- Stanford CS224N -  NLP w/ DL | Winter 2021 | Lecture 5 - Recurrent Neural networks (RNNs)

Stanford CS224N - NLP w/ DL | Winter 2021 | Lecture 5 - Recurrent Neural networks (RNNs)

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/3bqFlQV

This lecture will cover:
1. Neural dependency parsing (20 mins)
2. A bit more about neural networks (15 mins)
3. Language modeling + RNNs (45 mins)
A new NLP task: ...
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/3bqFlQV

This lecture will cover:
1. Neural dependency parsing (20 mins)
2. A bit more about neural networks (15 mins)
3. Language modeling + RNNs (45 mins)
A new NLP task: Language Modeling
A new family of neural networks: Recurrent Neural networks (RNNs)

To learn more about this course visit: https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning
To follow along with the course schedule and syllabus visit: http://web.stanford.edu/class/cs224n/

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

#Stanford #Stanford Online #AI #NLP #Deep Learning
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Stanford Online is Stanford’s online learning provider, offering learners access to Stanford’s extended education and lifelong learning opportunities. Our robust catalog of credit-bearing, professional, and free and open content provides a variety of ways to expand your learning, advance your career, and enhance your life. Stanford Online is operated and managed by the Stanford Center for Professional Development, a leader in online and extended education.

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Timetable

動画タイムテーブル

動画数:131件

plasticity - CS25 I Stanford Seminar - Mixture of Experts (MoE) paradigm and the Switch Transformer

plasticity

CS25 I Stanford Seminar - Mixture of Experts (MoE) paradigm and the Switch Transformer
2022年07月15日
00:20:10 - 01:05:44
Introduction - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Introduction

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:00:00 - 00:00:08
3-Gram Model (Shannon 1951) - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

3-Gram Model (Shannon 1951)

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:00:08 - 00:00:27
Recurrent Neural Nets (Sutskever et al 2011) - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Recurrent Neural Nets (Sutskever et al 2011)

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:00:27 - 00:01:12
Big LSTM (Jozefowicz et al 2016) - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Big LSTM (Jozefowicz et al 2016)

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:01:12 - 00:01:52
Transformer (Llu and Saleh et al 2018) - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Transformer (Llu and Saleh et al 2018)

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:01:52 - 00:02:33
GPT-2: Big Transformer (Radford et al 2019) - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

GPT-2: Big Transformer (Radford et al 2019)

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:02:33 - 00:03:38
GPT-3: Very Big Transformer (Brown et al 2019) - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

GPT-3: Very Big Transformer (Brown et al 2019)

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:03:38 - 00:05:12
GPT-3: Can Humans Detect Generated News Articles? - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

GPT-3: Can Humans Detect Generated News Articles?

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:05:12 - 00:09:09
Why Unsupervised Learning? - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Why Unsupervised Learning?

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:09:09 - 00:10:38
Is there a Big Trove of Unlabeled Data? - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Is there a Big Trove of Unlabeled Data?

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:10:38 - 00:11:11
Why Use Autoregressive Generative Models for Unsupervised Learnin - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Why Use Autoregressive Generative Models for Unsupervised Learnin

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:11:11 - 00:13:00
Unsupervised Sentiment Neuron (Radford et al 2017) - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Unsupervised Sentiment Neuron (Radford et al 2017)

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:13:00 - 00:14:11
Radford et al 2018) - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Radford et al 2018)

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:14:11 - 00:15:21
Zero-Shot Reading Comprehension - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Zero-Shot Reading Comprehension

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:15:21 - 00:16:44
GPT-2: Zero-Shot Translation - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

GPT-2: Zero-Shot Translation

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:16:44 - 00:18:15
Language Model Metalearning - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Language Model Metalearning

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:18:15 - 00:19:23
GPT-3: Few Shot Arithmetic - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

GPT-3: Few Shot Arithmetic

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:19:23 - 00:20:14
GPT-3: Few Shot Word Unscrambling - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

GPT-3: Few Shot Word Unscrambling

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:20:14 - 00:20:36
GPT-3: General Few Shot Learning - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

GPT-3: General Few Shot Learning

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:20:36 - 00:23:42
IGPT (Chen et al 2020): Can we apply GPT to images? - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

IGPT (Chen et al 2020): Can we apply GPT to images?

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:23:42 - 00:25:31
IGPT: Completions - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

IGPT: Completions

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:25:31 - 00:26:24
IGPT: Feature Learning - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

IGPT: Feature Learning

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:26:24 - 00:32:20
Isn't Code Just Another Modality? - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Isn't Code Just Another Modality?

