Enroll Course: https://www.udemy.com/course/advanced-computer-vision/
Have you ever marveled at the capabilities of AI like ChatGPT, GPT-4, DALL-E, Midjourney, or Stable Diffusion and wondered how they actually work? This Udemy course, ‘Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)’, offers a fascinating deep dive into the foundational concepts behind these revolutionary technologies.
This course is an exhilarating journey that showcases the rapid advancements in deep learning. While you might have some familiarity with Convolutional Neural Networks (CNNs), prepare to be impressed by how different this course is. It bridges the gap from basic CNN architectures to cutting-edge models like VGG, ResNet, and Inception. You’ll even apply these to real-world scenarios, such as creating a system that can outperform human experts in analyzing blood cell images – raising intriguing questions about the future of medicine with AI.
The curriculum goes beyond simple image classification. You’ll learn how to transform a CNN into an object detection system, enabling it to not only classify images but also precisely locate and label multiple objects within them. This skill is fundamental for applications like self-driving vehicles, which need to identify cars, pedestrians, and traffic signals in real-time. The course specifically highlights the state-of-the-art SSD algorithm for its superior speed and accuracy.
Another captivating topic covered is neural style transfer. Imagine taking the content of one image and applying the artistic style of another to create entirely new, visually stunning pieces in seconds – a feat that would take a human artist considerably longer.
Furthermore, you’ll be introduced to the renowned Generative Adversarial Networks (GANs). This section will demystify the technology behind neural networks that generate incredibly realistic, state-of-the-art images. The course also covers object localization as a crucial step towards building comprehensive object detection systems.
A major theme is the shift from focusing solely on the CNN architecture to building complex systems that utilize CNNs. The emphasis is on high-level building blocks, resulting in minimal math and avoiding complex low-level coding in frameworks like TensorFlow or PyTorch. The majority of the course utilizes Keras, simplifying much of the repetitive work.
True to the spirit of learning by doing, the instructor emphasizes understanding through implementation. As Richard Feynman famously said, “What I cannot create, I do not understand.” This course is unique in its commitment to teaching you how to implement machine learning algorithms from scratch, rather than just showing you how to plug data into libraries. You’ll gain a deeper understanding by building these concepts yourself, avoiding the trap of simply repeating the same few lines of code.
**Prerequisites:** A solid understanding of building and training CNNs with Python (preferably with libraries like Keras), basic knowledge of convolution and neural network theory, and decent Python coding skills, especially with NumPy, are recommended.
**Recommendation:** This course is highly recommended for anyone looking to advance their knowledge in computer vision and understand the inner workings of modern AI image generation and analysis tools. It offers a practical, implementation-focused approach that builds genuine understanding.
Enroll Course: https://www.udemy.com/course/advanced-computer-vision/