Towards AI

AUGUST 23, 2024

Approach LLMs as you would any other machine learning development — make the necessary adjustments, and you’re already halfway there. Below, I outline best practices for LLM development, aimed at helping data scientists and machine learning practitioners leverage this powerful technology for their needs.

ML 98

ML ML Hypothesis Testing Machine Learning 98

Towards AI

JUNE 6, 2023

Achieving Peak Performance: Mastering Control and Generalization Source: Image created by Jan Marcel Kezmann Today, we’re going to explore a crucial decision that researchers and practitioners face when training machine and deep learning models: Should we stick to a fixed custom dataset or embrace the power of cross-validation techniques?

Cross Validation 52

Cross Validation Deep Learning Deep Learning ML 52

Dataconomy

AUGUST 15, 2023

The Gaussian process for machine learning can be considered as an intellectual cornerstone, wielding the power to decipher intricate patterns within data and encapsulate the ever-present shroud of uncertainty. At its core, machine learning endeavors to extract knowledge from data to illuminate the path forward.

Machine Learning 91

Machine Learning Machine Learning Cross Validation Data Preparation 91

Towards AI

FEBRUARY 3, 2025

Inspired by Deepseeker: Dynamically Choosing and Combining ML Models for Optimal Performance This member-only story is on us. Photo by Agence Olloweb on Unsplash Machine learning model selection has always been a challenge. Traditionally, we rely on cross-validation to test multiple models XGBoost, LGBM, Random Forest, etc.

Supervised Learning 54

Supervised Learning Cross Validation Data Scientist Machine Learning 54

Dataconomy

AUGUST 22, 2023

Today, as machine learning algorithms continue to shape our world, the integration of Bayesian principles has become a hallmark of advanced predictive modeling. This is where machine learning comes in. What is machine learning? Machine learning algorithms help you find patterns in this data.

Machine Learning 91

Machine Learning Machine Learning Algorithm Deep Learning 91

Mlearning.ai

FEBRUARY 2, 2023

Data scientists use a technique called cross validation to help estimate the performance of a model as well as prevent the model from… Continue reading on MLearning.ai »

Cross Validation 52

Cross Validation Data Scientist ML ML 52

DrivenData Labs

JANUARY 22, 2025

Final Stage Overall Prizes where models were rigorously evaluated with cross-validation and model reports were judged by a panel of experts. The cross-validations for all winners were reproduced by the DrivenData team. Lower is better. Unsurprisingly, the 0.10 quantile was easier to predict than the 0.90

Cross Validation 147

Cross Validation Machine Learning Machine Learning ML 147

Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. billion in 2022 and is expected to grow to USD 505.42

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Flipboard

FEBRUARY 2, 2023

With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. We then explain the details of the ML methodology and model training procedures.

Cross Validation 123

Cross Validation ML ML Machine Learning 123

Mlearning.ai

NOVEMBER 29, 2023

How to Use Machine Learning (ML) for Time Series Forecasting — NIX United The modern market pace calls for a respective competitive edge. Data forecasting has come a long way since formidable data processing-boosting technologies such as machine learning were introduced.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

DagsHub

JUNE 3, 2024

The pedestrian died, and investigators found that there was an issue with the machine learning (ML) model in the car, so it failed to identify the pedestrian beforehand. But First, Do You Really Need to Fix Your ML Model? Read more about benchmarking ML models. How do you choose an appropriate ML model?

Machine Learning 52

Machine Learning Machine Learning Decision Trees ML 52

Mlearning.ai

JULY 6, 2023

AI-generated image ( craiyon ) In machine learning (ML), a hyperparameter is a parameter whose value is given by the user and used to control the learning process. Optuna has many uses, both in machine learning and in deep learning.

Machine Learning 52

Machine Learning Machine Learning Algorithm ML 52

Pickl AI

JANUARY 3, 2024

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. Embrace the benefits of feature engineering to unlock the full potential of your Machine-Learning endeavors and achieve accurate predictions in diverse real-world scenarios.