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:32:20 - 00:33:33
The HumanEval Dataset - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

The HumanEval Dataset

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:33:33 - 00:36:00
The Pass @ K Metric - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

The Pass @ K Metric

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:36:00 - 00:36:59
Codex: Training Details - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Codex: Training Details

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:36:59 - 00:38:03
An Easy Human Eval Problem (pass@1 -0.9) - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

An Easy Human Eval Problem ([email protected] -0.9)

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:38:03 - 00:38:36
A Medium HumanEval Problem (pass@1 -0.17) - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

A Medium HumanEval Problem ([email protected] -0.17)

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:38:36 - 00:39:00
A Hard HumanEval Problem (pass@1 -0.005) - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

A Hard HumanEval Problem ([email protected] -0.005)

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:39:00 - 00:41:26
Calibrating Sampling Temperature for Pass@k - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Calibrating Sampling Temperature for [email protected]

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:41:26 - 00:42:19
The Unreasonable Effectiveness of Sampling - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

The Unreasonable Effectiveness of Sampling

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:42:19 - 00:43:17
Can We Approximate Sampling Against an Oracle? - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Can We Approximate Sampling Against an Oracle?

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:43:17 - 00:45:52
Main Figure - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Main Figure

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:45:52 - 00:46:53
Limitations - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Limitations

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:46:53 - 00:47:38
Conclusion - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Conclusion

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:47:38 - 00:48:19
Acknowledgements - CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3

Acknowledgements

CS25 I Stanford Seminar - Transformers in Language: The development of GPT Models including GPT3
2022年07月12日
00:48:19 - 00:48:39
Introduction - Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)

Introduction

Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:00 - 00:00:06
Logic: first-order logic - Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)

Logic: first-order logic

Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:06 - 00:00:36
Limitations of propositional logic - Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)

Limitations of propositional logic

Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:36 - 00:05:08
First-order logic: examples - Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)

First-order logic: examples

Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:05:08 - 00:06:19
Syntax of first-order logic - Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)

Syntax of first-order logic

Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:06:19 - 00:12:55
Natural language quantifiers - Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)

Natural language quantifiers

Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:12:55 - 00:15:47
Some examples of first-order logic - Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)

Some examples of first-order logic

Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:15:47 - 00:20:01
Graph representation of a model If only have unary and binary predicates, a model w can be represented as a directed graph - Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)

Graph representation of a model If only have unary and binary predicates, a model w can be represented as a directed graph

Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:20:01 - 00:22:09
A restriction on models - Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)

A restriction on models

Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:22:09 - 00:24:16
Propositionalization If one-to-one mapping between constant symbols and objects (unique names and domain closure) - Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)

Propositionalization If one-to-one mapping between constant symbols and objects (unique names and domain closure)

Logic 7 - First Order Logic | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:24:16 - 00:26:10
Introduction - Logic 5 - Propositional Modus Ponens | Stanford CS221: AI (Autumn 2021)

Introduction

Logic 5 - Propositional Modus Ponens | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:00 - 00:00:06
Logic: modus ponens with Horn clauses - Logic 5 - Propositional Modus Ponens | Stanford CS221: AI (Autumn 2021)

Logic: modus ponens with Horn clauses

Logic 5 - Propositional Modus Ponens | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:06 - 00:01:13
Definite clauses - Logic 5 - Propositional Modus Ponens | Stanford CS221: AI (Autumn 2021)

Definite clauses

Logic 5 - Propositional Modus Ponens | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:01:13 - 00:04:07
Completeness of modus ponens - Logic 5 - Propositional Modus Ponens | Stanford CS221: AI (Autumn 2021)

Completeness of modus ponens

Logic 5 - Propositional Modus Ponens | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:04:07 - 00:06:06
Example: Modus ponens - Logic 5 - Propositional Modus Ponens | Stanford CS221: AI (Autumn 2021)

Example: Modus ponens

Logic 5 - Propositional Modus Ponens | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:06:06 - 00:07:06
Summary - Logic 5 - Propositional Modus Ponens | Stanford CS221: AI (Autumn 2021)

Summary

Logic 5 - Propositional Modus Ponens | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:07:06 - 00:08:07
Introduction - Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)

Introduction

Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:00 - 00:00:06
Logic: inference rules - Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)

Logic: inference rules

Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:06 - 00:05:51
Inference framework - Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)

Inference framework

Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:05:51 - 00:11:05
Inference example - Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)

Inference example

Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:11:05 - 00:12:45
Desiderata for inference rules - Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)

Desiderata for inference rules

Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:12:45 - 00:16:11
Soundness and completeness The truth, the whole truth, and nothing but the truth - Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)

Soundness and completeness The truth, the whole truth, and nothing but the truth

Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:16:11 - 00:17:58
Soundness: example - Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)

Soundness: example

Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:17:58 - 00:23:13
Fixing completeness - Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)

Fixing completeness

Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:23:13 - 00:24:20
Introduction - Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)

Introduction

Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:00 - 00:00:06
Logic: propositional logic semantics - Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)

