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Updated in [August 31st, 2023]

Skills and Knowledge:
- Understanding of when and how to use deep learning
- Knowledge of foundational algorithms such as gradient descent and backpropagation
- Understanding of neural network design and the impact of design decisions
- Knowledge of training techniques such as early stopping, dropout, regularization, and momentum
- Ability to implement deep learning algorithms using PyTorch code

Professional Growth:
This course provides a comprehensive introduction to deep learning, which is a rapidly growing field of artificial intelligence. It covers the fundamentals of deep learning, such as gradient descent and backpropagation, as well as more advanced topics such as neural network design and optimization techniques. By taking this course, professionals will gain a better understanding of deep learning and how to apply it to their own projects. This course will also help professionals stay up-to-date with the latest developments in deep learning, as well as provide them with the skills to develop their own deep learning models.

Further Education:
This course titled "Introduction to Deep Learning" seems suitable for preparing for further education. It covers various aspects of deep learning, including when to use it, foundational algorithms, design constructs of neural networks, and optimization techniques. The course also includes practical implementations using PyTorch code, which helps reinforce both the theory and practice of deep learning.

Course Syllabus

Deep Learning

Minimizing the Error Function with Gradient Descent

Introduction to Neural Networks

Training Neural Networks

Course Project: Developing a Handwritten Digits Classifier with PyTorch