Dataconomy

JUNE 29, 2023

Some machine learning packages focus specifically on deep learning, which is a subset of machine learning that deals with neural networks and complex, hierarchical representations of data. Let’s explore some of the best Python machine learning packages and understand their features and applications.

Machine Learning 113

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ML @ CMU

NOVEMBER 7, 2024

For the Risk Modeling component, we designed a novel interpretable deep learning tabular model extending TabNet. To validate the proposed system, we simulate different scenarios in which the RELand system could be deployed in mine clearance operations using real data from Colombia. Validation results in Colombia.

Clustering 152

Clustering Cross Validation Machine Learning Machine Learning 152

DataRobot

DECEMBER 20, 2021

Image recognition is one of the most relevant areas of machine learning. Deep learning makes the process efficient. However, not everyone has deep learning skills or budget resources to spend on GPUs before demonstrating any value to the business. With frameworks like Tensorflow , Keras , Pytorch, etc.,

Clustering 97

Clustering Deep Learning Deep Learning Exploratory Data Analysis 97

DrivenData Labs

APRIL 10, 2024

Model architectures : All four winners created ensembles of deep learning models and relied on some combination of UNet, ConvNext, and SWIN architectures. In the modeling phase, XGBoost predictions serve as features for subsequent deep learning models. Test-time augmentations were used with mixed results.

Deep Learning 130

Deep Learning Deep Learning Machine Learning Machine Learning 130

DrivenData Labs

MARCH 28, 2023

I am involved in an educational program where I teach machine and deep learning courses. Machine learning is my passion and I often take part in competitions. Training data was splited into 5 folds for cross validation. We implement machine learning and deep learning methods in our research projects.

Machine Learning 130

Machine Learning Machine Learning Cross Validation Deep Learning 130

Heartbeat

FEBRUARY 20, 2023

Additionally, I will use StratifiedKFold cross-validation to perform multiple train-test splits. Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

Cross Validation 87

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Smart Data Collective

JULY 1, 2023

The resulting structured data is then used to train a machine learning algorithm. There are a lot of image annotation techniques that can make the process more efficient with deep learning. Cross-validation Divide the dataset into smaller batches for large projects and have different annotators work on each batch independently.

Cross Validation 66

Cross Validation AI AI Machine Learning 66

Data Science Dojo

JULY 15, 2024

Technical Approaches: Several techniques can be used to assess row importance, each with its own advantages and limitations: Leave-One-Out (LOO) Cross-Validation: This method retrains the model leaving out each data point one at a time and observes the change in model performance (e.g., accuracy).

Machine Learning 307

Machine Learning Machine Learning Algorithm Clustering 307

Towards AI

JULY 19, 2023

Please refer to Part 1– to understand what is Sales Prediction/Forecasting, the Basic concepts of Time series modeling, and EDA I’m working on Part 3 where I will be implementing Deep Learning and Part 4 where I will be implementing a supervised ML model.

Cross Validation 98

Cross Validation Clustering EDA Data Preparation 98

DrivenData Labs

MAY 24, 2023

Several additional approaches were attempted but deprioritized or entirely eliminated from the final workflow due to lack of positive impact on the validation MAE. Her primary interests lie in theoretical machine learning. She currently does research involving interpretability methods for biological deep learning models.

Machine Learning 225

Machine Learning Machine Learning Data Science Decision Trees 225

Towards AI

FEBRUARY 7, 2025

Researchers have explored a variety of approaches over the years from classical statistical methods to deep learning architectures to tackle these challenges. With sequential dependencies, seasonal effects, and non stationary behavior, these datasets demand a modeling approach that truly understands time.

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Pickl AI

JULY 26, 2023

To mitigate variance in machine learning, techniques like regularization, cross-validation, early stopping, and using more diverse and balanced datasets can be employed. Cross-Validation Cross-validation is a widely-used technique to assess a model’s performance and find the optimal balance between bias and variance.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 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. The only drawback of using a bigger model is computational cost.

Machine Learning 52

Machine Learning Machine Learning Deep Learning Deep Learning 52

Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. TensorFlow and Keras: TensorFlow is an open-source platform for machine learning.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

PyImageSearch

JANUARY 23, 2023

In this tutorial, you will learn the magic behind the critically acclaimed algorithm: XGBoost. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Here you’ll learn how to successfully and confidently apply computer vision to your work, research, and projects.

Deep Learning 83

Deep Learning Deep Learning Algorithm Decision Trees 83

Dataconomy

AUGUST 22, 2023

And lastly, integrating Bayesian techniques with deep learning, which has gained tremendous popularity, presents additional challenges. Combining the flexibility of deep learning architectures with Bayesian updating can be intricate and require specialized knowledge.

Machine Learning 91

Machine Learning Machine Learning Algorithm Deep Learning 91

Pickl AI

NOVEMBER 28, 2024

Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible. Neural Networks These models simulate the structure of the human brain, allowing them to learn complex patterns in large datasets. Neural networks are the foundation of Deep Learning techniques.

Machine Learning 52

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Data Science Dojo

AUGUST 24, 2023

MLOps practices include cross-validation, training pipeline management, and continuous integration to automatically test and validate model updates. Examples include: Cross-validation techniques for better model evaluation. Managing training pipelines and workflows for a more efficient and streamlined process.

