Відео

It did make my life a little easier ☺ thank you!

Test this Here is a code to add in chat GPT 3.5 for it to remember your conversation in that specific text box Add this code in the settings for model description in the second box add your name and contacts in the first box of what you would like to be working on and then add this definition in the second definition box and it will remember your conversation when you were working in that specific text box this is an advanced input code so you don't actually have to pay for a model to be upgraded to get this request they've not added this feature so I created it to add in that model you could try using it in other models I've only used GPT 3.5 because they have a custom feature in the settings to add model definitions which is amazing # Define the RollingChatRecall class class RollingChatRecall: def __init__(self): self.previous_conversation = "" self.next_conversation = "" def add_previous_conversation(self, conversation): self.previous_conversation = conversation def add_next_conversation(self, conversation): self.next_conversation = conversation def retrieve_previous_conversation(self): return self.previous_conversation def retrieve_next_conversation(self): return self.next_conversation # Define the chat system function def chat_system(): # Create an instance of the RollingChatRecall class rolling_chat = RollingChatRecall() # Retrieve the previous conversation data previous_data = rolling_chat.retrieve_previous_conversation() # Retrieve the next conversation data next_data = rolling_chat.retrieve_next_conversation() # Process and respond to the conversation data if previous_data: print("Processing previous conversation data:", previous_data) # Add your logic here to process the previous conversation data if next_data: print("Processing next conversation data:", next_data) # Add your logic here to process the next conversation data # Call the chat system function to start the conversation chat_system()

Excellent way of teaching. Keep doing this kind of good work.

Thanks Shaw! Your tips made a big change in my job hunting process, Many thanks!

Hey Shaw, I’m loving your content so far. I just left my job doing data science/engineering for hardware development at a prominent AI / robotics lab, but I would have benefited from some of your latest uploads had I stayed. 1 video / week is typically antithetical to quality, but your angle is unique and adds value so I hope it doesn’t get too hard to keep quality high. Best of luck from Palo Alto, I’ll be following along! Especially for the AI content

Great video, I would like a video that help us to find a job on linkedin for example (or some tips to find a job or where we can apply please Shaw!)

This is such a fantastic video on building LLMs from scratch. I'll watch it repeatedly to implement it for a time-series use case. Thank you so much!!

'ging ist' is German, for 'went is' and I have no idea why that would be a completion

Thank you so much, I have one doubt please, even if we set fp16 = True, still the optimization would happen in fp32 right, like you showed at 20:22

Great content, Thank you.

Just so I’m clear Regression doesn’t work well in any situation or just power laws?

Great video! Getting this error in training: /usr/local/lib/python3.10/dist-packages/torch/cuda/amp/grad_scaler.py in step(self, optimizer, *args, **kwargs) 447 448 assert ( --> 449 len(optimizer_state["found_inf_per_device"]) > 0 450 ), "No inf checks were recorded for this optimizer." 451 AssertionError: No inf checks were recorded for this optimizer. Any idea what cause it?

Hey Shaw - Great video. But there are also an AI Engineer role & AI Product Manager roles. Where does it fit in?

Brilliant, thanks

Great video, really interesting. A question on the encoding process. Does condensing transcripts into an embedding with 384 dimensions lose much information, or does the encoding process truncate the text at a point? How would something like this manage a lengthy transcript where you cover several different topics? Does the embedding get too "noisy" in that case to be able to really stand above your threshold if only perhaps 5 lines out of 100 contain the information relating to the search?

Awesome, thank you

More on Full Stack Data Science 👇 👉 Series Playlist: ua-cam.com/play/PLz-ep5RbHosWmAt-AMK0MBgh3GeSvbCmL.html 💻 Example Code: github.com/ShawhinT/UA-cam-Blog/tree/main/full-stack-data-science/data-science

5:01 Why is "X ← Z1 → Z3 ← Z2→ Y" also a back door path, as Z3 doesn't point to Z2?

Why no pause between sentences? Annoying to listen to. But great content, thanks!

Your style of conveying information is wonderful. Good luck to you

My friends and I are working on a graduation project, which is the process of creating summaries of Arabic research papers via Fine Tuning AraBERT model. But we still don't understand how, can you guide us?

