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. Jupyter notebooks allow you to create and share live code, equations, visualisations, and narrative text documents.

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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.

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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.

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Pickl AI

NOVEMBER 2, 2023

– Quick comparison of libraries like Matplotlib, Seaborn, and ggplot2 – Information on how to install and import these libraries – Links to official documentation and additional resources Click here to access -> Cheat sheet for Popular Data Visualization Libraries How to Create Common Plots and Charts?

Data Scientist 52

Data Scientist Data Science Data Visualization Machine Learning 52

Heartbeat

DECEMBER 19, 2023

With the advent of Deep Learning, recommender systems have seen significant advancements. Resources Comet Documentation: Comet's official documentation provides detailed information on integrating Comet into machine learning projects, tracking experiments, and visualizing results.

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Heartbeat

MARCH 27, 2023

from comet_ml import API, Experiment experiment = Experiment() api = API() #naming the model "model1" and highlighting where it is stored in the computer experiment.log_model("model1", "/home/mwaniki-new/Documents/Stacking/model1.joblib") fit(X, y) #exporting model to desired location dump(model1, "model1.joblib")

Machine Learning 52

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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.

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Pickl AI

DECEMBER 16, 2024

Documenting Objectives: Create a comprehensive document outlining the project scope, goals, and success criteria to ensure all parties are aligned. Split the Data: Divide your dataset into training, validation, and testing subsets to ensure robust evaluation. accuracy, precision).

AI 52

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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. This can include user manuals, FAQs, and chatbots for real-time assistance.

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DagsHub

APRIL 21, 2024

Moving the machine learning models to production is tough, especially the larger deep learning models as it involves a lot of processes starting from data ingestion to deployment and monitoring. It provides different features for building as well as deploying various deep learning-based solutions. What is MLOps?

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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.

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Pickl AI

SEPTEMBER 12, 2024

Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset.

Data Analyst 52

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Pickl AI

DECEMBER 12, 2024

Regular updates, detailed documentation, and widespread tutorials ensure that users have ample resources to troubleshoot and innovate. Monitor Overfitting : Use techniques like early stopping and cross-validation to avoid overfitting. This flexibility is a key reason why its favoured across diverse domains.

Machine Learning 52

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The MLOps Blog

APRIL 20, 2023

ONNX : when working with different deep learning frameworks like PyTorch or TensorFlow, I often choose the Open Neural Network Exchange (ONNX) format. It’s easy to work with, supports asynchronous programming, and offers built-in validation and documentation features.

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