Vivek Kaushik
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From AI to GPT: A Beginner’s Guide to Neural Networks and Modern LLMs

Created
Aug 24, 2025 02:27 PM
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1. What Is AI, ML, and Deep Learning?

Before diving into neural networks and GPT-like models, we need the big picture.
  • Artificial Intelligence (AI): The broad field where machines perform tasks that we consider “intelligent”—like playing chess, recommending movies, or understanding language.
  • Machine Learning (ML): A subset of AI where systems learn patterns from data instead of being explicitly programmed.
  • Deep Learning (DL): A subset of ML that uses multi-layered neural networks to learn complex patterns (like understanding images, speech, and natural language).
You can think of it as:
AI
 └── Machine Learning
      └── Deep Learning

2. Machine Learning Categories

ML comes in several flavors depending on the data and the learning objective:

2.1 Supervised Learning

  • What: Train a model on labeled data (input + correct output).
  • Examples: Predicting house prices, spam detection.
  • Analogy: Like a student learning from solved examples.

2.2 Unsupervised Learning

  • What: Data has no labels; model finds patterns or groups.
  • Examples: Customer segmentation (clustering similar users), dimensionality reduction.
  • Analogy: Like walking into an unknown crowd and grouping people by observed similarity.

2.3 Self-Supervised Learning

  • What: A twist on supervised learning: models create their own labels from raw data.
  • Example: Masking words in a sentence and predicting the missing word (used in LLMs like BERT and GPT).
  • Analogy: Learning by solving your own puzzles.

2.4 Reinforcement Learning (RL)

  • What: An agent interacts with an environment, gets rewards/penalties, and learns strategies.
  • Examples: AlphaGo, robotic control.
  • In LLMs: Used later as RLHF (Reinforcement Learning with Human Feedback).
(There are also semi-supervised and other hybrid methods, but these four cover most cases.)
Machine Learning
 ├── Supervised
 ├── Unsupervised
 ├── Self-Supervised
 └── Reinforcement Learning

3. Neural Networks: The Engine of Deep Learning

3.1 What is a Neural Network?

A neural network (NN) is inspired by the brain. It consists of:
  • Input layer: Takes in features (numbers representing data).
  • Hidden layers: Multiple layers of “neurons” that learn patterns.
  • Output layer: Produces predictions.
Each neuron has:
  • Weights (w): Importance of each input.
  • Bias (b): An extra “push” that shifts the activation.
  • Activation function: Decides if the neuron should “fire” (like ReLU or Sigmoid).

3.2 How Does It Work?

  1. Forward Pass:
      2. Multiply inputs by weights, add biases, pass through activation → move to next layer.
  1. Prediction:
      2. Output layer generates ŷ (y-hat), the predicted value.
  1. Loss Calculation:
      2. Compare ŷ with actual label y using a loss function (e.g., Mean Squared Error, Cross-Entropy).
  1. Backpropagation:
      2. Calculate how much each weight contributed to the error.
  1. Gradient Descent:
      2. Adjust weights and biases slightly to reduce the loss.
Repeat this process over and over (epochs) until the model learns.

3.3 A Simple Example

Suppose you want to predict house prices from square footage:
  • Input (x): 2000 sq.ft.
  • Weight (w): 150 (price per sq.ft.)
  • Bias (b): 10,000 (base cost)
Prediction:
ŷ = x * w + b = 2000*150 + 10000 = 310,000
If actual price y = 300,000
Loss = (ŷ - y)² = (10,000)² = 100 million.
Backprop adjusts w and b to reduce this error.

3.4 Bias Explained Simply

Bias allows flexibility. Without it, the prediction line must pass through the origin (0,0). With bias, it can shift up/down to fit data better.

4. Types of Neural Networks

  • Feedforward NN: Basic networks; info flows forward only.
  • CNN (Convolutional NN): Great for images.
  • RNN (Recurrent NN): For sequences like text; remembers past inputs.
  • Transformers: Modern architecture used in LLMs; handles sequences more efficiently.

5. Transformers and LLMs

Transformers revolutionized NLP.

5.1 Encoder vs Decoder

  • Encoder: Understands and represents input (e.g., BERT).
  • Decoder: Generates text (e.g., GPT).
  • Encoder-Decoder: Both (e.g., T5, Gemini).
Transformers use self-attention, which lets the model weigh relationships between all tokens in a sequence simultaneously.

6. How Are LLMs Trained?

Modern LLMs (like GPT-5, Llama, Gemini):
  1. Pretraining: Self-supervised learning on trillions of tokens; predict next word.
  1. Fine-tuning: Adapt to specific tasks or styles.
  1. RLHF: Improve responses using human feedback and reinforcement learning.

Training Techniques

  • Activation functions: ReLU, GeLU.
  • Loss functions: Cross-entropy for language models.
  • Optimizers: Adam, SGD (variants of gradient descent).
  • Regularization: Dropout, weight decay to prevent overfitting.

7. Why Transformers Beat RNNs

  • Process sequences in parallel (faster).
  • Better at handling long-term dependencies.
  • Scale well with data and compute.

8. Key Takeaways

  • AI → ML → DL is a hierarchy.
  • ML has multiple paradigms (supervised, unsupervised, self-supervised, RL).
  • Neural networks are the core of DL; Transformers power modern LLMs.
  • LLMs like GPT are trained using self-supervised learning + RLHF.

Final Words

We went from the basics of AI to the nitty-gritty of LLM training. While math can get heavy, the intuition remains simple: models learn patterns from data by adjusting weights and biases to reduce errors.
Next time you interact with GPT or Gemini, you’ll know the journey:
data → neural nets → transformers → fine-tuning → conversational AI!