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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: 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...
TensorFlow: Multiclass Classification Model Elena Daehnhardt · 6 Feb, 2022 In Machine Learning, the classification problem is categorising input data into different classes. For instance, we can categorise email messages into two groups, spam or not spam. In this case, we have two classes, we talk about binary classification. When we have more than two classes, we talk about multiclass classification. In this post, I am going to address the latest multiclass classification, on the example of categorising clothing items into clothing types. Read more...

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