Introductory Context

• Ferdi Eruysal begins the session by asking if there are any questions.

• References previous lessons on building models, particularly K-nearest neighbors (KNN) and Naive Bayes.

Class Structure

• Today's session is dedicated to neural networks:

  • First 50 minutes: Theoretical understanding of how neural networks work.

  • Final 15 minutes: Practical model-building session.
    h- Encourages students to work on their assignments early and to ensure proper computational time for optimization.

Overview of Neural Networks

• Definition: Neural networks are simple yet powerful machine learning algorithms that replicate human brain functioning.

• Mentioned applications: AI systems like ChatGPT and virtual assistants like Siri are based on neural networks.

• Historical context: Neural networks have existed for over 60 years but gained popularity in recent years due to advances in computational power.

Biological Foundations of Neural Networks

• Explanation of a biological neuron:

  • Neurons are nerve cells that transfer information throughout the body.

  • Example: Touching a hot surface activates neurons which rapidly relay information to the brain for response.

• Analogy between biological neurons and artificial neurons:

  • The human brain continually processes data; in a neural network, artificial neurons mimic this through calculations.

Key Concepts in Neural Networks

• Neurons communicate through activation:

  • Basic idea: A neuron can fire a signal (e.g., sending a number to signify data importance).

  • High signals (e.g., 1 million) indicate significant information; low signals (e.g., 1) indicate trivial data.

• Structure of neural networks:

  • Neurons are interconnected, allowing signals to pass along pathways to other neurons.

  • Activation of a neuron depends on its input and weight assignments (calculations performed by each neuron).

Perceptrons

• Each neuron (referred to as a perceptron in machine learning) performs calculations on its inputs:

  • Example operations include multiplication and conditional firing based on thresholds.

  • This results in outputs that are passed to the next layer of neurons.

Network Architecture

• Layers of a Neural Network: Input layer, hidden layers, and output layer.

  • Input Layer: Number of neurons corresponds to features in the dataset (e.g., for a 28x28 pixel image, there are 784 neurons).

  • Output Layer: Usually corresponds to the classes in classification tasks (e.g., 10 neurons for digit classification).

  • Hidden Layers: Perform the majority of the calculations and can vary in number and size.

Classification Problem

• Neural networks can handle multi-class classification problems where more than two classes exist, distinguishing them from earlier binary classification tasks.

Learning Process in Neural Networks

• Learning refers to optimizing weights to improve network accuracy.

• Weight adjustments are crucial:

  • Each connection's strength (weight) dictates the influence of one neuron on another.

  • The network’s aim is to adjust these weights appropriately through training.

Activation Function

• Function used to squish the output of each neuron to a value between 0 and 1 (e.g., sigmoid function):

  • Transforms raw output to prevent the output from diverging to extremely high or low values.

Bias

• In addition to weights, each neuron has a bias term helping to shift the output function, allowing more flexibility in the model's learning capabilities.

The Role of Computational Power

• Importance of GPUs: Specialized hardware (like those from NVIDIA) designed for rapid calculations required by neural networks.

• As neural networks involve numerous calculations due to multiple layers and weights, the use of powerful GPUs is essential for efficiency.

Training Process

• During training, the model adjusts weights based on errors made in predictions compared to true outcomes.

• The learning rate is critical; it determines how much weights change during optimization:

  • A small learning rate means gradual changes, while large changes may destabilize learning.

Conclusion

• This introductory lesson sets the groundwork for deeper topics, including how large models (like ChatGPT) operate.

• Emphasis on understanding this session’s content before moving on to more complex subjects in future classes.