Tag: Kaggle

Bias-Variance Challenge

Elena Daehnhardt · 10 Nov, 2023

In machine learning, we usually start from a simple baseline model and progressively adjust its complexity until we reach that spot with the best model performance. How can we do this? Let's detail the most essential machine learning concepts and the bias-variance challenge. Read more...

Decision Tree versus Random Forest, and Hyperparameter Optimisation

Elena Daehnhardt · 6 Nov, 2023

Decision trees, with their elegant simplicity and transparency, stand in stark contrast to the robust predictive power of **Random Forest**, an ensemble of decision trees. In this post, we compare the key distinctions, advantages, and trade-offs between these two approaches. We will use Scikit-Learn for training and testing both models and also perform hyperparameter optimisation to find both model parameters for improved performance. Read more...

TensorFlow: Evaluating the Saved Bird Species Prediction Model

Elena Daehnhardt · 2 May, 2022

In this post, I have described the process of in-depth model evaluation. I have reused the previously created EffecientNetB0 model, which is fine-tuned with the 400 Bird Species Kaggle dataset. As a result, I have found out which bird species are not well predicted. Read more...

TensorFlow: Transfer Learning (Feature Extraction) in Image Classification

Elena Daehnhardt · 3 Mar, 2022

Image classification is a complex task. However, we can approach the problem while reusing state-of-the-art pre-trained models. Using previously learned patterns from other models is named "Transfer Learning." This way, we can efficiently apply well-tested models, potentially leading to excellent performance. Read more...

TensorFlow: Convolutional Neural Networks for Image Classification

Elena Daehnhardt · 19 Feb, 2022

In this post, I have demonstrated CNN usage for birds recognition using TensorFlow and Kaggle 400 birds species dataset. We observed how the model works with the original and augmented images. Read more...

Feature preprocessing

Elena Daehnhardt · 29 Jan, 2022

Machine Learning algorithms often require that data is in a specific type. For instance, we can use only numerical data. In other cases, ML algorithms would perform better or converge faster when we preprocess data before training the model. Since we do this step before training the model, we call it preprocessing. Read more...

Tools and Data to Experiment with Machine Learning

Elena Daehnhardt · 19 Oct, 2021

Python open-source library scikit-learn provides a comprehensive selection of machine learning techniques (regression, classification, clustering), feature selection, metrics, preprocessing, and other functionality. Scikit-learn has some basic neural-network support through MLPClassifier, but for real deep learning you are better off reaching for TensorFlow's Keras API rather than the now-defunct Scikit Flow wrapper. Read more...

Bias-Variance Challenge Elena Daehnhardt · 10 Nov, 2023 In machine learning, we usually start from a simple baseline model and progressively adjust its complexity until we reach that spot with the best model performance. How can we do this? Let's detail the most essential machine learning concepts and the bias-variance challenge. Read more...
Decision Tree versus Random Forest, and Hyperparameter Optimisation Elena Daehnhardt · 6 Nov, 2023 Decision trees, with their elegant simplicity and transparency, stand in stark contrast to the robust predictive power of Random Forest, an ensemble of decision trees. In this post, we compare the key distinctions, advantages, and trade-offs between these two approaches. We will use Scikit-Learn for training and testing both models and also perform hyperparameter optimisation to find both model parameters for improved performance. Read more...
TensorFlow: Evaluating the Saved Bird Species Prediction Model Elena Daehnhardt · 2 May, 2022 In this post, I have described the process of in-depth model evaluation. I have reused the previously created EffecientNetB0 model, which is fine-tuned with the 400 Bird Species Kaggle dataset. As a result, I have found out which bird species are not well predicted. Read more...
TensorFlow: Transfer Learning (Feature Extraction) in Image Classification Elena Daehnhardt · 3 Mar, 2022 Image classification is a complex task. However, we can approach the problem while reusing state-of-the-art pre-trained models. Using previously learned patterns from other models is named "Transfer Learning." This way, we can efficiently apply well-tested models, potentially leading to excellent performance. Read more...
TensorFlow: Convolutional Neural Networks for Image Classification Elena Daehnhardt · 19 Feb, 2022 In this post, I have demonstrated CNN usage for birds recognition using TensorFlow and Kaggle 400 birds species dataset. We observed how the model works with the original and augmented images. Read more...
Feature preprocessing Elena Daehnhardt · 29 Jan, 2022 Machine Learning algorithms often require that data is in a specific type. For instance, we can use only numerical data. In other cases, ML algorithms would perform better or converge faster when we preprocess data before training the model. Since we do this step before training the model, we call it preprocessing. Read more...
Tools and Data to Experiment with Machine Learning Elena Daehnhardt · 19 Oct, 2021 Python open-source library scikit-learn provides a comprehensive selection of machine learning techniques (regression, classification, clustering), feature selection, metrics, preprocessing, and other functionality. Scikit-learn has some basic neural-network support through MLPClassifier, but for real deep learning you are better off reaching for TensorFlow's Keras API rather than the now-defunct Scikit Flow wrapper. Read more...