All Pods / Computer Science
Deep Learning for Beginners: Foundations of Neural Networks and the PyTorch Framework
Some knowledge of Python is recommended
Group size
2-6 students
Outcome
Hands-on experience with PyTorch and foundational skills to build, train, and optimize neural networks for real-world applications.
Tuition
$495
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Deep Learning for Beginners: Foundations of Neural Networks and the PyTorch Framework
Deep Learning has emerged as a transformative tool for modeling and predictive analytics, extending beyond traditional applications like Computer Vision and Natural Language Processing to impact nearly every domain. This introductory course emphasizes the practical application of Deep Learning, equipping students with the skills to build and train neural networks. Through a hands-on approach, participants will gain experience using the PyTorch framework, learning to harness the power of Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). This course is designed to provide a foundational understanding of machine learning, deep neural networks, and their optimization, preparing you for advanced study and practical application in various fields. This course it targeted towards complete beginners, and the only expectation is having some basic knowledge of Python.
Week by week curriculum
Week 1
Introduction to Machine Learning and Deep Learning: Understand the basics of Machine Learning (ML) and how Deep Learning fits into this broader context. We'll cover the fundamental differences, key terminology, and the evolution from traditional ML to deep neural networks.
Week 2
Foundations of Neural Networks - Part 1: Explore the architecture of simple neural networks, focusing on the structure of neurons, layers, and how they learn. Introduction to the MNIST dataset as a practical exercise.
Week 3
Foundations of Neural Networks - Part 2: Deep dive into linear models using the MNIST dataset. We'll discuss activation functions, loss functions, and the basics of training with gradient descent, all within the PyTorch framework.
Week 4
Convolutional Neural Networks (CNNs): Introduce CNNs for image data processing. Learn about convolutional layers, pooling, and how these layers help in feature extraction for tasks like image classification on MNIST or similar datasets.
Week 5
Recurrent Neural Networks (RNNs): Examine RNNs for handling sequential data. We'll cover the basics of RNNs, their applications, and how they manage time series or text data. We will then train an RNN-based text classification model.
Week 6
Broader Perspectives in Deep Learning: Reflect on the field's current state, discussing emerging trends, ethical considerations, and future directions in deep learning. This week will synthesize what we've learned, looking at applications beyond our examples and discussing the broader impact of deep learning technologies.