Logic: propositional logic semantics

Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:06 - 00:05:19
Interpretation function: definition - Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)

Interpretation function: definition

Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:05:19 - 00:07:36
Interpretation function: example Example: Interpretation function - Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)

Interpretation function: example Example: Interpretation function

Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:07:36 - 00:11:13
Models: example - Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)

Models: example

Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:11:13 - 00:17:21
Adding to the knowledge base - Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)

Adding to the knowledge base

Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:17:21 - 00:23:17
Contradiction and entailment - Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)

Contradiction and entailment

Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:23:17 - 00:23:30
Contingency - Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)

Contingency

Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:23:30 - 00:25:40
Tell operation - Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)

Tell operation

Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:25:40 - 00:27:23
Ask operation - Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)

Ask operation

Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:27:23 - 00:28:19
Digression: probabilistic generalization - Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)

Digression: probabilistic generalization

Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:28:19 - 00:31:45
Satisfiability - Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)

Satisfiability

Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:31:45 - 00:37:02
Model checking - Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)

Model checking

Logic 3 - Propositional Logic Semantics | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:37:02 - 00:38:34
Introduction - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Introduction

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:00 - 00:00:06
Logic: overview - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Logic: overview

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:06 - 00:00:21
Question - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Question

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:21 - 00:01:41
Course plan - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Course plan

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:01:41 - 00:02:07
Taking a step back - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Taking a step back

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:02:07 - 00:03:16
Modeling paradigms State-based models: search problems, MDPs, games Applications: route finding, game playing, etc. Think in terms of states, actions, and costs - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Modeling paradigms State-based models: search problems, MDPs, games Applications: route finding, game playing, etc. Think in terms of states, actions, and costs

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:03:16 - 00:09:34
Motivation: smart personal assistant - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Motivation: smart personal assistant

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:09:34 - 00:10:06
Natural language - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Natural language

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:10:06 - 00:11:43
Language Language is a mechanism for expression - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Language Language is a mechanism for expression

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:11:43 - 00:12:48
Two goals of a logic language - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Two goals of a logic language

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:12:48 - 00:13:31
Ingredients of a logic Syntax: defines a set of valid formulas (Formulas) Example: Rain A Wet - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Ingredients of a logic Syntax: defines a set of valid formulas (Formulas) Example: Rain A Wet

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:13:31 - 00:16:10
Syntax versus semantics - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Syntax versus semantics

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:16:10 - 00:17:55
Propositional logic Semantics - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Propositional logic Semantics

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:17:55 - 00:20:34
Roadmap - Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)

Roadmap

Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:20:34 - 00:22:14
Introduction - Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)

Introduction

Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:00 - 00:00:06
Bayesian networks: smoothing - Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)

Bayesian networks: smoothing

Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:06 - 00:00:11
Review: maximum likelihood - Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)

Review: maximum likelihood

Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:11 - 00:01:49
Laplace smoothing example - Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)

Laplace smoothing example

Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:01:49 - 00:03:45
Laplace smoothing Key idea: maximum likelihood with Laplace smoothing - Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)

Laplace smoothing Key idea: maximum likelihood with Laplace smoothing

Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:03:45 - 00:04:47
Interplay between smoothing and data - Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)

Interplay between smoothing and data

Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:04:47 - 00:06:16
Summary - Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)

Summary

Bayesian Networks 8 - Smoothing | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:06:16 - 00:07:02
Introduction - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Introduction

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:00 - 00:00:06
Bayesian networks: supervised learning - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Bayesian networks: supervised learning

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:06 - 00:00:15
Review: Bayesian network - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Review: Bayesian network

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:00:15 - 00:01:22
Review: probabilistic inference - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Review: probabilistic inference

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:01:22 - 00:02:15
Where do parameters come from? - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Where do parameters come from?

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:02:15 - 00:02:37
Learning task - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Learning task

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:02:37 - 00:03:42
Example: one variable - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Example: one variable

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:03:42 - 00:05:41
Example: two variables - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Example: two variables

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:05:41 - 00:08:13
Example: v-structure - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Example: v-structure

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:08:13 - 00:11:33
Example: inverted-v structure - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Example: inverted-v structure

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:11:33 - 00:15:17
Parameter sharing - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Parameter sharing

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:15:17 - 00:18:10
Example: Naive Bayes - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Example: Naive Bayes

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:18:10 - 00:19:51
Example: HMMS - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Example: HMMS

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:19:51 - 00:22:57
General case: learning algorithm - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

General case: learning algorithm

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:22:57 - 00:24:15
Maximum likelihood - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Maximum likelihood

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:24:15 - 00:30:15
Summary - Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)

Summary

Bayesian Networks 7 - Supervised Learning | Stanford CS221: AI (Autumn 2021)
2022年06月01日
00:30:15 - 00:31:44