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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 ...

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

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

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

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

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

The wavelet transform is a mathematical tool that breaks signals into different frequency components, analyzing each with a ...

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

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

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

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

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

Lecture 07: Continuous-Time Fourier Series Topics covered: Response of continuous-time LTI systems to complex exponentials: ...

In this video, we take a look the Mask R-CNN network. What is it? How is it trained? Code for inference! ABOUT ME ⭕ Subscribe: ...

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