Introduction to Machine Learning with Neural Networks Training

• Arthur C. Clarke’s 3rd Law
• What is Machine Learning?
• Terminology: Features and Targets
• Terminology: Observations (Examples)
• Supervised and Unsupervised ML
• “Classical” ML and ML with Neural Networks
• The Shared Concepts and Principles
• AI and Data Science
• What is a Neural Network?
• Network vs Model
• Positional Types of Layers
• Deep Learning
• How Does My Network Know Which Problem I Want It to Solve?
• The Desired Model Properties
• The Artificial Neuron
• Perceptron
• The Perceptron Symbol
• A Breakthrough in Neural Networks Design
• Perceptrons and MLPs
• A Basic Neural Network Example
• Popular Activation Functions
• Navigating Neural Networks Layers
• A Sample Neural Network Diagram
• Model Training
• Measuring the Error with the Loss (Cost) Function
• Loss Function Properties
• Mini-batches and Epochs
• Neural Networks Training Steps
• The Chain Rule in Calculus
• The Chain Rule in Neural Networks
• The Gradient Descent Formulation
• Applying Efficiencies with Autodiff …
• Types of Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• RNN Common and Unrolled Visual Representation
• Autoencoders
• Neural Network Libraries and Frameworks
• Ethical AI
• Then a Miracle Occurs …

• Supervised and Unsupervised ML
• Self-Supervised Learning
• Terminology: Features and Targets
• Terminology: Observations (Examples)
• Notation for Observations
• Data Structures: Tensors
• Continuous and Categorical Features
• Continuous Features
• Categorical Features
• Feature Types Visually
• Common Distance Metrics
• The Euclidean Distance
• What is a Model?
• Model Parameters and Hyperparameters
• Model Accuracy
• Loss Functions
• Mean Squared Error (MSE)
• Mean Absolute Error (MAE)
• (Categorical) Cross Entropy Loss
• The Cross Entropy Loss Visually
• The softmax Function
• Confusion Matrix
• The Binary Classification Confusion Matrix
• Multi-class Classification Confusion Matrix Example
• Feature Engineering
• Data Scaling and Normalization
• Bias-Variance (Underfitting vs Overfitting) Trade-off
• Bias and Variance Visually
• Underfitting vs Overfitting Visually
• Ways to Balance Off the Bias-Variance Ratio
• Regularization
• Dimensionality Reduction
• Model Validation and Avoiding Test Data Leakage
• Training Error vs Validation Error Diagram
• Training/Validation/Test Data Split Ratios
• Online Glossaries

Introduction to Keras

• What is Keras?
• Core Keras Data Structures
• Layers in Keras
• The Dense Layer
• Defining the Layer Activation Function
• Models in Keras
• Components of a Keras Model
• Creating Neural Networks in Keras
• The Sequential Model
• A Sequential Model Code Example
• The Strengths and Weaknesses of Sequential Models
• The Functional API
• A Functional API Example
• The Strengths and Weaknesses of the Functional API
• Making New Layers and Models via Subclassing
• The Strengths and Weaknesses of Subclassing