 10 or 12 slides. And then we were going pretty quickly I covered a lot of this material on gene measurement techniques. And so we've already had one question, or one request, to talk about some... |
MIT_HST512_Genomic_Medicine_Spring_2004 | Lecture_8_Complex_Traits_What_to_Believe.txt | JOEL HIRSCHHORN: So the topic is basically comple-- the title is Complex Traits, What to Believe, which I think tries to get the gist of one of the main messages here and fit into the number of characters allotted for a title of a lecture. And it basically has to do with current efforts in human genetics to understand ... |
MIT_HST512_Genomic_Medicine_Spring_2004 | Lecture_6_Information_Science_at_the_Center_of_Genomic_Medicine.txt | ISAAC SAMUEL KOHANE: So let me give you a case history. This is work that I've done with one of my former interns, actually, who's a superb scientist at the Dana-Farber Cancer Institute, David Rowitch, who also is a part time neonatologist. I call this finding the needle in the haystack. And it's about studying the cer... |
MIT_HST512_Genomic_Medicine_Spring_2004 | Lecture_5_Limits_of_Technologies.txt | ZOLTAN SZALLASI: -technologies. And this is kind of the introductory slide. You heard a lot from Zack. I think that was last time, like a week ago or two weeks ago about microarray technology. And I'm sure that he gave an extremely inspirational and enthusiastic talk about the possibilities and scope of this technology... |
MIT_HST512_Genomic_Medicine_Spring_2004 | Lecture_16_Microarray_Disease_Classification_II.txt | PROFESSOR: OK. So last time, we spent the hour and a half talking about classification methods and their use of genomic technologies in direct and close to direct, which we called indirect, applications of clinical medicine. And we talked about three different types of class methods again, class exploration, class pred... |
MIT_HST512_Genomic_Medicine_Spring_2004 | Lecture_19_Modeling_and_Reverse_Engineering.txt | ZOLTAN SZALLASI: --like modeling and what modeling can achieve, or why people are doing it at all, or is it doable at all. Should one be interested in this, or this is completely harebrained idea? Reverse engineering, where you really want to reconstruct how the system looks like-- what you are going to use for modelin... |
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