Code Emporium

One of the most important ideas in computer vision came from biology: Receptive fields. In the retina, some ganglion cells respond only to specific patterns of light in a small region of visual space. For example, an on-center, off-surround cell is excited when light falls on the center region, but inhibited when light falls on the surrounding region. An off-center, on-surround cell does the opposite. This means the retina is not simply passing raw light information to the brain. It is already doing computation. It is detecting contrast. It is emphasizing changes. It is transforming visual input into a more useful signal. That idea matters deeply for computer vision. Because neural networks also learn to respond to specific patterns in local regions of an image. Early layers may respond to edges, corners, textures, or contrast patterns. Later layers combine those local responses into more complex representations. So before CNNs had receptive fields, biology had receptive fields. And studying the retina gives us a beautiful starting point for understanding why local feature detection matters. 🔗 I explain this idea in more detail in this video: https://lnkd.in/gzmbqGzN Happy Learning! ↓ Check out our Youtube channel to understand AI with 155,000 other learners

Studying human vision enhances our understanding of neural networks, particularly through the lens of the visual pathway. ➡️ Light enters the eye and is converted into electrical signals by the retina. ➡️ These signals travel through the optic nerve and reach the visual cortex, where the brain interprets what we see. This process is not merely passive image capture; it functions as a processing pipeline. This concept aligns closely with computer vision. A neural network also takes raw input and transforms it step by step. ➡️ Pixels serve as the starting point, not the final representation. ➡️ As the signal progresses through the network, the model learns increasingly useful features for the task, such as edges, textures, shapes, parts, objects, and meaning. Understanding the visual pathway has deepened my appreciation for the importance of representation learning. The true magic lies not just in the input but in the transformations applied to that input. I delve into this idea further in my video on the visual pathway: https://lnkd.in/gsNnxAHA, part of my free computer vision playlist on Code Emporium. Happy learning! Check out our YouTube channel to join 155,000 other learners in understanding AI.

10 years ago today, I started my YouTube channel, Code Emporium, with one simple goal: To make machine learning easier to understand — and more exciting to learn. Since then, the channel has grown to: 📌 370 videos 📌 156K subscribers 📌 9.4M+ views 📌 Courses, playlists, and deep dives across AI, ML, and deep learning But more than the numbers, what matters most is this: There are still so many curious minds out there who would enjoy learning these topics deeply. So I’d really appreciate your help. If you know someone interested in machine learning, AI, deep learning, or just understanding how modern technology works, please share Code Emporium with them. You can also repost this to help the channel reach more learners. My mission has always been to spread knowledge and help people become more curious. And there is a lot more to come. Click "Visit Website" to go to the channel! Happy Learning! #MachineLearning #ArtificialIntelligence #DeepLearning #AI #YouTube #Learning #Education #genAI #computervision #neuralnetworks

The brain does not receive an image; it receives signals. This distinction is crucial. When light enters the eye, it ultimately reaches the retina, which does not send a photograph to the brain. Instead, it converts light into electrical impulses. These impulses travel through the visual pathway before reaching the visual cortex. Human vision is not simply: Light ➡️ Image It is more accurately represented as: Light ➡️ Signal ➡️ Pathway ➡️ Processing ➡️ Perception This concept is powerful for understanding neural networks. Similarly, a computer vision model does not “understand” an image in one step. It processes information through layers, where each layer transforms the input into something more useful. Raw pixels become low-level features, low-level features become shapes, shapes become object parts, and object parts become concepts. This is why the visual pathway serves as a helpful analogy. Both biological vision and machine vision rely on progressive transformation. The input is important, but the pathway is just as significant. For a deeper dive into this topic, check out my video on the visual pathway: https://lnkd.in/gsNnxAHA. This is part of my free computer vision playlist on Code Emporium. Happy learning!

Vision does not occur solely in the eye. This insight transformed my understanding of computer vision. The eye captures light, but once it reaches the retina, the information must travel through a visual pathway before the brain can interpret it. In essence, seeing is not just about capturing images; it involves signal processing. 👁️The retina converts light into electrical signals. ⚡ These signals travel through the optic nerve. 〰️ They pass through intermediate processing regions. 🧠 Finally, they reach the visual cortex, where the brain constructs a more nuanced understanding of the scene. This perspective is also applicable to computer vision. A neural network does not comprehend an image from raw pixels alone; it processes information through layers. ➡️ Each layer transforms the signal. ➡️ Simple patterns evolve into complex patterns. ➡️ Edges become shapes. ➡️ Shapes become objects. ➡️ Objects culminate in meaning. I find the connection between biological vision and machine vision particularly intriguing: both depend on pathways that convert raw input into meaningful representations. For a deeper dive into the visual pathway, check out my video: https://lnkd.in/gsNnxAHA. This is part of my free computer vision playlist on Code Emporium. Happy learning! ↓ Check out our Youtube channel to understand AI with 155,000 other learners!

