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Analog and digital communications (Hinglish) I Course URL: https://onlinecourses.nptel.ac.in/noc26_ee03/preview Prof.

In this video, we take a look at Feature Pyramid Networks (FPN). What is it? How does it work? Why they are so useful in computer ...

In this video, we walk through the back propagation learning procedure in a convolution neural network, step by step. ABOUT ME ...

Lecture 08: Convolution Description: In linear time-invariant systems, breaking an input signal into individual time-shifted unit ...

10 Time Series Description: This video covers the Poisson Process, spike train variability, convolution, cross-correlation, ...

Lecture 09: Fourier Transform Properties Topics covered: Linearity, symmetry, time shifting, differentiation and integration, time ...

Lecture 04: Convolution Topics covered: Representation of signals in terms of impulses; Convolution sum representation for ...

Analog and digital communications (Hinglish) I Course URL: https://onlinecourses.nptel.ac.in/noc26_ee03/preview Prof.

Lecture 05: Properties of Linear, Time-Invariant Systems Topics covered: Properties of linear, time-invariant systems, including the ...

Let's talk about Boltzmann Machines RESOURCES [1 ] Main paper: ...

In this video, we take a look at R-CNN (regions with convolution features). We see how training and inference occurs. ABOUT ME ...

Lecture 12: Filtering Topics covered: Relation to the convolution property of Fourier transform; Ideal and non ideal ...

Lecture 11: Discrete-Time Fourier Transform Topics covered: Representation of aperiodic signals; Discrete-time Fourier transform ...

Lecture 22: The z-Transform Topics covered: Relationship to the discrete-time Fourier transform; Region of convergence (ROC); ...

Lecture 20: The Laplace Transform Topics covered: Relationship to the Fourier transform; Class of rational transforms and the ...