AMAaryan meenaindev-aaryan.hashnode.dev·Aug 16, 2025 · 3 min readWeights RegularizationIN simple words,Reduce overfitting and ensure that weights are neither too large nor too small. Now, Deep Understanding of Weight Regularization (Simple to Advanced)When we train a deep learning model, the goal is to make it learn patterns from the d...00
AMAaryan meenaindev-aaryan.hashnode.dev·Aug 1, 2025 · 3 min readWhat Are Kernel Functions? Learn Their Role in Machine LearningWhat is the Kernel function? Kernel is a technique in which we expand our data points to a higher dimension, allowing us to draw a clear boundary or distinguish between different classes. Kernel function is a mathematical function that enables algori...00
AMAaryan meenaindev-aaryan.hashnode.dev·Jun 16, 2025 · 2 min readone-hot-encoding or one-out-of-N encoding, also known as dummy variables.The way of representing categorical data is known as one-hot encoding or dummy variables. Because we cannot proceed with categorical data like a machine learning model, it does not understand text data; it only understands numerical data. And we cann...00
AMAaryan meenaindev-aaryan.hashnode.dev·Jun 9, 2025 · 2 min readThe Effect of Preprocessing on Supervised LearningWe will fit the cancer dataset without scaling and then with different types of Scaling algorithms. First, let’s fit the SVC on the original data again for comparison: from sklearn.svm import SVC X_train, X_test, y_train, y_test = train_test_split(ca...00
AMAaryan meenaindev-aaryan.hashnode.dev·Jun 9, 2025 · 3 min readHow to Effectively Preprocess and Scale Your DataWe know that some algorithms, like neural networks and SVMs, are very sensitive to the scaling of the data. Therefore, adjust the features so that data representation is more suitable for these algorithms. Sometimes we need to scale our data to 0 to ...00
