(ML 12.3) Model complexity parameters
Some general guidelines on the distinction between model complexity parameters and model-fitting parameters.
From playlist Machine Learning
Model Theory - part 01 - The Setup in Classical Set Valued Model Theory
Here we give the basic setup for Model Theory. I learned this from a talk Tom Scanlon gave in 2010 at CUNY.
From playlist Model Theory
(ML 13.3) Directed graphical models - formalism (part 1)
Definition of a directed graphical model, or more precisely, what it means for a distribution to respect a directed acyclic graph.
From playlist Machine Learning
What Is Modelica? Modelica is a modeling language that makes it easy to set up mathematical models of dynamic systems. Wolfram MathCore, creator of System Modeler, is a founding member of the Modelica Association, which is a collaboration providing a tool-independent open industry standar
From playlist Why Use Modelica and FMI?
(ML 13.4) Directed graphical models - formalism (part 2)
Definition of a directed graphical model, or more precisely, what it means for a distribution to respect a directed acyclic graph.
From playlist Machine Learning
Towards a Model-Based Approach | Systems Engineering, Part 2
See all the videos in this playlist: https://www.youtube.com/playlist?list=PLn8PRpmsu08owzDpgnQr7vo2O-FUQm_fL The role of systems engineering is to help find and maintain a balance between the stakeholder needs, the management needs, and the engineering needs of a project. So we can thin
From playlist Systems Engineering
Managing Requirements Based Testing Process with Testing Dashboard
For rigorous processes such as ISO 26262, it is challenging to manage the many activities and artifacts such as requirements, design, test cases, and test results and determine when testing is complete. The Model Testing Dashboard collects metric data from the model design and testing arti
From playlist Tips and Tricks from MATLAB and Simulink Developers
(ML 13.6) Graphical model for Bayesian linear regression
As an example, we write down the graphical model for Bayesian linear regression. We introduce the "plate notation", and the convention of shading random variables which are being conditioned on.
From playlist Machine Learning
(ML 13.1) Directed graphical models - introductory examples (part 1)
Introduction to (directed) graphical models. Simple examples to motivate the concept.
From playlist Machine Learning
Cassandra Data Modeling | Introduction to Cassandra Data Model | Apache Cassandra Training | Edureka
***** Apache Cassandra Certification Training : https://www.edureka.co/cassandra ***** In this Edureka Video, you will learn about Cassandra Data Model and similarities between RDBMS and Cassandra Data Model. You will also understand the key Database Elements of Cassandra (Keyspace, Cluste
From playlist Cassandra Tutorial Videos
Jason Ernst: "Deciphering the Non-coding Human Genome"
Computational Genomics Summer Institute 2016 "Deciphering the Non-coding Human Genome" Jason Ernst, UCLA Institute for Pure and Applied Mathematics, UCLA July 20, 2016 For more information: http://computationalgenomics.bioinformatics.ucla.edu/
From playlist Computational Genomics Summer Institute 2016
Lecture 07: Planning and Learning with Tabular Methods
Seventh lecture video on the course "Reinforcement Learning" at Paderborn University during the summer term 2020. Source files are available here: https://github.com/upb-lea/reinforcement_learning_course_materials
From playlist Reinforcement Learning Course: Lectures (Summer 2020)
DATASET to fine-tune SBERT (w/ CROSS-ENCODER) for a better Domain Performance 2022 (SBERT 32)
Use a training dataset to fine-tune your SBERT model. Python code on how to train famous SNLI dataset for a CROSS-ENCODER /Sentence Transformer model. Example on COLAB with PyTorch. #datascience #datastructure #dataset #datasets #pytorch #deeplearning #colab #sbert #semantic #se
From playlist Training DATASET for fine-tuning Sentence Transformers SBERT Python
Is it Enough to Simply Apply Language Model for Optimal Text Classification?
Presented by: Meysam Ghaffari - Senior Data Scientist (NLP and Deep Learning) at Memorial Sloan Kettering Cancer Center Using language models to solve the NLP tasks is getting more popular each day. It has been proven that language models can give us state of the art results in most of th
From playlist Healthcare NLP Summit 2022
Privacy-Preserving Analytics on the Edge
A Google TechTalk, 2020/7/30, presented by Hamed Haddadi, Imperial College London ABSTRACT: We are increasingly surrounded by applications, connected devices, services, and smart environments which require fine-grained access to various personal data. The inherent complexities of our per
From playlist 2020 Google Workshop on Federated Learning and Analytics
Sarah Yip: "Connectome-based modeling of real world clinical outcomes in addictions"
Computational Psychiatry 2020 "Connectome-based modeling of real world clinical outcomes in addictions" Sarah Yip - Yale University Abstract: Recent advances in predictive modeling and machine learning methods enable data-driven prediction of complex behaviors. By focusing on individual
From playlist Computational Psychiatry 2020
Language Assessment 1 - Developing a Speaking Assessment
In this video, I briefly discuss how a Speaking Assessment can be developed and administered. The video was originally was created for my MA students at the National Institute of Education of Singapore, but I think there are some useful tips and guidelines which is applicable to other asse
From playlist Language Assessment & Technology
Learn SBERT Sentence Transformers: TSDAE, SimCSE and CT #sbert #deeplearning (SBERT 15)
Real time code for SBERT Sentence Embedding in a vector space with SBERT Transformer models, Bi-encoder Transformer models! Learn SBERT Sentence Embedding: TSDAE, SimCSE and CT. With NEW pre-trained models best suited for your application. A) Add "SUPERVISED training data" to your Sente
From playlist SBERT: Python Code Sentence Transformers: a Bi-Encoder /Transformer model #sbert
Python Tutorial to Fine-tune SBERT BI-Encoder w/ my Domain-specific Training Dataset (SBERT Ep 39)
SOTA Word embedding and Sentence embedding in NLP: Fine-tune SBERT models with my own TRAINING DATASET for a specific DOMAIN (eg Science) with 100000 sentence-pairs with labeled data (entailment, neutral, contradiction). Fine-tune my SBERT BI-ENCODER model with an improved LOSS Function o
From playlist SBERT: Python Code Sentence Transformers: a Bi-Encoder /Transformer model #sbert
Some Benefits of Model-Based Systems Engineering | Systems Engineering, Part 5
See all the videos in this playlist: https://www.youtube.com/playlist?list=PLn8PRpmsu08owzDpgnQr7vo2O-FUQm_fL Learn how model-based systems engineering (MBSE) can help you cut through the chaos of early systems development and get you from definition to execution more seamlessly. You’ll h
From playlist Systems Engineering