Hey Sophia, thanks for such wonderful explanation of space. I am facing a issue where I am doing this all for the first time Error I'm facing is : repo does not exist I using the username same as in huggingface and using the token as password Still I'm facing issue Kudos again

Thank you for the fantastic overview of LlamaIndex functionalities 😊

I am halfway through reading the book myself. It is absolutely amazing. Simple and high level enough to keep you emersed when commuting, but still rich enough to learn quite a few things on the way!

great explanations!

How could this ever be applied to creating a RAG for a customized ChatGPT? Closer to the point, as something with a user interface that my grandmother could use? Obviously the average user simply isn’t going to run anything that has code on the page.. they’ll need a slick UI. Something like the OpenAI Custom GPT. How does that slick UI ever become integrated with what you’re doing?

Great video! I needed this to understand my starting point for User Activation Analysis

00:03 Sebastian Raschka shares his background and work experience 02:18 Sebastian Raschka discusses his motivation for writing a Q&A book on machine learning. 06:27 Author's approach to writing the book 08:35 Using earlier layers in neural networks for embeddings depends on the data set and task 12:42 Adding layers without removing any can improve model performance 14:55 Pruning models to create more efficient smaller models 19:21 Parameter efficient model adaptation technique explained 21:22 Q Laura allows using only one GPU instead of eight for fine-tuning. 25:21 Discussion on LAMA adapters and their similarities 27:37 Machine learning foundations remain consistent with potential additions in newer editions. 31:52 Utilizing multiple GPUs for faster training 33:56 Fully sharded data parallelism in PyTorch is a combination of data and tensor parallelism. 37:45 Usage of Fabric to simplify practice in machine learning. 39:46 Discussion on fine-tuning models for specific use cases 43:53 Models may look good on the surface but lack truthfulness compared to rlhf 45:56 Europe's challenge to train an ML model with limitations 49:45 Discussion on terminology in deep learning architectures 51:52 Comparison between encoder and decoder in autoencoders 55:49 Quantization methods and their implications 57:46 Encouragement to reach out for questions and check out the book Crafted by Merlin AI.

One recommendation, instead of a list of definitions about a topic, it would be more useful explain the main topic first.Like class imbalance, what it is, explain the definition. Then the list of class imbalance concepts can be only listed and maybe you dont even need to explain each of those.

just brilliant , thank you

Great video!

Thank you Sophia

Great you mention that those tools are not exclusive. However probably for simpler use cases one of them will be enough.

Great interview! I am glad to see a fellow brazilian 😁

How do you compare streamlit vs panel?

Amazing video but I have a question. I would be very happy if you could respond; even though it enhances the accuracy a lot, recursive retriever takes so much time. Would persisting indexes to the disk also be a solution in this case? or is there an alternative way to reduce the time? Thank you very much!! <3

How do you research for your videos?

Great contents and clear messages. Thank you so much! ❤

Is there anyway I can implement this without langchain and llama-index? I am not in favor having my RAG solution dependent on them.

Hello, what is toB & toC?

Do ai Poe chat how generation image and lyric on there can you do review on ai Poe chat

Great video. One question. How do I save the model locally.

Can i train this in Mac Air M1?

Can I deploy a fastapi app on it. It's basically a prediction app using deeplearning model

i have an api error

You sped past the most critical part of setting up the environment. please don't do that lol

Underrated channel

Yea his video was like a missing link explained perfectly...This one is nice too...:)

hi. please help me. how to create custom model from many pdfs in Persian language? tank you.

What I love the most is the kitten on the lower right of the video :)

Also thanks ! As after watching this video I also re-watch kaparthy and recreated everything and it worked ok for a text generation model .

Sorry but how to save the final model produced for deployment ? .. then how to fine tune this model for a downstream task such as question and answer ?

Thank you, Sophia.

Thank you

Contagious enthusiasm from a frontline pro

--start is promt? also Sophia please tell, will it work for other language?

Please, can you let me know if you connected your GPU to MAC M1 laptop or other source ?

Thank you Sophia, you really explained everything in clear way. How many hours(may be days, already) lost trying to find what you have explained! (sigh)👏

thank you very much, sister!

a tutorial of a tutorial -- lol -- much thanks!! 🙏

Hi, I just discovered your channel, love the content, i was wondering what would you use to make an LLM write code like you would do it, would use the codebase/context aware method or the training method, if so what tools and LLMs would be the best, if you make a video about this. Thank you

Awesome explanation Sophia !!

@13:00 what statistical test is used to calculate the p value?

Hey, thank u for this nice video. Can you please explain me how to set secret variables?

Really useful stuff! Thanks for making this video.

Excellent. Thanks so much. Question: is using ConversationalRetrievalChain more expensive (tokenwise)?

Proofed it with Python: kzbin.info/www/bejne/q3bNe5KOeq5-jJo Thanks fir sharing!

Great explanation!

Watched 3 times + read the reference! Great simple explanation!

Thanks Sophia, ur explanation is very clear

good one in short time