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amazing video!

Your code is not the TabNet, it is TabTransformer., they are different papers.

overall presentation needs improvement. Your accent isn't that hard to understand but your mic is tinny and I don't want to see your browser or desktop.

Thank you for sharing this short and concise video. It is helpful to understand knowledge distillation and for showing an example in code and for also walking through the code examples.

Here, are you tuning a text completion model? What about the instruct or chat models?

Thank you!!! I love every bit of the video. The fact that you didn't disrupt the lecture with "...Like, comment, share, and subscribe to the channel..." in 59 minutes till the end of the video shows how passionate you are to just share your knowledge. Thank you again. Could you cover the Molecule Generation Tasks? That will be very helpful to my current project.

Can you please share your jupyter notebook for tutoring/experimenting? Thanks.

Where can I find part2?

Great Cython introduction video! in the video you mentioned you're going to go in depth with Cython's features...is there a follow up Cython video to this one? Would love to see Cython's implementations either in data analytics (Pandas) or AI-related (pytorch, llamaindex, etc). Liked and subbed!

There is some strange screech sounds when ever you speak...

well stated sir. thanks for brief explanation and few source you presented. i hope we can appreciate your work and time you spent here by expanding this course and knowledge. btw i think you are persian, so i might say "daste shoma dard nakone" :)

What if we don't have access to the teacher's data?

eigen vector ---> آیگِن وکتور خوانده می شود.

Could you please explain the code of this paper as well

🎯 Key Takeaways for quick navigation: 00:00 📚 *Introduction to Probability Calibration* - Brief overview of why probability calibration is important. - Explanation of what probability calibration is. - Mention of examples to be covered in Jupyter Notebook. 01:32 🤔 *Why Probability Calibration is Needed* - Discussion on the unreliability of probability values from classifiers. - Importance of probability calibration for cases where percentages matter. - Example scenarios where calibrated probabilities are crucial. 03:30 📊 *Example: Calibration Process with 10 Data Points* - Introduction to a dataset with 10 data points for calibration. - Explanation of binning and cross-validation in probability calibration. - Visualization of the calibration curve for the given dataset. 07:42 📈 *Example: Calibration Process with 100 Data Points* - Overview of a dataset with 100 data points. - Observation of calibration histogram peaks due to classifier assumptions. - Discussion and visualization of the calibration plot for the 100-data-point dataset. 09:36 🔧 *Implementation of Probability Calibration in scikit-learn* - Introduction to two ways of implementing probability calibration: with prefix and without prefix. - Explanation of the code snippet for probability calibration implementation. - Overview of the scikit-learn library for calibration. 12:17 🖥️ *Implementation in Jupyter Notebook* - Importing necessary libraries for the Jupyter Notebook implementation. - Creation of a synthetic dataset for demonstration. - Setting up the dataset visualization using Seaborn. 25:38 📊 *Probability Calibration Plot in Jupyter Notebook* - Implementation of probability calibration in Jupyter Notebook. - Visualization of the probability calibration plot using Matplotlib. - Comparison of calibrated and uncalibrated probability curves. 30:02 📉 *Evaluation Metrics Comparison* - Calculation and comparison of evaluation metrics (Brier score, precision, recall, F1) for both calibrated and uncalibrated classifiers. - Interpretation of the metrics to assess the effectiveness of probability calibration. - Presentation of the results for the Gaussian Naive Bayes classifier. 34:44 📚 *Conclusion* - Recap of the key points discussed in the video. - Invitation for comments and suggestions from viewers. - Announcement of the next and final video on supervised learning in scikit-learn. Made with HARPA AI

How to download the train_clean_kalman.csv😁?

hi! thanks for the work, excellent explanation. notebook is not added?

Thank you for your sharing!

link to previous video, you are talking about in the start please ..?

Hi , Great content.. Could you please let me know how to copy values from table detected into a csv file?

Thanks for the review, great Job :), there is only one point about isotonic that I am not sure is correct if what you mentioned isotonic is appropriate for small data sets!? It is known to be the other way. would you please check? [time:10:14]

Thanks for putting these out! I have a hard time fitting reading into my schedule so these are really valuable for helping me stay up to date

I was hoping you would start with Dear Fellow Scholars hahaha

Thanks for sharing this. I am planning to buy a GPU, what's the cheapest GPU you suggest for fine-tuning LLMs. Did you try Mistral 7B? Any LLM which we can finetune on CPU as I have domain data on which I want to finetune a model for QA. Tried plain gpt2 model with vector db but didn't perform well..

promo sm

Can you make a video explaining some supervised training algorithm for SNN, I am trying to understand the available algorithms but can't get enough out of them, I need someone to explain.

How much is the download size?

Guys, are the features of the mojo extension working for you?

You imported pytorch or pandas as np but in the next line you are trying to call np.array which naturally results in exception.

Wow, what a garbage language :D

Btw you couldn't import torch as there was an exception on line 19. You forgot to remove np.array. 😂

Mojo sounds great, I didn't dl it though because their website uses google analytics. I don't like that.

I just realized that about 5 minutes of video is cut (maybe my fault during editing, but I am 90% sure I checked before uploading). In those five minutes I was talking about licensing and I had some criticism about it. Weird!

Is there any way to convert my Django project to Mojo or we aren't there yet? I wonder if this would be possible at all.

That is awesome. It looks a lot like Kotlin. I mostly use Python for AI/ ML, Kotlin for Android Mobile Dev and C++ for Game Dev. And this language Mojo looks like the best from all those 3 languages, plus is fast. Definitely learning this.

waiting for windows release, for me WSL is not working sadly

Congratulations on doing the first RUclips video as the local release was made available for Linux. I am still waiting for release on apple M chips

what configuration did you use?

Since Mojo is closed source 🥺 Do I have to pay money 💸💵💵💸 to use Mojo 🔥

how do you feel about the acquisition?

Interesting! Thank you AIology!

Another good one! Great new format. I still think your code tutorials are gold but this was great, thank you!

Excellent work, very clean. Could I have the notebook, PLEASE?

Thank you for this great video. It was really informative!

Can you share a better example to train ? this image example is confusing

What would we need to edit to run this with distributed inference? Assuming we just had a pc with 2 gpu cards??

hey i have one question , if i am finetuning it as in the example do we have to do it token by token for instance for every input output pair we would append the output tokens into input and use the next output as the prediction , but this would multiply datasets to max_output_length*total_pairs fold , is there a better way to train? not essentially token by token if i predict max_length and then use cross entropy is it fine ?

At min 17:47 shouldn't the skip layer be applied on the inputx rather than x, based on the architecture diagram? x is not the input to layer but an output of dilated conv & activations