Hi everyone! :)
\nI have created three notebooks for this competition, which may serve as tutorials for all levels of expertise. Let me introduce them, from the most basic to the most advanced ones:
\nI hope you find value in my work. If that is the case, please consider giving them an UpVote. Thank you very much for your time and attention :)
\nIf you have comments, find errors, or want to give constructive feedback, please drop them in the Comments section below. I will answer them all!
", "messages": [ { "id": 2540596, "postDate": "2023-11-27T20:07:13.670Z", "content": "Hi everyone! :)
\nI have created three notebooks for this competition, which may serve as tutorials for all levels of expertise. Let me introduce them, from the most basic to the most advanced ones:
\nI hope you find value in my work. If that is the case, please consider giving them an UpVote. Thank you very much for your time and attention :)
\nIf you have comments, find errors, or want to give constructive feedback, please drop them in the Comments section below. I will answer them all!
", "rawMarkdown": "Hi everyone! :)\n\nI have created three notebooks for this competition, which may serve as **tutorials** for all levels of expertise. Let me introduce them, from the most basic to the most advanced ones:\n\n1. **PCA and KNN**\n- Link: https://www.kaggle.com/code/marcpaulo/pca-knn\n- Machine Learning algorithms (not Deep Learning)\n- Understand and use PCA to perform dimensionality reduction\n- Train a KNN Classifier on the principal components\n\n2. **MultiLayer Perceptron using Sklearn**\n- Link: https://www.kaggle.com/code/marcpaulo/sklearn-mlp\n- Deep Learning with MultiLayer Perceptron (not Convolutional)\n- Explore and understand the potential of this type of architecture\n\n\n3. **Convolutional NN using PyTorch**\n- Link: https://www.kaggle.com/code/marcpaulo/pytorch-cnn-tutorial\n- Deep Learning with Convolutional Neural Networks\n- Explore and understand the potential of this type of architecture\n- AI Explainability: visualize the learned convolutional filters\n\nI hope you find value in my work. If that is the case, please consider **giving them an UpVote**. Thank you very much for your time and attention :)\n\nIf you have comments, find errors, or want to give constructive feedback, please drop them in the *Comments* section below. I will answer them all!", "votes": 5 } ], "comments": [], "raw_markdown_by_id": { "2540596": "Hi everyone! :)\n\nI have created three notebooks for this competition, which may serve as **tutorials** for all levels of expertise. Let me introduce them, from the most basic to the most advanced ones:\n\n1. **PCA and KNN**\n- Link: https://www.kaggle.com/code/marcpaulo/pca-knn\n- Machine Learning algorithms (not Deep Learning)\n- Understand and use PCA to perform dimensionality reduction\n- Train a KNN Classifier on the principal components\n\n2. **MultiLayer Perceptron using Sklearn**\n- Link: https://www.kaggle.com/code/marcpaulo/sklearn-mlp\n- Deep Learning with MultiLayer Perceptron (not Convolutional)\n- Explore and understand the potential of this type of architecture\n\n\n3. **Convolutional NN using PyTorch**\n- Link: https://www.kaggle.com/code/marcpaulo/pytorch-cnn-tutorial\n- Deep Learning with Convolutional Neural Networks\n- Explore and understand the potential of this type of architecture\n- AI Explainability: visualize the learned convolutional filters\n\nI hope you find value in my work. If that is the case, please consider **giving them an UpVote**. Thank you very much for your time and attention :)\n\nIf you have comments, find errors, or want to give constructive feedback, please drop them in the *Comments* section below. I will answer them all!" } }