i think instead of ax = sns.barplot(list(tags), list(tag_counts)) it should be ax = sns.barplot(x=list(tags), y=list(tag_counts)) in code at https://madewithml.com/courses/mlops/exploratory-data-analysis/

Hi @GokuMohandas,

I've been recently taking a look at the sample Notebooks in this project and I found them really interesting and valuable for teaching purposes. We're even thinking about adding part of them to our curriculum at https://rmotr.com/ (cofounder and teacher here), in our Data Science program.

We have a small service at RMOTR that lets you run a Jupyter environment online in a single click. Similar to Google Colab or Binder, but also with the ability of installing custom requirements, clone an entire GH repo, etc. We use it for our students, so they don't have to hit the initial wall of installing the whole local Jupyter setup when they are getting started in the DS world.

You can see how practicalAI looks like in the service using this link: https://notebooks.rmotr.com/clone/gh/GokuMohandas/practicalAI

Note that all requirements listed in requirements.txt are already installed when the env is loaded, so people can start using it right away. That gives you the flexibility of adding any requirement, and not being tied to what Colab provides by default.

Do you think it would be a good choice to add it as a third launching option? Alternatively to Colab and Binder, already listed in the README.

I hope you like it, and I truly appreciate any feedback.

thanks.

1. Typo under Model section: 3. We'll apply convolution via filters (filter_size, vocab_size, num_filters) should be embedding_dim to replace vocab_size?
2. Typo under Experiments: first have to decice
3. Typo under Interpretability padding our inputs before convolution to result is outputs is should be in
4. Could there be a general explanation of moving models/data across devices? My current understanding is that they have to be both on the same place (cpu/gpu). If on gpu, just stay on gpu through the whole train/eval/predict session. I couldn't understand why under Inference device = torch.device("cpu") moves things back to cpu.
5. interpretable_trainer.predict_step(dataloader) breaks with AttributeError: 'list' object has no attribute 'dim'. The precise step is F.softmax(z), where for interpretable_model, z is a list of 3 items and it was trying to softmax a list instead of a tensor.

Under Pytorch --> Interpretability: b_unscaled = b * y_scaler.scale_ + y_scaler.mean_ - np.sum(W_unscaled*X_scaler.mean_) This line seems to be missing a * (y_scaler.scale_/X_scaler.scale_) in the last np.sum term.

The table for W unscaled was also confusing. It has a sum term shown there, which means if X began with 2 predictors (this lesson only used 1 predictor), the scaled W will have 2 predictors while the sum will aggregate the 2 weights into 1 unscaled weight? Can't wrap my head around this.

Also, under Pytorch --> Interpretability, W_unscaled = W * (y_scaler.scale_/X_scaler.scale_) there was no sum used here, so looks inconsistent with the formula in the table.

hi! appreciate your work here, me and my friends really learned a lot here we happened to find a platform in mainland China providing similar service to google colab and kaggle ( as you may known there is connectivity problem to google services in mainland China) called KESCI(www.kesci.com). They provide dev-ready and up-to-date Python & R cpu environment all for free and an upcoming gpu support. we also managed to translate the whole series to Chinese and applied for a column to publish them on KESCI, as a series. you can access it here : https://www.kesci.com/home/column/5c20e4c5916b6200104eea63 the Computer Vision notebook has already been translated but is still being trained in the transfer-learning section also, do you think it is possible to add this as another launching option? i think there must be more people in China who could learn from your tutorials!

Hi,GokuMohandas: I translate all content of Made-With-ML into chinese language, I post the content in my [blog] (https://franztao.github.io) and wechat blog。I wish get your agree about the recreated content by the original copyright owner?

While creating the LabelEncoder class, I couldnt understand why return self in class method fit(self,y)? My understanding is that when we call this method, the object variables are updated so no need for self? Please correct me if I'm wrong, just trying to reason myself with each step of the code.

    def fit(self, y):
        classes = np.unique(y)
        for i, class_ in enumerate(classes):
            self.class_to_index[class_] = i
        self.index_to_class = {v: k for k,v in self.class_to_index.items()}
        self.classes = list(self.class_to_index.keys())
        return self #Why?

I am running the tagifai.ipynb notebook on the windows platform but facing difficulty viewing the experiment in MLflow.

Steps Done:

1. Cloned the repo
2. Running the "mlops-course\notebooks\tagifai.ipynb" in vs code locally.
3. To run the server "mlflow server -h 0.0.0.0 -p 8000 --backend-store-uri /experiments/" from the location of the notebook, experiments is the next folder inside it. # $PWD is omitted because of windows.
4. Opening the "http://localhost:8000/#/"

Observation :

1. No signs of experiment run.
2. Image attached below for ref.

Please provide assistance with this issue.

Thanks

Hi Goku... I am really thankful for all your amazing tutorials.

I however was facing some issues in the Transformers lecture. There are a few minor bugs here with missing variables and imports; which was not an issue.

The training code however is missing the block:

# Train
best_model = trainer.train(
    num_epochs, patience, train_dataloader, val_dataloader)

Also when i wrote this and ran it, I got an error:

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  from ipykernel import kernelapp as app
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
[<ipython-input-68-8d0f0dee99db>](https://localhost:8080/#) in <module>()
      1 # Train
      2 best_model = trainer.train(
----> 3     num_epochs, patience, train_dataloader, val_dataloader)

6 frames
[/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py](https://localhost:8080/#) in dropout(input, p, training, inplace)
   1277     if p < 0.0 or p > 1.0:
   1278         raise ValueError("dropout probability has to be between 0 and 1, " "but got {}".format(p))
-> 1279     return _VF.dropout_(input, p, training) if inplace else _VF.dropout(input, p, training)
   1280 
   1281 

TypeError: dropout(): argument 'input' (position 1) must be Tensor, not str

Apparently, the issue comes from the line :

seq, pool = self.transformer(input_ids=ids, attention_mask=masks)

wherein the "pool" returned is of class string. Upon printing the type and the value of it i get the following :

<class 'str'>
pooler_output

Can you please have a look into this. Thanks in Advance!!

Hi Goku,

I'm going through the Pandas and I noticed that in the Feature engineering section, you mentioned about applying a lambda function to create a new feature, but the code for it does not appear. I think it's just a minor typo.

Regards, Roberto

Problem: Starting from either https://practicalai.me/learn/lessons/ or https://github.com/practicalAI/practicalAI, when attempting to click any of the lessons I see "Notebook not found". Proposed fix: Possibly "basic_ml" should be added to the path?

When I click "authorize with Github" I see the same thing:

The link given then does not work:

In the case of the "linear regression" notebook, the non-working link given on the "lessons" page is https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/04_Linear_Regression.ipynb

Whereas if you go find it on github directly, it is https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/basic_ml/04_Linear_Regression.ipynb

Changed $\hat{y} = \frac{e^{XW_y}}{\sum_j e^{XW}}$ to $\hat{y} = \frac{e^{XW}}{\sum_j e^{XW_j}}$ in the equation defining softmax (located after the second paragraph).

fstring format error with too many double quotes as in the first example of this error print (f"m:b = {class_counts["malignant"]/class_counts["benign"]:.2f}"), Should be single quote like print (f'm:b = {class_counts["malignant"]/class_counts["benign"]:.2f}')