Overview of the Class Session

• Instructor: Ferdi Eruysal

• Topics Covered: Neural networks, model building, data clinics, multi-class classification, gradient descent, backpropagation, and more.

Class Agenda

• Continuing with neural networks.

• Focus on key and technical concepts relevant to data analysts.

Initial Discussion

• Questions and Check-ins:

  • Instructor checks for questions from students.

  • Collects initials for attendance using an Italian sheet.

  • Addresses team project setups and assignments.

  • Mention of upcoming class assignments post-discussion.

• Reminder to students about team project timelines:

  • Aim to complete half of the project before Thanksgiving (3-4 weeks away).

Neural Networks Introduction

• Gradient Descent:

  • Central to how neural networks learn.

  • Essential knowledge for understanding machine learning operations.

• Neural Network Functionality:

  • Focus on handwritten digit classification as a common practical example (MNIST dataset).

  • Structure: images represented in a 28x28 pixel grid.

    • Each pixel's grayscale value between 0 (white) and 1 (black).

    • 784 neurons in the input layer activated based on pixel values.

  • Connection of neurons across layers via weighted sums and biases.

    • Each neuron's activation determined by previous layers.

    • Use functions like Sigmoid and ReLU for activation processes.

Network Architecture

• The network used for examples comprises:

  • Two hidden layers, each containing 16 neurons.

  • Total parameters: approximately 13,000 weights and biases to adjust.

• Classification Task:

  • The output layer’s activation indicates which digit (0-9) the network identifies based on weighted sums from layers.

  • Insights into neuron behavior:

    • Layered architecture allows for progressive detection of features (e.g., edges, loops).

Training Process

• Training Data:

  • Network requires large datasets (e.g., MNIST) for effective learning.

  • Adjusts weights and biases based on input images and their labels.

• Cost Function/ Error Calculation:

  • Defines how far off the predictions are from actual outcomes.

  • Utilizes a loss function to represent the total error, applicable across all machine learning models — including decision trees and regression models.

  • Example of calculating loss when predicting the digit ‘3’ instead of ‘10’ during training.

    • The differences of predicted outputs compared to actual values are squared, summed, and averaged over all training examples.

Cost Function and Gradient Descent

• Conceptual Framework:

  • Understanding loss as a function providing feedback to the network — essential for evaluating performance.

• Optimization Strategy:

  • Neurons' outputs interpreted as probabilities must achieve a total sum of 1 after training.

  • Proportional metrics introduced to assess changes to weights based on error rates.

• Importance of the learning rate is discussed:

  • Controls the extent of weight updates during training iterations.

  • High learning rates might cause oscillation, while low rates can slow convergence to a minimum.

Backpropagation Technique

• Definition:

  • A method for updating weights by propagating errors backward through the layers of the network.

  • Weights adjusted sequentially from the output layer back to the input layer.

  • Aims to minimize the cost function iteratively until optimal weights are achieved.

• Gradient Descent Connection:

  • Slope of the cost function indicates the direction and magnitude of weight updates.

  • Algorithm to approach local minimums in cost functions across numerous parameters.

Multi-Class Classification Application

• Discussion on transitioning from binary classification to multi-class classification:

  • Focus on drug classification example with multiple categorical outcomes.

  • Different treatment of labels according to the problem type.

Hands-on Demonstration

• AI Studio Use Case:

  • Data preparation and processing for multi-class classification problems using provided datasets.

  • Importance of using correct data types and structures, with examples querying dataset variables (e.g., Age, Blood Pressure).

  • Direct instructions on preprocessing data for machine learning (imputations, removals).

Metric Evaluation

• Precision and Recall:

  • Formulas clarified with practical examples drawn from the neural network performance:

    • Recall = True Positives / (True Positives + False Negatives).

    • Precision = True Positives / (True Positives + False Positives).

  • Understanding confusion matrix dimensions expand with the increase in classified variables.

Final Thoughts and Conclusions

• Introductory recap on required programming skills for upcoming projects; emphasis on collaborative tools like ChatGPT for resource gathering and problem-solving.

• Open forum for clarifications and student inquiries about the material or upcoming tasks.

Key Takeaways

• Understanding neural networks’ foundational concepts is critical for upcoming projects.

• Solidify grasp of relevant terminologies, including gradient descent and backpropagation, and computation of cost functions.

• Importance of correct data preparation, error metrics, and group collaboration in machine learning projects was reiterated.

Homework/Projects

• Projects require the application of neural network construction and data manipulation in AI Studio leading to practical implementation of discussed techniques. Ensure understanding of metrics utilized for evaluating machine learning model performance.

• Next Steps: Follow through with team projects, verify data accuracy, and engage actively in learning new operators as the semester progresses.