HyperParameter

How you can improve the “learning” and “training” of neural networks through tuning hyperparametersContinue reading on Towards Data Science » How you can improve the “learning” and “training” of neural networks through tuning hyperparametersContinue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the…

Use wrapper functions to avoid OOM-exceptionsIt’s the year 2023. Machine learning is no longer hype but at the core of everyday products. Ever faster hardware makes it possible to train ever larger machine learning models — in shorter times, too. With around 100 papers submitted per day on machine learning or related domains to arXiv, chances are high that at least one-third of them have leveraged the hardware’s capabilities to do hyperparameter searches to optimize their used model. And that’s straightforward, is it not?…

A path to enable the distributed hyperparameter tuning in GCP Vertex AI pipelinePhoto by Marsha Reid on UnsplashVertex AI pipelines offer a handy way to implement end-to-end ML workflows from data collection to endpoint monitoring with extremely low effort. For new users, the easiness of development and deployment is largely thanks to the Vertex AI pipeline example offered by GCP.Despite the comprehensive demonstration of the essential components, the official example also exposes the feasibility for users to customize…

A step-by-step tutorial to build Bayesian optimization from the grounds upPhoto by Brett Jordan on UnsplashHyperparameter optimization has become a necessary step in most machine learning pipelines and probably the most well-known “learning” approach towards hyperparameter optimization is Bayesian Optimization. The task intended to help choose a set of optimal parameters for the cost (or objective) function in a learning algorithm is called hyperparameter optimization. These parameters can be data-driven (e.g. various…

Most common hyperparameter optimization methodologies to boost machine learning outcomesPhoto by Jonas Jaeken on UnsplashUsually the first solution that comes to mind when trying to improve a machine learning model is to just add more data to it. Additional data usually helps (barring certain situations) but generating high-quality data can be quite expensive. Hyperparameter optimization can save us time and resources by getting the best model performance using the existing data.Hyperparameter optimization, as the name…

Using SageMaker Managed Warm PoolsPhoto by SpaceX on Unsplash.This article shares a recipe to speeding up to 60% your hyperparameter tuning with cross-validation in SageMaker Pipelines leveraging SageMaker Managed Warm Pools. By using Warm Pools, the runtime of a Tuning step with 120 sequential jobs is reduced from 10h to 4h.Improving and evaluating the performance of a machine learning model often requires a variety of ingredients. Hyperparameter tuning and cross-validation are 2 such ingredients. The first finds the…

A complete guide with examples in PythonPhoto by Joanne Francis on UnsplashIn a few months, I will have been working as a Data Scientist for 3 years. I know it is not a long career yet, but together with my academic experience, I have been able to work on several machine learning projects for different sectors (energy, customer experience…). All of them were fed by tabular data, which means structured data (organised in rows and columns). In contrast, there are projects fed by unstructured data such as images or text…

Two Methods for Tuning on a Dedicated Ray ClusterPhoto by Davide Cantelli on UnsplashThis is the second part of a three-part post on the topic of hyperparameter tuning (HPT) machine learning models in the cloud. In part 1 we set the stage by introducing the problem and defining a toy model that we will use in our tuning demonstrations. In this part we will review two options for cloud based optimization, both of which involve parallel experimentation on a dedicated tuning cluster.The first option we consider for…

Cloud Tuning by Parallelizing Managed Training JobsPhoto by Kenny Eliason on UnsplashThis the final part of a three-part post on the topic of hyperparameter tuning (HPT) machine learning models in the cloud. In the first part we set the stage by introducing the problem and defining a toy model and a training function for our tuning demonstrations. In the second part we reviewed two options for cloud based optimization, both of which involved parallel experimentation on a dedicated tuning cluster. In this part we will…

Finding the best sampling strategy using pipelines and hyperparameter tuningOne of the go-to steps in handling imbalanced machine learning problems is to resample the data. We can either undersample the majority class and/or oversample the minority class. However, there is a question that needs to be addressed: to what number should we reduce the majority class, and/or increase the minority class? An easy but time-consuming method is to alter the resampling values of the majority and minority classes one by one to find…