- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Reinforcement Learning
- Linear Regression
- Ridge Regression
- Lasso Regression
- Elasticnet Regression
- Decision Tree Regression
- Gradient Boosting Regression
- KNN Regression
- Bayesian Regression
- Classification vs Regression
- Logistic Regression
- Support Vector Machine
- Traditional Machine Learning vs Deep Learning
- How Learning Algorithms Work
- Neural Network (Primary Concepts)
- Building Blocks of Neural Network (Primary Concepts)
- Input Layer and Weight Relevancy in Neural Network
- Weights and Biases Relevancy in Neural Network
- Hidden Layers Relevancy in Neural Network
- Activation Function Relevancy in Neural Network
- Output Layer Relevancy in Neural Network
- Feedback Layer Relevancy of Neural Network
- Error Reduction in Prediction of Result – Conceptual Steps in Neural Network
- Backpropagation in Neural Networks
- Feed Forward Mechanism in Neural Network
- Loss Function Relevancy in Neural Network
- Learning Rate Adjustment Impact in Neural Network
- Overfitting vs Underfitting Impact in Neural Network
- Mean Square Error Usage in Neural Network Use Cases
- What is a Tensor (Primary Concepts)
- Relationship of Tensors and Neural Networks Computing (Basic Concept)
- Tensor, Weight and Neural Network
- Advanced Neural Network Concepts – Just a Heads up for Now
-
Feed Forward Mechanism with Multiple Neurons
- Backpropagation with Multiple Neurons
- Epoch in Neural Network
- Binary Cross Entropy Relevancy in Neural Network
- Categorical Cross-Entropy Relevancy in Neural Network
- Gradient Descent Concept Relevancy in Neural Network
- Weight Initialization Techniques and Applicability in for Different Use Cases in Neural Network
- Xavier Initialization Applicability in Neural Network
- Sparse Initialization Applicability in Neural Network
- LeCun Initialization Applicability in Neural Network
- Regularization in Neural Network
- L1 Regularization in Neural Network
- L2 Regularization in Neural Network
- L1 Regularization vs L2 Regularization Selection for Different Use Cases in Neural Network
- Prediction Error in Neural Network
- Minimize objective in Neural Network
- Gradient Descent in Neural Network
- Mean Square in Neural Network
- First and Second Derivatives in Neural Networks