The 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.
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We 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.
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We 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.
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Artificial 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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Artificial intelligence or AI is all the rage these days, driving amazing innovations across multiple industries and transforming how we interact with technology. Machine learning, a subset of AI, plays a crucial role in this transformation by enabling systems to learn from data and improve over time. Among the most powerful techniques in machine learning are Artificial Neural Networks (ANNs), which are modeled after the human brain and have become essential for solving complex problems in fields like image recognition, voice recognition, natural language processing, and predictive analytics. In this post, we will explore the basics of ANNs and how they work.
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