@@emileeid6347 no as long as you understand it they will definetly qask about it and stuff. maybe add something once you finish which can make it unique
This is by far the most insightful content I have come across. It has enhanced my practical understanding of NLP. I can't wait to see what you share next! Many thanks man, wishing you the best of luck.
One of the best single CS related videos on TH-cam. I have watched all of your computer vision videos so far. Amazing work keep at it. Maybe develop an AI Golf coach program where they give feedback on your posture when taking a swing. That would be a massive project😅
Bro, the amount of skills i gained from your 3 projects is impressive! You are helping people to convert their ml/dl knowledge into something "material". teniss/football analyzer, chatbot. Keep it that way.
This video offers a fascinating dive into TV series analysis with a smorgasbord of AI techniques. From scraping data with Scrapy to crafting character networks and building chatbots, it’s a treasure trove for anyone eager to explore AI's creative potential.
Allah bless you my friend. Your channel is my favorite among my 3 favorite channels on TH-cam. Andrej Karpathy, Umar Jamil and You. How can donate you on the youtube?
Timestamps (Powered by Merlin AI) 00:05 - Build an AI/NLP TV Series Analysis System 02:07 - Setting up data sets for TV series analysis 07:11 - Ninjutsu, Genjutsu, and physical attacks are three types of combat techniques in the TV series analysis system. 09:45 - Building an HTML page with tags 15:29 - Creating and styling containers using classes and divs 18:10 - Using Scrapy library for web scraping 22:57 - Iterating over web pages to extract data using Python 25:41 - Extracting website links and crawling relevant information using Python 31:04 - Extract classification information from a specific div 34:03 - HTML aside section and its components 39:40 - Extracting Jutsu description from webpage 42:13 - Running Scrapy for data extraction 47:25 - Neural network predicting defined classes like cat or dog 49:44 - Training involves forward and backward propagation and batch processing for efficient model improvement. 54:31 - Analyzing text using mathematical functions in AI 56:55 - Introduction to LSTM and challenges with RNN 1:02:02 - Introduction to attention mechanism and transformation model 1:04:29 - Training GPT model using semi-supervised learning 1:09:11 - Implementing zero-shot classifiers for flexible theme classification 1:11:21 - Setting up AI/NLP environment and Transformers installation. 1:16:36 - Setting up Torch for GPU usage and loading AI model 1:19:17 - Theme classification using zero-shot classifier 1:25:03 - Cleaning text data and preparing it for classification 1:27:50 - Cleaning and processing subtitles for TV series analysis 1:33:35 - Demonstrating how to run NLP on Pandas data frame 1:36:32 - Executing batched AI/NLP TV series analysis 1:42:15 - Wrangling and calculating mean scores for themes using numpy 1:45:01 - Using the apply function to run a function on all rows for a specific column. 1:50:39 - Initializing theme list and loading model in AI/NLP TV Series Analysis System 1:53:37 - Building a function called get themes for running inference on the whole Pandas data frame 1:59:08 - Utilizing os.path.join for folder paths in a Python project. 2:01:32 - Creating AI/NLP TV Series Analysis System 2:07:20 - Creating a function to analyze TV series themes and generate plot output. 2:10:02 - Setting up and visualizing a theme classifier in Python. 2:15:41 - Demonstrating theme classification and visualization using Gradio 2:18:17 - Setting up a vertical chart with specific dimensions 2:23:07 - Setting up AI/NLP project in GitHub 2:25:47 - Setting up AI/NLP analysis system on Google Colab 2:31:14 - Uploading data set and running the application 2:33:55 - Setting up and managing data and tools for analysis 2:38:52 - Utilizing Spacy for word-level named entity classification 2:41:41 - Loading and processing data sets in the analysis system 2:47:34 - Extracting entity names and labeling in sentences 2:50:14 - Creating a character network for TV series analysis. 