Overview of the T81_558 Deep Learning Project
The T81_558 deep learning course offered by Washington University in St. Louis is a comprehensive exploration into the world of neural networks and their applications. Taught by Jeff Heaton, a prominent figure in the field, the course is regularly updated to reflect the latest advancements in deep learning. This specific version focuses on teaching deep learning concepts using TensorFlow and Keras, but currently at the university, a PyTorch version is being offered.
Course Description
Deep learning is a rapidly advancing field within artificial intelligence, allowing machines to process and learn from vast amounts of data in a way that mimics the human brain. This course is designed to introduce students to various neural network architectures, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), General Adversarial Networks (GANs), and reinforcement learning frameworks. Students will learn how these networks can apply to tasks in computer vision, time series analysis, natural language processing (NLP), and more.
The course employs high-performance computing, demonstrating how deep learning models can be executed using graphics processing units (GPUs) and on computational grids. While the primary focus is on practical applications, the course also introduces the mathematical foundations of deep learning. Python is the primary programming language used, with Google TensorFlow and Keras as the deep learning frameworks.
Course Materials
All course content is freely available on GitHub, with a corresponding textbook titled "Applications of Deep Neural Networks with Keras." The materials cover fundamental concepts and provide a structured syllabus to guide students through the complex landscape of neural networks.
Learning Objectives
The course has three main objectives:
- To differentiate neural networks, both deep and conventional, from other machine learning models.
- To enable students to identify when a deep neural network is the appropriate tool for a given problem.
- To encourage practical application and comprehension of the subject matter through a final project.
Course Structure and Modules
The course is divided into multiple modules, each focusing on specific aspects of deep learning:
- Module 1 introduces Python programming and file handling fundamentals.
- Module 2 covers Python tools like Pandas for machine learning tasks.
- Module 3 delves into using TensorFlow and Keras for constructing neural networks.
- Module 4 focuses on handling and training tabular data.
- Module 5 addresses regularization techniques and overfitting reduction.
- Module 6 explores CNNs for image recognition.
- Module 7 introduces Generative Adversarial Networks (GANs).
- Module 8 involves practical experience with Kaggle, a competitive data science platform.
- Module 9 covers transfer learning techniques.
- Module 10 deals with time series data and the use of transformers.
- Module 11 teaches natural language processing using Hugging Face.
- Module 12 revolves around reinforcement learning and its applications.
- Module 13 focuses on deploying deep learning models and monitoring their performance.
Datasets
The course provides a collection of datasets that can be downloaded for project work and practical exercises. These datasets are essential for the hands-on approach the course adopts, encouraging students to apply theoretical knowledge in real-world scenarios.
Overall, the T81_558 deep learning course is a blend of theory, practical implementation, and the latest technology trends, making it a valuable educational experience for anyone looking to delve deeper into the realm of artificial intelligence and neural networks.