The nice thing about ChatGPT and similar systems is that the complexity of AI/ML functionality is hidden behind a friendly natural language interface. This makes it easily reachable to the masses. But behind this easy to use facade is a lot of advanced functionality that involve a sequence of data processing steps called a pipeline. An AI-powered business card reader, for example, would first detect text and then recognize the individual letters within the context of the words they belong to. A license plate reader would be similar. Detection is an important process that you often need in your AI/ML projects. And that’s why we will be looking at YOLO.
Read MoreThe basic artificial neural network we created doesn’t have the best accuracy. Though it performed somewhat well in the recognition of MNIST test data images, it didn’t do as well in the recognition of real-world images. To improve its performance, we can do some adjustments to our model and training data.
Read MoreWe have previously through the process of recognizing numbers utilizing our artificial neural network (ANN). If you haven’t gone through that post on, you can do so now. However, we ran the recognition task on images from the MNIST dataset. Even though we used the test data, it’s still cannot be considered real-world. It’s clean, well-labeled, and structured, with a lot of the noise and ambiguity removed.
Read MoreWe have previously created and trained a basic artificial neural network (ANN). If you haven’t gone through that post on, you can do so now. In this post, we’ll continue and go through the process of recognizing numbers utilizing the ANN model that we created.
Read MoreArtificial Neural Networks (ANN) can be complex but it has become much easier to implement, thanks to frameworks and libraries, the past few years. In this post, we’ll walk through the process of creating a basic ANN. We’ll be using Python, TensorFlow, and Keras to create an ANN for recognizing handwritten digits. This is kind of the “Hello World” of AI.
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