Fundamentals of machine learning and neural networks: Quick Reference — Core Machine Learning and AI Knowledge (NVIDIA-Certified Associate: Generative AI LLM)

Fundamentals of Machine Learning and Neural Networks – Quick Reference

This quick reference summarizes the essential concepts and definitions related to machine learning and neural networks, foundational for the NVIDIA-Certified Associate: Generative AI LLM exam.

Key Concepts in Machine Learning

• Machine Learning (ML): A subset of AI focused on algorithms that improve automatically through experience and data.
• Supervised Learning: Training models on labeled data to predict outputs from inputs.
• Unsupervised Learning: Finding patterns or structures in unlabeled data.
• Reinforcement Learning: Learning optimal actions through rewards and penalties in an environment.
• Overfitting: When a model learns noise or details in training data, reducing generalization.
• Underfitting: When a model is too simple to capture underlying data patterns.

Neural Networks Basics

• Neuron (Node): Basic computational unit that receives inputs, applies weights, sums them, adds bias, and passes through an activation function.
• Layers: Input layer (data input), hidden layers (feature extraction), and output layer (final prediction).
• Weights and Biases: Parameters learned during training to minimize error.
• Activation Functions: Introduce non-linearity. Common types include:
  • ReLU (Rectified Linear Unit)
  • Sigmoid
  • Tanh
• Forward Propagation: Passing inputs through the network to generate output.
• Backpropagation: Algorithm to update weights by propagating error gradients backward.

Deep Learning Model Architectures

• Feedforward Neural Networks (FNN): Data flows in one direction from input to output.
• Convolutional Neural Networks (CNNs): Specialized for spatial data, using convolutional layers to detect features.
• Recurrent Neural Networks (RNNs): Designed for sequential data, maintaining internal state.
• Transformers: Use self-attention mechanisms, foundational for large language models (LLMs).

Transfer Learning

• Definition: Leveraging pre-trained models on large datasets to improve efficiency and performance on new, related tasks.
• Benefits: Reduces training time, requires less data, and often improves accuracy.
• Common Practice: Fine-tuning the last layers of a pre-trained model for a specific application.

Training Deep Learning Models – Fundamental Techniques

• Loss Function: Quantifies the difference between predicted and true values (e.g., cross-entropy, mean squared error).
• Optimizer: Algorithm to minimize the loss function (e.g., SGD, Adam).
• Learning Rate: Controls the step size during weight updates.
• Epoch: One complete pass through the entire training dataset.
• Batch Size: Number of samples processed before updating model parameters.
• Regularization: Techniques like dropout or L2 regularization to prevent overfitting.

Worked Example: Forward Pass in a Simple Neural Network

Problem: Given inputs x = [2, 3], weights w = [0.5, -1], bias b = 0.1, and ReLU activation, compute the output.

Solution:

• Calculate weighted sum: z = (2 * 0.5) + (3 * -1) + 0.1 = 1.0 - 3.0 + 0.1 = -1.9
• Apply ReLU activation: output = max(0, -1.9) = 0

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