❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 06th, 2023]

Modern Deep Convolutional Neural Networks with PyTorch is a comprehensive course that covers the fundamentals of deep learning and convolutional neural networks. Learners will gain an understanding of the principles of deep learning and how to apply them to solve real-world problems. They will learn how to use PyTorch to build and train deep convolutional neural networks, and how to use regularization and normalization techniques to improve the performance of their models. They will also learn how to use transfer learning to quickly build powerful models. This course is ideal for anyone looking to gain a deeper understanding of deep learning and convolutional neural networks.

[Applications]
After completing this course, students should be able to apply the knowledge they have gained to create and train their own deep convolutional neural networks using PyTorch. They should be able to use the techniques of image processing, advanced deep learning techniques, regularization, and normalization to improve the accuracy of their models. Additionally, they should be able to use transfer learning to apply pre-trained models to their own datasets.

[Career Paths]
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use deep learning techniques such as convolutional neural networks and image processing to create models that can be used to solve complex problems. They also use regularization and normalization techniques to improve the accuracy of the models. With the increasing demand for machine learning engineers, this is a great career path for those interested in deep learning.

2. Data Scientist: Data Scientists use deep learning techniques to analyze large datasets and uncover patterns and insights. They use convolutional neural networks to identify patterns in images and use transfer learning to apply existing models to new datasets. With the increasing demand for data scientists, this is a great career path for those interested in deep learning.

3. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for developing and deploying AI systems. They use deep learning techniques such as convolutional neural networks and image processing to create AI systems that can be used to solve complex problems. They also use regularization and normalization techniques to improve the accuracy of the AI systems. With the increasing demand for AI engineers, this is a great career path for those interested in deep learning.

4. Computer Vision Engineer: Computer Vision Engineers are responsible for developing and deploying computer vision systems. They use deep learning techniques such as convolutional neural networks and image processing to create computer vision systems that can be used to solve complex problems. They also use regularization and normalization techniques to improve the accuracy of the computer vision systems. With the increasing demand for computer vision engineers, this is a great career path for those interested in deep learning.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path provides students with a comprehensive understanding of computer science fundamentals, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and deep learning. With the increasing demand for AI and machine learning, this degree path is becoming increasingly popular.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of AI and machine learning algorithms and techniques. It covers topics such as natural language processing, computer vision, robotics, and deep learning. This degree path is ideal for those who want to specialize in AI and machine learning.

3. Master of Science in Data Science: This degree path focuses on the analysis and interpretation of large datasets. It covers topics such as data mining, data visualization, and machine learning. This degree path is ideal for those who want to specialize in data science and analytics.

4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of advanced machine learning algorithms and techniques. It covers topics such as deep learning, reinforcement learning, and natural language processing. This degree path is ideal for those who want to specialize in machine learning and AI.

Course Syllabus

What is image

Motivation to Convolutions

Convolution operation

Parameters of the convolution

Non-linear function

Max Pooling and Average Pooling

Building deep convolutional network