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.

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Data Science Dojo

AUGUST 24, 2023

ML models have grown significantly in recent years, and businesses increasingly rely on them to automate and optimize their operations. However, managing ML models can be challenging, especially as models become more complex and require more resources to train and deploy. What is MLOps?

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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).

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DrivenData Labs

JANUARY 11, 2023

Feature engineering vs. neural network feature learning : The top performing solutions included deep learning models that used image or sequence representations of the data as inputs and feature engineering to capture the mass spectrograms. All winners who used deep learning fine-tuned pre-trained models.

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Mlearning.ai

MARCH 7, 2023

Dive Into Deep Learning — Part 3 In this part, I will summarize section 3.6 Dive Into Deep Learning — Part 2 Dive Into Deep Learning — Part1 Generalization The authors give an example of students who prepare for an exam, student 1 memorizes the past exams questions and student 2 discovers patterns in the questions, if the exam is 1.

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Dataconomy

AUGUST 22, 2023

Even if your plan on paper is quite simple, creating an algorithm from scratch is complicated and unpredictable, but luckily there are many mathematical theorems you can use for your next ML initiative. And lastly, integrating Bayesian techniques with deep learning, which has gained tremendous popularity, presents additional challenges.

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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. It is clear that implementation of this library for ML dimension.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Mlearning.ai

FEBRUARY 15, 2024

Data Science Project — Predictive Modeling on Biological Data Part III — A step-by-step guide on how to design a ML modeling pipeline with scikit-learn Functions. Many ML optimizing functions assume that data has variance in the same order that means it is centered around 0. You can refer part-I and part-II of this article.

Data Science 52

Data Science Decision Trees Exploratory Data Analysis Cross Validation 52

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.

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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.

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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.

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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. What is MLOps?

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Heartbeat

FEBRUARY 20, 2023

Comet ML has an intricate web of tools that combine simplicity and safety and allows one to not only track changes in their model but also deploy them as desired or shared in teams. Workflow Overview The typical iterative ML workflow involves preprocessing a dataset and then developing the model further. Big teams rely on big ideas.

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Mlearning.ai

MAY 23, 2023

Let us first understand the meaning of bias and variance in detail: Bias: It is a kind of error in a machine learning model when an ML Algorithm is oversimplified. It is introduced into an ML Model when an ML algorithm is made highly complex. This model also learns noise from the data set that is meant for training.

Data Science 52

Data Science Decision Trees Deep Learning Deep Learning 52

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.

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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.

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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

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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.

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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.

AI 59

AI AI Cross Validation Machine Learning 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. Each season consists of around 17,000 plays.

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Heartbeat

MAY 29, 2023

The ML process is cyclical — find a workflow that matches. Check out our expert solutions for overcoming common ML team problems. Batch size and learning rate are two important hyperparameters that can significantly affect the training of deep learning models, including LLMs.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

The MLOps Blog

APRIL 20, 2023

Hardware-specific optimization : optimize your model for the specific hardware it will be deployed on, such as using libraries like TensorFlow Lite or Core ML, which are designed for edge devices like smartphones and IoT devices. Over the years, I’ve worked with various formats, such as TensorFlow Lite, ONNX, and Core ML.

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Mlearning.ai

FEBRUARY 2, 2023

The use of Jupyter Notebooks was done in order to make it possible to train and validate the models on Google Colab in order to get access to free GPUs. doing cross-validation on the training set and a mean absolute error of 8.3 Proceedings of the Northern Lights Deep Learning Workshop 4 , (2023). & Tavashi, B.

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Mlearning.ai

JUNE 27, 2023

The optimal value for K can be found using ideas like Cross Validation (CV). In K-Means clustering, the parameter K represents the number of clusters, and it is a hyper parameter that needs to be determined. K = 3 ; 3 Clusters. K = No of clusters. Geometrically, K-Means clustering involves assigning centroids to groups of points.

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Mlearning.ai

FEBRUARY 23, 2023

Learning about perceptrons is important for building a foundation in neural network concepts and understanding the potential of more advanced models for solving complex problems. Read More Linear Regression from Scratch with Gradient Descent Smart Aspects of CatBoost Algorithm How Fast Is Your Light GBM?

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