CNN vs. FIR Filter: Visualizing Neural Network Weights for Signal Processing

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Skeptical of ‘Black Box’ AI? We prove that a 1D Convolutional Neural Network (CNN) is mathematically identical to an adaptive FIR filter. Includes Python code to train a CNN to reinvent the Matched Filter and visualize its frequency response.

Shrink to Win: Regularization’s Role in Model Success

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Overfitting? Shrink your model! Regularization prevents memorization, enabling true learning & robust success. Discover how. A central problem in machine learning is to develop algorithms that work well both on training data and on new inputs (test data). Most machine learning tasks can be generalized as the estimation of a function \(\hat{f}(X)\) that maps the … Read more

Model Distillation Explained: How DeepSeek Leverages the Technique for AI Success

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Model distillation, also known as knowledge distillation, is a supervised learning technique that condenses the capabilities and thought processes of a large, pre-trained “teacher” model into a smaller “student” model. This allows the student model to achieve comparable performance to the teacher model, but at a lower cost and with faster performance. Chinese AI lab … Read more

Convolutional Codes – sliding window and shift register

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Key focus : Convolutional codes: Error-correcting codes effective in noisy channels and burst error correction. Know about sliding window & shift registers and how they are used in a convolutional encoder. Linear block codes Linear block codes, like Hamming codes, are mainly employed in hard decision decoding, utilizing the inherent algebraic structure of the code … Read more

Basic operations on signal sequences – Addition

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Key focus: How to implement the basic addition operation on two discrete time signal sequences. Python code for signal addition is provided. Signal addition Given two discrete-time sequences \(x_1[n]\) and \(x_2[n]\), the addition of these two sequences is represented as \(x_1[n]+ x_2[n]\). The start of the sample at \(n=0\) can be different for these two … Read more