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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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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