Machine Learning 195

Machine Learning Machine Learning ML ML 195

Heartbeat

OCTOBER 31, 2023

Cross-validation is recommended as best practice to provide reliable results because of this. Editor's Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

Decision Trees 52

Decision Trees Deep Learning Deep Learning Cross Validation 52

Pickl AI

DECEMBER 10, 2024

Neural Networks In Deep Learning, key model-related hyperparameters include the number of layers, neurons in each layer, and the activation functions. Combine with cross-validation to assess model performance reliably. Best Practices Start with Grid Search for smaller, more defined hyperparameter spaces.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Mlearning.ai

FEBRUARY 6, 2023

MNIST examples Experiment on MNIST Figure 3 shows the 2D CNN architecture that was trained and validated using 10-fold cross-validation on the MNIST dataset. The answer is … almost , and I will show you this in an experiment on the well-known MNIST dataset (Figure 2 shows examples from the MNIST dataset).

Cross Validation 52

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Pickl AI

FEBRUARY 12, 2024

What is cross-validation, and why is it used in Machine Learning? Cross-validation is a technique used to assess the performance and generalization ability of Machine Learning models. What is the Central Limit Theorem, and why is it important in statistics?

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

AWS Machine Learning Blog

MAY 31, 2024

SageMaker notably supports popular deep learning frameworks, including PyTorch, which is integral to the solutions provided here. Following Nguyen et al , we train on chromosomes 2, 4, 6, 8, X, and 14–19; cross-validate on chromosomes 1, 3, 12, and 13; and test on chromosomes 5, 7, and 9–11.

AWS 115

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Mlearning.ai

JULY 6, 2023

Optuna formulates the hyperparameter optimization problem as a process of minimizing or maximizing an objective function that takes a set of hyperparameters as an input and returns its (validation) score. Optuna has many uses, both in machine learning and in deep learning.

Machine Learning 52

Machine Learning Machine Learning Algorithm ML 52

Heartbeat

JUNE 14, 2023

Measuring Calibration in Deep Learning. Cross Validated] Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. 10] Nixon, Jeremy, et al. CVPR workshops.

Deep Learning 59

Deep Learning Deep Learning Support Vector Machines Machine Learning 59

Mlearning.ai

MAY 23, 2023

What is deep learning? What is the difference between deep learning and machine learning? Deep learning is a paradigm of machine learning. In deep learning, multiple layers of processing are involved in order to extract high features from the data. What is a computational graph?

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 2, 2023

Broadly this domain can be divided into the following categories: Key Machine Learning Algorithms and Their Applications – A list of common algorithms (e.g., Broadly this domain can be divided into the following categories: Key Machine Learning Algorithms and Their Applications – A list of common algorithms (e.g.,

Data Scientist 52

Data Scientist Data Science Data Visualization Machine Learning 52

Heartbeat

MARCH 27, 2023

Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Machine Learning 52

Machine Learning Machine Learning Algorithm ML 52

Heartbeat

DECEMBER 19, 2023

With the advent of Deep Learning, recommender systems have seen significant advancements. Editor's Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

Deep Learning 52

Deep Learning Deep Learning Algorithm Machine Learning 52

Mlearning.ai

APRIL 11, 2023

Use the cross validation technique to provide a more accurate estimate of the generalization error. This phenomenon was observed through some algorithms such as linear regression and neural networks [4] and remains an active area of research in the field of Machine Learning/Deep Learning.

Cross Validation 52

Cross Validation Machine Learning Machine Learning Deep Learning 52

Pickl AI

SEPTEMBER 17, 2024

What Is the Difference Between Artificial Intelligence, Machine Learning, And Deep Learning? Artificial Intelligence (AI) is a broad field that encompasses the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 52

Heartbeat

OCTOBER 23, 2023

Image and Signal Processing: In medical imaging and signal processing, data scientists and machine learning engineers employ advanced algorithms to extract valuable information from images, such as CT scans, MRIs, and EKGs. We're committed to supporting and inspiring developers and engineers from all walks of life.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Big Data Analytics 52

Pickl AI

MAY 24, 2023

Scikit-Learn Scikit Learn is associated with NumPy and SciPy and is one of the best libraries helpful for working with complex data. Its modified feature includes the cross-validation that allowing it to use more than one metric. The number of TensorFlow applications is unlimited and is the best version.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Pickl AI

DECEMBER 10, 2024

Methods like Histogram of Oriented Gradients (HOG) or Deep Learning models, particularly Convolutional Neural Networks (CNNs), effectively extract meaningful representations from images. Machine Learning models can analyse complex datasets and deliver impactful results by converting unstructured data into structured features.

Machine Learning 52

Machine Learning Machine Learning Algorithm Deep Learning 52

Heartbeat

MAY 29, 2023

Hyperparameters are the configuration variables of a machine learning algorithm that are set prior to training, such as learning rate, number of hidden layers, number of neurons per layer, regularization parameter, and batch size, among others. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

DataRobot Blog

DECEMBER 20, 2022

For example, the model produced a RMSLE (Root Mean Squared Logarithmic Error) Cross Validation of 0.0825 and a MAPE (Mean Absolute Percentage Error) Cross Validation of 6.215. This would entail a roughly +/-€24,520 price difference on average, compared to the true price, using MAE (Mean Absolute Error) Cross Validation.

AI 59

AI AI Cross Validation Machine Learning 59

Pickl AI

DECEMBER 16, 2024

Split the Data: Divide your dataset into training, validation, and testing subsets to ensure robust evaluation. Cross-validation: Implement cross-validation techniques to assess how well your model generalizes to unseen data.

AI 52

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