Machine Learning 52

Machine Learning Machine Learning Exploratory Data Analysis Cross Validation 52

Pickl AI

JULY 26, 2023

The concepts of bias and variance in Machine Learning are two crucial aspects in the realm of statistical modelling and machine learning. Understanding these concepts is paramount for any data scientist, machine learning engineer, or researcher striving to build robust and accurate models.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Flipboard

JANUARY 30, 2025

Furthermore, a tenfold cross-validation process ensures a comprehensive evaluation and the proposed method outperforms different Machine Learning (ML) / Deep Learning (DL) classifiers.

Cross Validation 78

Cross Validation Deep Learning Deep Learning Machine Learning 78

Pickl AI

MAY 17, 2023

Machine learning empowers the machine to perform the task autonomously and evolve based on the available data. However, while working on a Machine Learning algorithm , one may come across the problem of underfitting or overfitting. Both these aspects can impact the performance of the Machine Learning model.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Mlearning.ai

FEBRUARY 15, 2023

Figure 1 Preprocessing Data preprocessing is an essential step in building a Machine Learning model. Deep ensemble learning models utilise the benefits of both deep learning and ensemble learning to produce a model with improved generalisation performance. Ensemble deep learning: A review. link] Ganaie, M.

Cross Validation 52

Cross Validation Decision Trees Algorithm Natural Language Processing 52

DrivenData Labs

MAY 22, 2024

I saw this as an exciting opportunity to test and expand my machine learning skills in a practical, real-world setting. Also, I have 10 years of experience with C++ cross-platform development, especially in the medical imaging domain, and for embedded solutions. quantile forecast. Specialises in the CV and generative AI.

Cross Validation 130

Cross Validation Machine Learning Machine Learning ML 130

Mlearning.ai

MAY 20, 2023

image from lexica.art Machine learning algorithms can be used to capture gender detection from sound by learning patterns and features in the audio data that are indicative of gender differences. Training a Machine Learning Model : The preprocessed features are used to train a machine learning model.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines Cross Validation 52

Pickl AI

MARCH 26, 2024

Summary: The KNN algorithm in machine learning presents advantages, like simplicity and versatility, and challenges, including computational burden and interpretability issues. Unlocking the Power of KNN Algorithm in Machine Learning Machine learning algorithms are significantly impacting diverse fields.

K-nearest Neighbors 52

K-nearest Neighbors Machine Learning Machine Learning Algorithm 52

Towards AI

AUGUST 16, 2023

In fact, AI/ML graduate textbooks do not provide a clear and consistent description of the AI software engineering process. Therefore, I thought it would be helpful to give a complete description of the AI engineering process or AI Process, which is described in most AI/ML textbooks [5][6]. Thus, machine learning is a subfield of AI.

AI 98

AI AI Machine Learning Machine Learning 98

AWS Machine Learning Blog

MAY 31, 2024

Here, we use AWS HealthOmics storage as a convenient and cost-effective omic data store and Amazon Sagemaker as a fully managed machine learning (ML) service to train and deploy the model. SageMaker notably supports popular deep learning frameworks, including PyTorch, which is integral to the solutions provided here.

AWS 109

AWS ML ML Machine Learning 109

Towards AI

AUGUST 23, 2024

Simplifying LLM Development: Treat It Like Regular ML Photo by Daniel K Cheung on Unsplash Large Language Models (LLMs) are the latest buzz, often seen as both exciting and intimidating. Approach LLMs as you would any other machine learning development — make the necessary adjustments, and you’re already halfway there.

ML 52

ML ML Hypothesis Testing Machine Learning 52

Mlearning.ai

FEBRUARY 10, 2023

For example, if you are using regularization such as L2 regularization or dropout with your deep learning model that performs well on your hold-out-cross-validation set, then increasing the model size won’t hurt performance, it will stay the same or improve. Machine learning yearning. References [1].Ng, Ng, Andrew.

Machine Learning 52

Machine Learning Machine Learning Deep Learning Deep Learning 52

AWS Machine Learning Blog

FEBRUARY 25, 2025

For the classfier, we employed a classic ML algorithm, k-NN, using the scikit-learn Python module. To implement the classifier, we employed a classic ML algorithm, SVM, using the scikit-learn Python module. For the classifier, we employ SVM, using the scikit-learn Python module.

