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Little Bits of Artificial Intelligence

Little Bits of Artificial Intelligence

  • Contents
    • Technical Grouping
  • Spread the Basics

Basic Concepts

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning

Classification Problems

  • Classification vs Regression
  • Logistic Regression
  • Support Vector Machine

Tensors

  • What is a Tensor (Primary Concepts)
  • Relationship of Tensors and Neural Networks Computing (Basic Concept)
  • Tensor, Weight and Neural Network

Neural Network Advanced Concepts

  • 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
  • Prediction Error in Neural Network
  • Minimize objective in Neural Network
  • Gradient Descent in Neural Network
  • First and Second Derivatives in Neural Networks

Neural Network Initialisation

  • 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

Deep Learning

  • Deep Learning with Neural Networks
  • Machine Learning and Deep Learning – why and where should we use the concepts?
  • Deep Learning Categories

Regression Problems

  • Linear Regression
  • Ridge Regression
  • Lasso Regression
  • Elasticnet Regression
  • Decision Tree Regression
  • Gradient Boosting Regression
  • KNN Regression
  • Bayesian Regression

Neural Network Basic Concepts

  • 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

L1 and L2 Regularisation

  • 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

Neural Network Advanced Concepts - 2

  • 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
  • Feature Engineering in Neural Network
  • Encoding in Neural Networks
  • One-Hot in Neural Network
  • Bucketing in Neural Networks
  • Normalization in Neural Networks
  • Standardization in Neural Networks
  • Machine Learning Algorithm Selection – Some of the Key Areas to understand
  • Validation Set in Neural Network
  • Hyperparameters in Neural Networks
  • Parameters vs Hyperparameters in Neural Network
  • Hyperparameter Tuning in Neural network
  • Cross Validation in Neural Network

Quick Refresh

  • Python Primer
  • Linear Algebra Primer
  • Vector Primer
  • Matrix Primer
  • Basic Statistics
  • Advanced Statistics
  • Matplotlib Primer
  • Generative AI made sense
  • Privacy Policy
  • GDPR Statement
  • Terms & Conditions

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