/books/machine-learning-in-elixir
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Latest Threads About This Book
@seanmor5
I am enjoying the book so far, but ran into an Elixir error on the last example in chapter 1:
In my last Livebook cell I have...
New
The model training loop on page 273 should visualize the model output after each epoch. Instead, an argument error is reported after the ...
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@seanmor5
On p. 41 it says:
For now, you’ll use standardization to scale your data…
then the following code is given
cols = ~w(sepa...
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Comments on chapter 13, version B 3.0
@seanmor5
One advice: build the phoenix from scratch, do not use the version from the example code...
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Comments on Chapter 12, Version B 3.0
@seanmor5
All the Axon.Optimizers.adam(…) should be replaced by Polaris.Optimizers.adam( learning_...
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@seanmor5
The KNN accuracy code has a small error. Please replace
Scholar.Metrics.confusion_matrix(test_targets, test_preds, num_classe...
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Comments to Chapter 11 version B 3.0
@seanmor5
The dependencies from chapter 11 gave errors on 10 march 2024.
I followed the advices...
New
Hello @seanmor5 !
On page 180 the link to the onnx model is outdated : https://github.com/onnx/models/blob/main/vision/classification/mo...
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@seanmor5
On page 177, the line |> Stream.chunk_every(batch_size, :discard) should be |> Stream.chunk_every(batch_size, batch_size...
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Hello @seanmor5
Maybe you missed my post about ch8
page 191:
github.com/onnx/tensorflow_onnx doesn’t exist (anymore).
page 193:
The ...
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Most Active This Week
@seanmor5
I am enjoying the book so far, but ran into an Elixir error on the last example in chapter 1:
In my last Livebook cell I have...
New
Most Active This Month
Comments to Chapter 11 version B 3.0
@seanmor5
The dependencies from chapter 11 gave errors on 10 march 2024.
I followed the advices...
New
The model training loop on page 273 should visualize the model output after each epoch. Instead, an argument error is reported after the ...
New
Most Active This Year
Author Spotlight: Sean Moriarity (@seanmor5)
Machine learning sounds both magical and difficult, but with the right tools and the rig...
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Leverage Elixir and the Nx ecosystem to build intelligent applications that solve real-world problems in computer vision, natural languag...
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Getting an error when installing the dependencies at the start of this chapter:
could not compile dependency :exla, "mix compile&qu...
New
@seanmor5
The mutate macro from the Explorer.DataFrame variable does not like variables:
It works without the variable:
New
Running the code snippet of Chapter 2 on page 44 of version B1.0, I get the following error:
Evaluation process terminated - an exceptio...
New
@seanmor5
I don’t know if there’s much you can do about it, but the SSL certificate for the Wine dataset from archive.ics.uci.edu has an...
New
@seanmor5
In chapter 1, the accuracy of the model is 0.9666666. On my laptop I only get an accuracy of 0.5333333. Is that a problem? ...
New
@seanmor5
For Explorer.Query to access variables defined outside of the query ^ must be used.
Updated in the example below:
cols = ~w(...
New
Hey @seanmor5,
I’m having great training accuracy but poor evaluation accuracy for the example in Chapter 1 when following the code in t...
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Title: Machine Learning in Elixir: Chapter 1
Example: Flower classification - final accuracy on the test data is inconsistent.
When com...
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@seanmor5
In book:
Actual
Diff-ish
Polars[150 x 5]
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@seanmor5
I want to invert the cat image. Therefore I downloaded the file in the same folder as the livemd file for chapter 03 I created...
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@seanmor5
Title: Machine Learning in Elixir (epub so pages different i think)
From book:
Current output:
Diff:
inputs: %{&qu...
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@seanmor5
the code provided I am running into an error when training the first model that uses transfer learning with ONNX model mobilen...
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Hi,
When running
tensor = Nx.random_uniform({1_000_000})
There’s a warning message warning: Nx.random_uniform/1 is deprecated. Use Nx....
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Most Active Last Three Years
Version: B1.0
On page 104 the code:
Scholar.Metrics.confusion_matrix(test_targets, test_preds)
throws an error: required :num_classes ...
New
Some minor nits in the beta version:
“but is often considered it’s own type of machine learning.” → should be “its”
“represents a d...
New
This didn’t seem right to me:
For instance, you can squeeze the values of a feature between 0 and 1 by dividing every individual featur...
New
@seanmor5
Per
“You can install Livebook locally, or run it in the cloud with Fly.”
Since we can run livebook on Huggingface (Liveboo...
New
Hey @seanmor5 ,
Similar to the errata on deprecation on page 44, on page 128:
Nx.random_uniform({})
|> NeuralNetwork.predict(w1, b1,...
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Page 226
Mix install uses older versins of exla, should 0.6 to work with cuda120
Page 230
targets pipeline in Data module misses |>...
New
Hey @seanmor5,
The function given in the book
defn profits(trees) do
trees
|> Nx.pow(4)
|> Nx.negate()
|> Nx.add(Nx.mu...
New
Version: B1.0
On page 100 (epub version) the code:
model = Scholar.Clustering.KMeans.fit(train_inputs, num_clusters: 3)
throws an er...
New
Title: Machine Learning in Elixir: Learning to Learn with Nx and Axon (6)
“You will sometimes see surprising due to precision issues.”
...
New
Machine Learning in Elixir: Scholar.Clustering.KMeans.fit/2 is undefined (B1.0, Chapter 5, Page 109)
Hey @seanmor5,
On page 109:
model = Scholar.Clustering.KMeans.fit(train_inputs, num_clusters: 3)
results in an UndefinedFunctionError....
New
Version: B1.0
TL;DR: In page 98 should say two instead of three tensors:
Where it says
Next, you create three random normal tensors: ...
New
@seanmor5
Title: Machine Learning in elixir: Some problems with chapter 7 in Version B2.0
On 3 january 2024 I downloaded the Cats & ...
New
Version: B1.0
On page 103 & 104 where the code example is:
Scholar.Linear.LogisticRegression.fit( train_inputs,
train_targets,
num_...
New
“You will sometimes see surprising … due to precision issues.”
Missing word, maybe “consequences” or “effects” ?
Thanks for the great j...
New
@seanmor5
In the example:
Nx.tensor(0.0000000000000000000000000000000000000000000001)
is said to be 1.0e-45 but it is actually 1.0e-46
New
Book Info
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Pragmatic Bookshelf
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