11–20 of 23 posts
Just a shout-out to Professor Polo (the Professor who is the leader of the Polo Club of Data Science who wrote the tutorial on CNN's), 3 years ago, as a CRUD code monkey, I found out about Georgia Tech's launching of Online Masters in Data Science on Hacker News and then entered into the program and took Dr.Polo's class on Data Visualization and Analysis (for that semester I lit. spent more time on the class than on…
Just a shout-out to Professor Polo (the Professor who is the leader of the Polo Club of Data Science who wrote the tutorial on CNN's), 3 years ago, as a CRUD code monkey, I found out about Georgia Tech's launching of Online Masters in Data Science on Hacker News and then entered into the program and took Dr.Polo's class on Data Visualization and Analysis (for that semester I lit. spent more time on the class than on…
Very nice interactive tutorial tool. I wish some of the terms were defined. Hyperparameter? Convolution? Kernel?
Model: the learnt relationship (eg., f(x; a,b) = ax + b) Parameter: an aspect of the model, a dial which is fixed by data (eg., a) Kernel (as used here): a subset of such parameters Algorithm: procedure which accepts data and produces a model Hyperparameter: an aspect of the algorithm, a dial which changes model production Convolution: A convolution of image A and Filter B describes to what degree A is "like" B. Here…
This seems a bit cryptic. The way I understand hyperparamters, they define how a model learns, i.e. you can set an alpha in gradient descent. Now when you compare them to "ordinary" parameters, hyperparamters do not define relationship between data and output.
Very nice interactive tutorial tool. I wish some of the terms were defined. Hyperparameter? Convolution? Kernel?
Model: the learnt relationship (eg., f(x; a,b) = ax + b) Parameter: an aspect of the model, a dial which is fixed by data (eg., a) Kernel (as used here): a subset of such parameters Algorithm: procedure which accepts data and produces a model Hyperparameter: an aspect of the algorithm, a dial which changes model production Convolution: A convolution of image A and Filter B describes to what degree A is "like" B. Here…
And you misunderstood my suggestion for the article as a request for your help. But thanks. I don’t doubt what you wrote is accurate and helpful in the same way that saying “a transom is a part of a building” is accurate and helpful.
Just a shout-out to Professor Polo (the Professor who is the leader of the Polo Club of Data Science who wrote the tutorial on CNN's), 3 years ago, as a CRUD code monkey, I found out about Georgia Tech's launching of Online Masters in Data Science on Hacker News and then entered into the program and took Dr.Polo's class on Data Visualization and Analysis (for that semester I lit. spent more time on the class than on…
3 years is a long time for a degree. Should have done Udacity.
Earlier quoted context omitted.
Model: the learnt relationship (eg., f(x; a,b) = ax + b) Parameter: an aspect of the model, a dial which is fixed by data (eg., a) Kernel (as used here): a subset of such parameters Algorithm: procedure which accepts data and produces a model Hyperparameter: an aspect of the algorithm, a dial which changes model production Convolution: A convolution of image A and Filter B describes to what degree A is "like" B. Here…
These definitions are too vague by half. In a word, useless. “An aspect of the algorithm, a dial” so the same as a parameter then, according to your definition... the only distinction is it changes model production, but in my experience data does that too, so... no clarity here. You make them sound the same to the naive reader. And you misunderstood my suggestion for the article as a request for your help. But thanks…
thanks for sharing this, great content. made me think about old projects I have laying around.
Why doesn't it explain what the "bias" is?
Here are examples of equivalent notations that may make it more familiar:
y = 2⋅x + b or
y = a⋅x + b
- a/2 would be the weight, often named 'w'.
- b would be the bias, often named 'b'.
- y is the output.
In neural network documentation this is often written as (often case sensitive, which may be confusing): output = w⋅x + b (output = weight * input + bias).
I hope that explains it.
Very nice interactive tutorial tool. I wish some of the terms were defined. Hyperparameter? Convolution? Kernel?
Hyperparameters in ML is the tuning parameters on the shape and structure of the model, such as the number of features in linear regression above, the number of layers in a NN or number of neurons in each layer. I think basically any tuning parameters besides theta can be considered hyperparameters. The difference is the theta parameters are learned while the hyperparameters are decided by human. But you can also run experiments on different tuning parameters and compare the outcomes so in a sense hyperparameters can be learned.
Convolution, well, the article is trying to explain it. It's like rolling up a portion of an image using a filter. E.g. Making an image blur by pixelizing it. The main purpose is the find out high level feature of the image. E.g. Put a filter on to find the edge of an object in the image.
Kernel is a small NxN matrix (3x3, 4x4, 16x16, etc) used as filter to convert the pixels in an image to high level feature. E.g. the mean-color-kernel takes 4x4 pixels and computes the average of their colors. Now apply the mean-color-kernel over all the 4x4 blocks of an image and you got one convolution.
Very nice interactive tutorial tool. I wish some of the terms were defined. Hyperparameter? Convolution? Kernel?