Mathematical optimization | Model selection
In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. The same kind of machine learning model can require different constraints, weights or learning rates to generalize different data patterns. These measures are called hyperparameters, and have to be tuned so that the model can optimally solve the machine learning problem. Hyperparameter optimization finds a tuple of hyperparameters that yields an optimal model which minimizes a predefined loss function on given independent data. The objective function takes a tuple of hyperparameters and returns the associated loss. Cross-validation is often used to estimate this generalization performance. (Wikipedia).
Hyperparameter Optimization | Applied Machine Learning, Part 3
Machine learning is all about fitting models to data. This process typically involves using an iterative algorithm that minimizes the model error. The parameters that control a machine learning algorithm’s behavior are called hyperparameters. Depending on the values you select for your h
From playlist Applied Machine Learning
Hyperbola 3D Animation | Objective conic hyperbola | Digital Learning
Hyperbola 3D Animation In mathematics, a hyperbola is a type of smooth curve lying in a plane, defined by its geometric properties or by equations for which it is the solution set. A hyperbola has two pieces, called connected components or branches, that are mirror images of each other an
From playlist Maths Topics
11_3_8 Example problem calculating a tangent hyperplane
Let's look at an example where we calculate the function of a tangent hyperplane to a point on a higher dimensional curve.
From playlist Advanced Calculus / Multivariable Calculus
This video is #7 in the Adaptive Experimentation series presented at the 18th IEEE Conference on eScience in Salt Lake City, UT (October 10-14, 2022). In this video, Sterling Baird @sterling-baird presents on multiobjective optimization where a pareto front of non-dominated solutions can
From playlist Optimization tutorial
Introduction to Hyperbolic Functions
This video provides a basic overview of hyperbolic function. The lesson defines the hyperbolic functions, shows the graphs of the hyperbolic functions, and gives the properties of hyperbolic functions. Site: http://mathispower4u.com Blog: http://mathispower4u.wordpress.com
From playlist Differentiation of Hyperbolic Functions
Optimizing Hyperparameters | Predictive Modeling and Machine Learning, Part 5
Manually adjusting parameter values is an impractical approach to finding the best set of hyperparameters. This video explains how to automate the optimization process. First, learn how to optimize a machine learning decision tree, then optimize an ensemble of decision trees, and lastly ap
From playlist Predictive Modeling and Machine Learning
Hypercube Edges - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
13_2 Optimization with Constraints
Here we use optimization with constraints put on a function whose minima or maxima we are seeking. This has practical value as can be seen by the examples used.
From playlist Advanced Calculus / Multivariable Calculus
Introduction to Hyperparameters | Predictive Modeling and Machine Learning, Part 4
You may see terms like parameters and hyperparameters to describe characteristics of your machine learning models but not know the difference between them. In this video, learn what hyperparameters are, why they are important, and various approaches to optimize them. - Learn more: https:
From playlist Predictive Modeling and Machine Learning
Training learned optimizers: VeLO paper EXPLAINED
Why tune optimizers hyperparameters (Adam) by hand, when one can train a neural network to behave like an optimizer and dynamically find the best update for your neural network’s weights? In this video, we explain the work on VeLO to train an optimizer from data from previous training runs
From playlist Explained AI/ML in your Coffee Break
PB2 - Population-Based Bandit Optimization
Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-15th-2021-a68f599395e3428c878dc74c5f0e1124 Chapters 0:00 Introduction 2:41 Hyperparameter Optimization 3:44 Population-Based Training 6:12 Evolution + Bayesian Optimization 8:54 ASHA 10:48 Results Thanks
From playlist AI Weekly Update - July 15th, 2021!
Challenges of Advanced AutoML - Determined AI
This video explains the key challenges of using the latest AutoML algorithms and why most researchers just don't bother with it. Determined AI has implemented many features that make using AutoML much easier saving you a massive amount of Time and Money!! Please leave any questions you hav
From playlist Determined AI
What are Hyperbolas? | Ch 1, Hyperbolic Trigonometry
This is the first chapter in a series about hyperbolas from first principles, reimagining trigonometry using hyperbolas instead of circles. This first chapter defines hyperbolas and hyperbolic relationships and sets some foreshadowings for later chapters This is my completed submission t
From playlist Summer of Math Exposition 2 videos
Keras Tuner with Google Cloud Compute - Keras Examples
This video walkthroughs a series of new tutorials on integrating Google Cloud runtimes with the Keras Tuner library. I hope from this tutorial you are able to get a sense of how to setup hyperparameters, interface them in a model builder function, and connect your experiments to Google Clo
From playlist Keras Code Examples
I'm really excited to present this video on Determined AI! Determined has been teaching me how to use their platform and showing me what they are building. I am so excited about this with advancing my Deep Learning experimentation skills and I hope you all find value out of this as well.
From playlist Determined AI
Machine Learning 3 - Generalization, K-means | Stanford CS221: AI (Autumn 2019)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/30Z6b0p Topics: Generalization, Unsupervised learning, K-means Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onl
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019
From playlist COMP0168 (2020/21)
Introduction to Hyperbolic Functions
This video provides a basic overview of hyperbolic function. The lesson defines the hyperbolic functions, shows the graphs of the hyperbolic functions, and gives the properties of hyperbolic functions.
From playlist Using the Properties of Hyperbolic Functions
Lecture 13: Convolutional Neural Networks
Lecture 13 provides a mini tutorial on Azure and GPUs followed by research highlight "Character-Aware Neural Language Models." Also covered are CNN Variant 1 and 2 as well as comparison between sentence models: BoV, RNNs, CNNs. -------------------------------------------------------------
From playlist Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017)