Length: 6 Weeks
Type: Online and fully mentored with no weekly meetings
Start date: April 29, 2024
This course delves into the core principles of Deep Learning, providing students with both theoretical understanding and practical skills. It covers the fundamental concepts of neural networks, advanced techniques in training deep learning models, and applications in real-world scenarios. The course is designed for students with a basic understanding of machine learning and aims to impart expertise in deep learning.
Task 1: Introduction to Neural Networks and Deep Learning
Objective: Understand the basic principles of neural networks and their evolution into deep learning.
Construct and Train a Simple Neural Network:
- Students will construct a basic neural network using the Tensorflow deep learning framework.
- They will train this network on a simple dataset to understand the fundamentals of forward and backward propagation.
Analyze and Compare Models
- Students will analyze the performance of their neural network and compare it with a traditional machine learning model on the same dataset.
- They will comparatively assess the strengths and weaknesses of each approach in a written report.
Present Neural Network Concepts
- Each student will present a brief overview of how neural networks differ from traditional machine learning models.
- The presentation will focus on the structure, functioning, and basic principles of neural networks.
Assignment: Create a report comparing traditional machine learning algorithms with basic neural networks, highlighting their differences in approach, metrics and efficiency.
Task 2: Advanced Neural Network Architectures
Objective: Explore advanced neural network architectures like CNNs and RNNs
- Design and implement a Convolutional Neural Network (CNN) for image recognition.
- Develop a Recurrent Neural Network (RNN) for sequential data processing.
Assignment: Implement a CNN for a real-world image classification task and an RNN for a time-series prediction problem, followed by a detailed performance analysis of both models.
Task 3: Training Deep Learning Models
Objective: Master techniques for effectively training deep learning models, including optimization algorithms, regularization, and hyperparameter tuning.
- Apply different optimization techniques and regularization methods to enhance model performance.
- Conduct hyperparameter tuning to optimize a deep learning model.
Assignment: Optimize the performance of a deep learning model using various techniques and hyperparameters, and present the improvements in model performance with statistical evidence.
Task 4: Mastering Transformers in Deep Learning
Objective: Gain in-depth understanding and practical skills in implementing Transformer models, which have revolutionized fields like NLP and are increasingly being applied in other areas of deep learning.
Implement a Transformer Model:
- Students will implement a Transformer model from scratch using a deep learning framework.
- Focus on understanding the self-attention mechanism and how it differs from the architectures learned in previous units.
Fine-Tune a Pre-Trained Transformer:
Students will fine-tune a pre-trained Transformer model (such as BERT or GPT) on a specific task (e.g., text classification, question-answering).
This will help them understand the practical aspects of applying Transformers to real-world problems.
Experiment and Analyze Transformer Models:
- Conduct experiments by varying the architecture of the Transformer model and its hyperparameters.
- Analyze the impact of these variations on the model’s performance and efficiency.
Explore Advanced Applications:
- Explore advanced applications of Transformers beyond NLP, such as image recognition or time-series analysis.
- Understand the versatility of the Transformer architecture in various domains.
Transformer Model Project:
Leverage Transformer models, known for their effectiveness in handling sequential data, to predict stock market prices.
This project aims to demonstrate the application of Transformer architectures in a time-series forecasting scenario, specifically for predicting the future prices of stocks based on historical data.
The project should include:
Comprehensive documentation of the model architecture and the rationale behind design choices.
Detailed performance evaluation using relevant metrics, comparing the Transformer model with other architectures where applicable.
A reflection on the scalability, efficiency, and potential biases of the model.
Present the project in a format that combines a written report and a practical demonstration.
Dataset: S&P 500 Historical Stock Data
The S&P 500 historical stock data is an excellent dataset for this assignment. It includes the daily price movements (open, high, low, close, volume) of stocks listed in the S&P 500 index.
Task 5: Deep Learning in Practice
Objective: Explore practical applications of deep learning in fields such as Natural Language Processing (NLP), Computer Vision, and Autonomous Systems.
- Implement a deep learning solution for a real-world problem in NLP or Computer Vision.
- Analyze the ethical and societal implications of deploying deep learning models.
Assignment: Develop a deep learning-based project addressing a specific problem in NLP or Computer Vision, including a report on ethical considerations and potential societal impacts.
Capstone: Comprehensive Deep Learning Project
Identify a real-world problem that can be addressed using deep learning.
Develop a complete deep learning solution, from data preprocessing to model deployment.
Evaluate the model’s performance using appropriate metrics.
Address any ethical and societal concerns related to the project.
Prepare a comprehensive report and presentation detailing the project’s methodology, results, and impact.
This course ensures that students not only grasp the theoretical aspects of deep learning but also gain hands-on experience in applying these concepts to solve practical problems. The capstone project serves as a culmination of the skills and knowledge acquired throughout the course.