Dig into the code a little deeper and you’ll see how tabular_learner constructs the TabularModel
def tabular_learner(data:DataBunch, layers:Collection[int], emb_szs:Dict[str,int]=None, metrics=None,
ps:Collection[float]=None, emb_drop:float=0., y_range:OptRange=None, use_bn:bool=True, **kwargs):
"Get a `Learner` using `data`, with `metrics`, including a `TabularModel` created using the remaining params."
emb_szs = data.get_emb_szs(ifnone(emb_szs, {}))
model = TabularModel(emb_szs, len(data.cont_names), out_sz=data.c, layers=layers, ps=ps, emb_drop=emb_drop,
y_range=y_range, use_bn=use_bn)
return Learner(data, model, metrics=metrics, **kwargs)
How do we deal with binary variables which do not have NA? Do we consider them as categorical variable of cardinality 2 and stick it into an embedding matrix or do we consider them as a real value as part of continuous variables?
In resnet the first kernel applied to the image inpit layer is (7,7) in conv2d.
In lesson 6 Jeremy explained that kernels are typically as deep as there are channels to learn from the relationship between colours. I.e For 3 channel images the kernel size could be (3,3,3) or staying with the above (3,7,7).
Does resnet actually use a (3,7,7) kernel or a (7,7) kernel for each of the layers respectively?
@Taka, following on from this point, at pymetrics we developed this python library called audit-AI. The purpose of it is to measure and mitigate the effects of discriminatory patterns in training data and the predictions made by machine learning algorithms trained for the purposes of socially sensitive decision processes.
We originally built this tool for internal use but the fundamental notion is so important (as Jeremy discussed in the ethics topic) that we decided to open-source the package to promote awareness and help others create models responsibly.
Thanks Rachel and Jeremy for highlighting these very important issues as we venture out onto the bleeding edge of data science.
One thing I’ve pondered is how we might address the issue of “fake news” that, at this point in time, continues to perpetuate and feedback loop as a result of the algorithms on Facebook and YouTube.
Off the top of my head, I wonder if this is a necessary use case for blockchains, i.e., cryptographic identity. It seems to me that otherwise literate people are not trained to spot the difference between algorithmically-generated news and information from humans, and the entire point of such news is to defeat that level of discrimination anyway. It would seem to me that we would need to move towards cryptographically-verified identity and propagate some form of news-flagging that verifies information sources as either definitely a person or possibly a bot. So far as I understand it, companies like Facebook and YouTube are not yet taking responsibility for the effects of their algorithms, in which case this seems like a possible solution.
Aside from this, and at the individual or small group level, I wonder if it’s prudent to create some sort of license that dictates that users of our data or source code must include disclaimers or links to information about ethics issues in data science. I’m not sure how effective this would be without some prosecutorial teeth, but there’s that.
Just leaving thoughts here as I haven’t found a dedicated category for this topic. Open to discussion and possibly separating this topic into its own category.
Having trouble with pip install isoweek. It’s saying my requirement is already satisfied in both the terminal and the notebook, but when I attempt to import it it’s saying there isn’t a module named isoweek:
ModuleNotFoundError: No module named ‘isoweek’
Any ideas? I’ve tried restarting the kernel and even shutting it down.
Hi all,
I had a question about the batch_norm layer described in the lecture. Why is this layer only applied to the continuous variables and not the embeddings created from the categorical variables (this is essentially a continuous representation of these variables if I understand correctly)
Hii All,
I started working on mitosis detection in breast histopathology Image using MITOS dataset. How should I make progress? Any help will be appreciable.
Thanks Nick,
Yeah, I wrote a method using datetime that did the same thing.Other than that i didn’t change anything, and ended up with slightly better accuracy metric