The eye does not send a raw image to the brain, which is crucial for understanding computer vision. When light enters the eye, it passes through structures like the cornea, lens, and retina. The key step is that the retina converts light into electrical signals. Therefore, the brain is not directly receiving “pixels,” but rather a transformed signal. This serves as a useful analogy for neural networks. A computer vision model begins with raw input: pixel values. As the image progresses through the model, those pixels are transformed into more useful representations. Early layers may detect simple patterns like edges, while later layers may identify textures, shapes, object parts, and eventually higher-level concepts. Both biological vision and machine vision involve transformation. Input alone is not sufficient; the system must convert input into representation. This connection is fascinating: human vision helps us understand why neural networks require layers, features, and representations. 🔗 For a deeper dive, I explain the structure of the eye in this video: https://lnkd.in/g6QErF7P. This is part of my free computer vision playlist on Code Emporium. Happy learning! ↓ Check out our Youtube channel to understand AI with 155,000 other learners!

Most people perceive the eye as a camera, but it serves as a complex biological vision system with various components, each playing a role in visual processing: ➡️ The cornea bends incoming light. ➡️ The lens focuses that light. ➡️ The iris regulates the amount of light entering. ➡️ The retina converts light into electrical signals. ➡️ The optic nerve transmits those signals to the brain. This understanding provides a foundation for exploring computer vision. It goes beyond merely capturing pixels; it involves transforming visual input into meaningful information, akin to how the eye functions. Just as light enters the eye and is processed by the brain, an image enters a neural network, where deeper layers create representations. This relationship underscores how insights from human vision can enhance modern computer vision. 🔗 For a more detailed explanation, I discuss this diagram in my video on the structure of the eye: https://lnkd.in/gxrz_yU3. This video is part of my free computer vision playlist on Code Emporium. Happy learning! ↓ Check out our Youtube channel to understand AI with 155,000 other learners!

Before diving into computer vision, it's essential to grasp the fundamentals of human vision. My journey began with understanding the structure of the eye, which is much more than just a camera. The eye functions as a complex biological system that: ⚡ Takes in light 🔍 Focuses it 〰️ Converts it into signals 🧠 Sends those signals to the brain Each component plays a crucial role: ▶️ The cornea and lens focus light. ▶️ The iris regulates the amount of light entering. ▶️ The retina transforms light into electrical signals. ▶️ The optic nerve transmits those signals to the brain. This understanding is vital because modern computer vision also involves transforming raw visual input into meaningful representations. Pixels alone are insufficient; a model must learn about structure, including edges, shapes, textures, objects, and their meanings. Biology serves as a powerful foundation for comprehending AI vision systems. In my latest video, I explain the structure of the human eye and its significance in the context of computer vision. 🔗 Watch here: https://lnkd.in/gxrz_yU3 This video is part of my free computer vision playlist on Code Emporium, where I explore the journey from human vision to modern neural networks and generative models. Happy learning! ↓ Check out our Youtube channel to understand AI with 155,000 other learners

For over a year, I have been exploring computer vision from first principles, delving into the fundamental question: How does vision itself work? My journey began with the structure of the human eye and led me through several key topics, including: - How the retina processes light - How the brain builds visual representations - The significance of convolution in vision - How neural networks learn image features - The impact of transformers on computer vision - How generative models like VAEs, DALL·E, and diffusion models create images I have compiled this learning experience into a free playlist of 30 videos, complete with slides, on my YouTube channel, Code Emporium. The aim is to assist curious learners in understanding modern computer vision by starting from biology and progressing to today’s AI systems. Here is the playlist: https://lnkd.in/g6dgEh_d I will continue to update it as I learn and create more content. If you find it useful, please consider liking the videos to help them reach a wider audience. Happy Learning! ↓ Check out our Youtube channel to understand AI with 155,000 other learners:**https://lnkd.in/gAD7KgKY**

For over a year now, I’ve been studying computer vision and its interplay with neural networks. My journey started with the structure of the human eye and eventually took me through many of the generative models used today. I wanted to share that learning journey with others. So over the last year, I’ve been putting together a free playlist of 30 videos, along with slides, to help explain how neural networks are used in computer vision — starting from our own biology and building up to modern AI systems. You can find the playlist here: https://lnkd.in/g6dgEh_d I’ll continue updating it as I learn and create more. If you find the videos useful, please consider liking them so they can reach more curious learners. Happy Learning! ↓ Check out our Youtube channel to understand AI with 155,000 other learners:**https://lnkd.in/gAD7KgKY**