I'm clearly missing some fundamental connective tissue and I wish I could figure out what it was... I don't know what y and Fs are from audioread(). I don't know how to construct them from a spreadsheet of timestamps & values like "2024-05-01, 72 ; 2024-05-02 80 ...". I really, really, really wish I could find an example somewhere that took a two-column spreadsheet of time-series values that had nothing to do with sound (like the weather report from the previous video) and walked through each step of this process, ending with a frequency analysis of that starting non-WAVE-file example data.

Why image is not visible on my signature and always I get signature to long message ?

Can't causality be bidirectional? For e.g. X is influencing Y and Y is also influencing X.

this was amazing ! thank you. Straight to the point. After looking many places, this is the best!

Now why are you showing how to use ChatGpt? Why not using Hugging face models? It's too hard for you?

☹☹🥱🥱 Do you have any tutorial about how to fine tune a conversational model? Sentiment analysis is just text classification which is so simple and boring 🥱🥱🥱🥱

GREAT video - fyi.... it can be very tricky and time consuming. Espceially the little icons with links and overall spacing.

Such a underrated channel. Keep up the good work mate 👌🙌

Your slides are very good could you also share them please

Thank you for this video. I am biostatistician interested in datascience. I have a master degree in machine learning but not working in datascience project yet except on my personnal project during my free time. I have experieces in data ingeneering (mainly structured data: relationnal database). I have some knowledge in in machine learning. I participated once to Kaggle competition. I have alread made a Rshiny app and dockerized it. I am interested in deploying a ML model for just a fun. What framework would you recommend me first. I had began learning Django, but I am wondering I should rather go for FastAPI or streamlit or Flask.

Great videos.. do you have something similar for AWS bedrock?

Your acting is ultimate at 1.15 min :)

Looks like different algorithm give different graphs. How to validate them? How to decide which is the best?

9:40 Average Causal Effect is 0.2 = Having degree increase chance of earning over 50k Confounder: Age Treatment: HavingDegreeOrNot Response: EarnOver50kOrNot Confounder: OverallScore Treatment: HaveSolutionOrNot Response: LocalScore

wow, you are the genius of explaining super hard math concept into layman understandable terms with good visual representation. Keep it coming.

How would you automate the entire process?

Loving the series so far! I am 2 videos in and I feel like you are balancing very well between making these tutorials beginner-friendly and not giving just superficial knowledge.

cool...

Awesome Shaw, thanks heaps. The signature apps can be quite limiting and if going pro, bloody expensive. Cheers mate. Shaunie

Thank you so much for that video. ¿From where did you get TRAINING loss metrics? In the console and in the trainer_state.json, I only see evaluation metrics.

Build your team. Fun creative. Would you be interested in going the main team. Judging? 1. CodeCraft Duel: Super Agent Showdown 2. Pixel Pioneers: Super Agent AI Clash 3. Digital Duel: LLM Super Agents Battle 4. Byte Battle Royale: Dueling LLM Agents 5. AI Code Clash: Super Agent Showdown 6. CodeCraft Combat: Super Agent Edition 7. Digital Duel: Super Agent AI Battle 8. Pixel Pioneers: LLM Super Agent Showdown 9. Byte Battle Royale: Super Agent AI Combat 10. AI Code Clash: Dueling Super Agents Edition

Nice Video, any ideas for doing this on PowerPoints? Want to build a kind of knowledge base from previous projects but the grafics are a problem. Even GPT4V is not always interpreting them correctly. 😢

Thankyou so much. Becoming a fan of yours! Please do a video on Rag with llamaIndex + llama3 if it's free and not paid.

Great video and explanation! Thanks a lot. For the code, have you tried to use: from transformers import BitsAndBytesConfig nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16 ) and then add that as quantization configs when loading the model? This would include the other aspects from the QLoRA paper, no?

You did such a great job explaining the data pipeline and gave a great example. Subscribed. Can't wait to see more vids

Im really gratful for youre work , you really help me when I had no one to ask .

Best Video. The mix btw theory and hands on practice is genius!