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I'm currently learning machine learning.i get confused what is Model weights term. please explain to me what is model weight really means
Weights are the numbers you use to turn your samples into a prediction. In many (most?) cases this is what you are learning with your system. For example, suppose you want to predict house price using only the house size (x). You might use a simple linear regression model that tries to fit a line to the data. The formula you will use is the formula for a line:
y = w * x + b
Here x is given (the house size) and you use w and b to predict y the price. In this case w and b are your weights. The goal is to determine which w and b give the best fit to the data.
In more complex models like neural networks (or even more complicated linear regression) you may have dramatically more weights in you model, but the basic idea of finding those weights that best fit the data is the same.
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Do you have any code example or paper that refers to something like the following diagram?
I want to know why we want to stack multiple resnet blocks as opposed to multiple convolutional block as in more traditional architectures? Any code sample or referring to one will be really helpful.
Also, how can I transfer that to something like the following that can contain self-attention module for each resnet block?
Applying self-attention to the outputs of Resnet blocks at the very high resolution of the input image may lead to memory issues: The memory requirements of self-attention blocks grow quadratically with the input size (=resolution). This is why in, e.g., Xiaolong Wang, Ross Girshick, Abhinav Gupta, Kaiming He Non-Local Neural Networks (CVPR 2018) they introduced self-attention only at a very deep layer of the architecture, once the feature map was substantially sub-sampled.
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my question is:
for example: i have a trained FCC and i want to implement it on Hardware(ASIC). i want to ask how to utilize weights and biases from trained model in verilog ?
Should i make RAM and then store the values in it, or is any other way to be used?
I need this values(weights and biases) to propagate them to MAC units.
The weights and biases need to be converted into specific number format (say Fixed Point) and then stored in RAM.
Then the values should be fetched and given to the MAC units.
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I am curious about how many ways can we normalize data in data processing step before we use it to train machine learning model, deep learning model and so on.
All I know is
Z-score normalization = (data - mean)/variance.
Min-Max normalization = (data - min)/(max - min)
Do we have other ways except these two that I know?
There are many ways to normalize the data prior to training a model, some depends on the task, data type (tabular, image, signals) and data distribution. You can find the most important ones in scikit-learn preprocessing subpackage:
To highlight few that I have been using consistently, Box-Cox or Yeo-Johnson transformation, where it is used when your feature's distribution is skewed. This will minimize the skewness through maximum likelihood.
Another normalization technique is called Robust Scaler that is can perform better than the Z-score normalization if your dataset contains many outliers as they can falsely influence the sample mean and variance.
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I have executed a program with image classification and it was running good .I was running the code with conv1D and conv2D . I am getting accuracy of 0.854 for both conv1D and conv2D.
Can i know the exact differences between these two things in detail?
Conv1d is a convolution filter of 1 dimension (imagine it like a one dimension array). Conv2d is a filter with 2 dimensions (like a 2d array) and it is more suitable for data like images where it can retain more spatial information in a data point because it is applied to all the neighbors. You can see what is a kernel to understand why this is better for data like images. For non image data I guess it will not have significant impact whether you use 1d or 2d convolution arrays.
Note: Also this site is for programming problems, maybe you should ask your question in Data Science
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Often hear about curve modeled using spline. What's the advantage of using spline?
Spline data consists of control points and weights that are related to each other (a point on a spline depends on the coordinates and weights several neighboring control points). Curve data would either be a large set of closely spaced points to approximate the curve (expensive to store, where spline data is sparse), or an equation which might take a lot of horsepower to solve for y from a given x. Splines can be cheaply computed and subdivided/interpolated to achieve the desired precision but a curve of explicit points loses precision without having weight information. Splines are also really useful in vector art (think Flash or Adobe Illustrator) and 3D graphics because you can intuitively drag a few control points around to get exactly the curve you want instead of having to move a ton of individual curve points.