Applications of Deep Neural Networks PyTorch Course Overview (1.1, Spring 2024)
Welcome to the course overview for Applications of Deep Neural Networks PyTorch (1.1, Spring 2024)! In this course, students will learn how to apply deep neural networks using the PyTorch framework to solve real-world problems.
Course Objectives:
- Understand the fundamentals of deep learning and neural networks
- Learn how to implement deep learning models using PyTorch
- Explore various applications of deep neural networks, such as image classification, natural language processing, and reinforcement learning
- Gain hands-on experience through practical labs and projects
Course Syllabus:
| Week | Topics |
|---|---|
| 1 | Introduction to deep learning and neural networks |
| 2-4 | PyTorch basics and building neural networks |
| 5-7 | Image classification with convolutional neural networks |
| 8-10 | Natural language processing with recurrent neural networks |
| 11-13 | Reinforcement learning with deep Q-networks |
Prerequisites:
This course is designed for students with some prior knowledge of machine learning and Python programming. Familiarity with basic concepts such as supervised learning and gradient descent is recommended.
Enrollment:
To enroll in Applications of Deep Neural Networks PyTorch (1.1, Spring 2024), please visit our enrollment page or contact our registration office at 555-1234.
Instructors:
Our team of experienced instructors includes experts in deep learning and PyTorch. They are dedicated to providing a stimulating and engaging learning experience for all students.
Can we take this class remotely
Hmm just noticed my camera focused on my books, not me.
Thanks, Jeff.
Thank you Prof Jeff.
Thank you Jeff.