In today’s article, we are going to investigate what Convolutional Neural Networks (CNNs) learn during the object classification task. Visualizing CNN’s features would allow us to see what from CNN’s point of view makes thing a thing. By the end of this article, you will be able to visualize hierarchical features reflecting how CNNs ‘understand’ images.
In other words - if you are curious about what’s in the below image, keep reading!
In today’s article, we are going to visualize gradient weighted class activations. It may sound confusing at first, but at the end of this article, you will be able to ‘ask’ Convolutional Neural Networks (CNNs) for visual explanations of their predictions. In other words, you will be able to highlight image regions responsible for predicting a given class.
This is the second part of the CNN Explainer series. If you haven’t checked the first part yet, feel free to do it now.
Let’s consider the following input image:
In today’s article, we are going to start a series of articles that aim to demystify the results of Convolutional Neural Networks (CNNs). CNNs are very successful in solving many Computer Vision tasks, but as they are Neural Networks after all, they may fall into the category of ‘black box’ systems, that don’t provide explanations of their predictions out of the box.
However, in this project, we are going explain their behaviors by visualizing learned weights, activation maps, and occlusion tests. …
In today’s article, we are going to improve Jetson’s sensing and perception abilities with Computer Vision and Machine Learning techniques. It would allow our toy car to learn how to handle new cases going far beyond the simple path following. By the end of this article, you’ll see how a self-driving toy car can learn how to take correct turns on crossroads, follow the driveable path, and stop when the road ends.
In today’s article, we are going to begin a self-driving toy car series. The first part of the series will cover the car assembly and basic AI autopilot motion. We are going to start building an end-to-end vision self-driving system which will leverage Robotics, Computer Vision, and Machine Learning. By the end of this article, you will be able to assemble a self-driving toy car, make it learn how to drive, and finally let it operate fully autonomously like in the below video!
In today’s article, we are going to perform Multi-Label Image Classification using Convolutional Neural Networks. Machine Learning model trained with such an approach, would be able to generate multiple descriptive labels for a given input image. Such an application could be used to generate hashtags for social media posts, like in the below example (iOS App).
In today’s article, we are going to explore the basics of low-level GPU computations used for rendering graphics. In order to do so, we are going to dive into the world of GPU Shaders and perform some image manipulations like contrast, brightness, blur, and pixellation. By the end of this article, you will be able to perform basic pixel operations using top graphics libraries like OpenGL and Metal (iOS, macOS).
In today’s article, we are going to use basic Computer Vision techniques to approach the street lanes detection problem which is crucial for self-driving cars. By the end of this article, you will be able to perform real-time lane detection with Python and OpenCV.
In today’s article, we are going to learn how to write programs that write programs. The notion of programs that can generate other programs is called metaprogramming and by the end of this article, you will be able to create your own code-generating system.
Take a look at the following example of a self-generated program that prints ‘HI’ to the console.
If you are confused by the above code, don’t worry - you are not alone. It’s written in an esoteric, though Turing complete programming language called Brainf*ck which is notorious for i’s unreadability. …
In today’s article, we are going to build a prediction system with simple statistical methods. We are going to show that Machine Learning applications aren’t limited to neural networks only and we can achieve a decent predictive behavior with Computational Statistics. By the end of this article, you will be able to implement a very basic, yet impressive prediction system.