Algorithm 74

Algorithm Machine Learning Machine Learning K-nearest Neighbors 74

Mlearning.ai

NOVEMBER 30, 2023

Mastering Tree-Based Models in Machine Learning: A Practical Guide to Decision Trees, Random Forests, and GBMs Image created by the author on Canva Ever wondered how machines make complex decisions? Just like a tree branches out, tree-based models in machine learning do something similar. Let’s get started!

Machine Learning 52

Machine Learning Machine Learning Decision Trees Data Science 52

AWS Machine Learning Blog

OCTOBER 3, 2024

Steamlining model management and deployment with SageMaker Amazon SageMaker is a managed machine learning platform that provides data scientists and data engineers familiar concepts and tools to build, train, deploy, govern , and manage the infrastructure needed to have highly available and scalable model inference endpoints.

Data Science 92

Data Science AWS Machine Learning Machine Learning 92

Iguazio

JUNE 25, 2023

Evaluating ML model performance is essential for ensuring the reliability, quality, accuracy and effectiveness of your ML models. In this blog post, we dive into all aspects of ML model performance: which metrics to use to measure performance, best practices that can help and where MLOps fits in. Why Evaluate Model Performance?

ML 52

ML ML Clustering Cross Validation 52

Heartbeat

FEBRUARY 20, 2023

First-time project and model registration Photo by Isaac Smith on Unsplash The world of machine learning and data science is awash with technicalities. Machine learning problems could grow to such an extent that you constantly lose track of what you are doing. One problem that is particularly prevalent is model tracking.

Cross Validation 87

Cross Validation ML ML Machine Learning 87

Mlearning.ai

JUNE 28, 2023

{This article was written without the assistance or use of AI tools, providing an authentic and insightful exploration of PyCaret} Image by Author ‍In the rapidly evolving realm of data science, the imperative to automate machine learning workflows has become an indispensable requisite for enterprises aiming to outpace their competitors.

Machine Learning 52

Machine Learning Machine Learning Data Preparation Data Science 52

Mlearning.ai

JUNE 7, 2023

Machine learning is a rapidly evolving field that provides powerful tools for data analysis and prediction. Continue reading on MLearning.ai »

Cross Validation 40

Cross Validation Machine Learning Machine Learning Data Analysis 40

Mlearning.ai

APRIL 13, 2023

Photo by Robo Wunderkind on Unsplash In general , a data scientist should have a basic understanding of the following concepts related to kernels in machine learning: 1. Machine learning algorithms rely on mathematical functions called “kernels” to make predictions based on input data. What are kernels? Linear Kernels 2.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Support Vector Machines 52

Ocean Protocol

FEBRUARY 2, 2023

Indeed, the most robust predictive trading algorithms use machine learning (ML) techniques. On the optimistic side, algorithmically trading assets with predictive ML models can yield enormous gains à la Renaissance Technologies… Yet algorithmic trading gone awry can yield enormous losses as in the latest FTX scandal.

Cross Validation 52

Cross Validation Algorithm ML ML 52

AWS Machine Learning Blog

DECEMBER 13, 2024

Amazon SageMaker Pipelines includes features that allow you to streamline and automate machine learning (ML) workflows. Ensemble models are becoming popular within the ML communities. Pipelines can quickly be used to create and end-to-end ML pipeline for ensemble models.

ML 86

ML ML Clustering AWS 86

DrivenData Labs

APRIL 10, 2024

Michal Wierzbinski ¶ Place: 2nd Place Prize: $3,000 Hometown: Rabka-Zdroj (near the city of Cracow), Poland Username: xultaeculcis Social Media: GitHub , LinkedIn Background: ML Engineer specializing in building Deep Learning solutions for Geospatial industry in a cloud native fashion.

Deep Learning 130

Deep Learning Deep Learning Machine Learning Machine Learning 130

DagsHub

APRIL 21, 2024

Source: [link] Similarly, while building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. You need to make that model available to the end users, monitor it, and retrain it for better performance if needed.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. Journal of machine learning research 9, no.

ML 84

ML ML Machine Learning Machine Learning 84