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Machine Learning
Part1: Supervised and Unsupervised
Part2: Regression and Classification
Part3: Bias and Variance
Part4: Underfitting and Overfitting
Part5: K-Fold Cross Validation
Part6: Lasso and Ridge
Part7: Feature Engineering
Part8: Ensemble Methods
Part9: Decision Trees
Part10: Random Forest
Part11: K-NN
Deep Learning
Part1: Convolution
Part2: Bias
Part3: Activation Functions
Part4: Batch Normalisation
Part5: Pooling layer
Part6: Data Augmentation
Part7: Losses
Part8: Gradient
Part9: Optimizers
Part10: Back-Propagation and Training
Azure Machine Learning
Part1: Workspace
Part2: Compute Resources
Part3: Exploring Data
Part4: Train Using Azure ML
Part5: Deploy
Part6: Clean-up
News
Deep Learning
December 15, 2023
Streamlining Neural Network Training: The Power of Back-Propagation
Puru Dewan
December 14, 2023
Mastering Optimizers in CNNs: SGD to Adam
Puru Dewan
December 14, 2023
Optimizing CNNs with Gradient Calculation: A Deep Dive
Puru Dewan
December 13, 2023
Navigating Loss Functions in Deep Learning: From MSE to Triplet Loss
Puru Dewan
December 12, 2023
Enhancing Machine Learning Models with Data Augmentation in PyTorch
Puru Dewan
December 11, 2023
Demystifying Pooling Layers in CNNs: MaxPool, AvgPool, and More
Puru Dewan
December 11, 2023
Maximizing Efficiency with Batch Normalization in CNNs
Puru Dewan
December 11, 2023
Exploring Activation Functions in Neural Networks: ReLU, Sigmoid, and TanH
Puru Dewan
December 9, 2023
Understanding the Role of Bias in Convolutional Neural Networks (CNN)
Puru Dewan
December 9, 2023
Understanding Convolution Neural Networks (CNN)
Puru Dewan
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