Summary

Welcome back to the realm of artificial intelligence! We previously explored machine learning, where computers learn from data to make predictions. Today, we delve into a powerful subset of machine learning: Deep Learning.

Eligibility

You need to be a Dumfries and Galloway region resident at the age of 19 and above, the caveat to that is that you're a school leaver with no positive destination in mind.

Pre-Requisite

This course is designed for programmers with a basic understanding of machine learning concepts and Python libraries like NumPy and Pandas. 

Course Duration  

Two Days (1000 to 1430 hours GMT) with a 30 minutes break in both sessions. 

Course Objectives 

• Gain a foundational understanding of deep learning architectures and principles. 

• Explore popular deep learning algorithms like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). 

• Learn about essential deep learning frameworks like TensorFlow or PyTorch. 

• Implement simple deep learning models for image classification and text processing tasks. 

• Understand the challenges and considerations for training and optimizing deep learning models. 

Outline

Day 1

Introduction to Deep Learning (1 hour)

• What is deep learning?

• Artificial Neural Networks (ANNs) and their role in deep learning

• Advantages and limitations of deep learning compared to traditional machine learning

• Applications of deep learning in various domains (computer vision, natural language processing)

Deep Learning Architectures (1 hour)

• Understanding artificial neurons, activation functions, and loss functions

• Convolutional Neural Networks (CNNs) for image recognition

• Recurrent Neural Networks (RNNs) for sequential data like text

• Introduction to other architectures like Autoencoders and Generative Adversarial Networks (GANs)

Hands-on Lab 1: Introduction to a Deep Learning Framework (1 hour)

• Setting up a development environment with a deep learning framework (e.g., TensorFlow or PyTorch)

• Building and training a simple neural network for a classification task

• Visualizing the learning process and evaluating model performance

Day 2

Deep Learning for Image Classification with CNNs (1.5 hours)

• Understanding convolutional layers, pooling layers, and image pre-processing for CNNs

• Implementing a CNN architecture for image classification tasks

• Training the CNN model on a sample dataset and interpreting results

Deep Learning for Text Processing with RNNs (1.5 hours)

• Understanding Long Short-Term Memory (LSTM) networks and their applications in text

• Techniques for text pre-processing and sequence representation for RNNs

• Building an RNN model for text classification or sentiment analysis

Deep Learning Optimization and Regularization (1 hour)

• Exploring optimization algorithms like gradient descent and Adam for training deep learning models

• Addressing overfitting and underfitting issues with regularization techniques

• Hyperparameter tuning and best practices for training deep learning models

Course Wrap-up (30 min)

• Q&A and Discussion on Deep Learning concepts and challenges

• Introduction to advanced deep learning topics (e.g., transfer learning, attention mechanisms)

• Resources for further learning and exploration of deep learning

• Course Feedback

Note: This course emphasizes hands-on learning through labs that introduce participants to building basic deep learning models. Due to the time constraint, the course focuses on core concepts and practical implementation rather than in-depth theoretical details. The chosen deep learning framework can be adjusted based on instructor expertise and participant preferences.