Applied Deep Learning: Computer Vision

An exciting, hands-on introduction to neural networks and image data. This workshop makes the complex topic of deep learning accessible and fun, culminating in a functional image recognition model.

Level

Intermediate

For

Grades 6-12

Duration

1 or 3 Days

What You Will Master

Images as Data

Understand how images are represented as numerical data (tensors) that computers can process.

Neural Network Fundamentals

Learn about layers, neurons, activation functions, and the process of model training (backpropagation).

Convolutional Neural Networks (CNNs)

Grasp the core architecture for image tasks and why it's so effective for visual data.

TensorFlow & Keras

Build and train models using industry-standard deep learning frameworks used at Google and beyond.

The Capstone Project

Handwritten Digit Recognition (MNIST)

The "hello world" of deep learning. Students will build, train, and evaluate a neural network to recognize handwritten digits from the famous MNIST dataset. This project provides a tangible and motivating visual learning experience and a solid foundation for more advanced computer vision tasks.

Key Transformation

Grasp the fundamental mechanics of neural networks and build a first image recognition model from scratch, demystifying the "magic" of AI and creating a foundational portfolio project.

Course Syllabus

1
Session 1: Introduction to Neural Networks

Build a conceptual understanding of how neural networks learn from data, inspired by the human brain. Understand concepts like weights, biases, and loss functions in an interactive way.

2
Session 2: Building a Simple Network with Keras

Code your first neural network to understand the mechanics of layers (Dense), data flow, and the training loop (`model.fit`). Visualize the model's architecture.

3
Session 3: The Power of Convolutions (CNNs)

Explore why CNNs are uniquely suited for image data by learning about filters and feature maps. Build a convolutional model for superior performance on the MNIST dataset.

4
Session 4: Training, Validation, and Improvement

Learn the complete workflow for training a deep learning model, including techniques like using a validation set to prevent overfitting and improve accuracy. Visualize training history.

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