2:55:47 - Implementing entity relationship extraction with sorting for standardization 2:58:24 - Creating a character relationship analysis system using Python libraries 3:03:30 - Setting up a TV series analysis system in Python 3:06:10 - Setting up NER model and defining functions in Python notebook 3:12:10 - Creating a character network visualization in Python 3:14:55 - Creating HTML and exposing modules outside the folder 3:20:21 - Setting up character network analysis with Gradio and Python 3:22:59 - Configuring paths and resolving errors in the script 3:28:08 - Setting up and running NLP analysis using Hugging Face 3:30:43 - Learned about NLP and building a text classification model 3:35:48 - Simplifying Jutsu into different types for analysis 3:38:16 - Implement a cleaner function to remove HTML tags and format text 3:43:37 - Troubleshooting label encoding and mapping errors 3:46:28 - Transforming text data into numerical format 3:51:25 - Convert a Pandas dataframe to a Hugging Face dataset for analysis. 3:54:05 - Creating a cleaner function and a Jutsu classifier file 3:59:36 - Loading data and preprocessing with Hugging Face models 4:02:26 - Import pre-processing and labels, train test split, and dataset 4:07:27 - Obtaining class weights for data analysis 4:10:13 - Building AI/NLP TV Series Analysis System 4:15:01 - Building a custom trainer for AI model with Hugging Face. 4:17:44 - Neural Network Training Process Overview 4:23:03 - Training code is complete, now focus on loading and inference 4:25:41 - Creating a text classification function with post-processing 4:31:23 - Creating a hugging face token for model deployment 4:33:41 - Create a token and add it to .env file for security 4:38:45 - Fixing and updating the dataset path for training purposes 4:41:35 - Setting up the AI/NLP TV Series Analysis System 4:46:28 - Keep your model files up to date to avoid errors during training. 4:48:50 - Building a character chatbot module using pre-existing models in Gradio 4:53:03 - Building a character chatbot with Hugging Face and Python 4:55:37 - Using Gradio for character selection and text processing 5:00:43 - Creating a flag to determine relevant rows 5:03:03 - Selecting indices for conversation analysis 5:08:13 - Data wrangling and model training with Hugging Face 5:10:31 - Creating a character chatbot in Python using Hugging Face 5:15:43 - Creating data set and loading model in Python 5:18:13 - Defining the train function with parameters and settings 5:23:22 - Configuring and initiating the model for sequence classification 5:25:52 - Setting up required configurations for Hugging Face and TRL 5:31:04 - Setting up model training and saving process 5:33:39 - Loading and configuring the pre-trained model and tokenizer. 5:39:02 - Setting up the pipeline for text generation using Transformers 5:41:37 - Adding system prompt and messages 5:47:02 - Creating a character chatbot with Gradio 5:49:54 - Setting up character chatbot and examining edits in the code 5:55:22 - Ensure correct spelling in quantization error config 5:58:42 - Creating a character chatbot using state-of-the-art models 6:04:03 - Built AI/NLP TV Series Analysis System with various tools 6:06:21 - Learned skills are important for NLP and AI.
00:05 Building AI/NLP TV Series Analysis System with multiple components 02:07 Setting up data folders and acquiring data sets 07:11 Ninjutsu, physical attack, and genjutsu classification 09:45 Introduction to basic HTML tags 15:29 Using classes to style divs 18:10 Introduction to web scraping using Scrapy library 22:57 Iterating over next pages using anchor tags 25:41 Creating a web scraping function to extract data from a specific website 31:04 Extract classification and relevant data from div using Beautiful Soup 34:03 Exploring HTML structure of aside section 39:40 Using BeautifulSoup to extract and manipulate text data 42:13 Scrapy used to crawl websites and save data in structured format 47:25 Neural networks predict defined classes like cat or dog based on input data 49:44 Discussing the training process of AI models 54:31 Neural network architecture for text analysis 56:55 Introduction to the limitations of RNN in AI models 1:02:02 Introduction to attention mechanism and transition to Transformer model 1:04:29 AI models are trained using semi-supervised learning 1:09:11 Zero shot classifiers allow input of any arbitrary number of classes for classification. 1:11:21 Setting up the environment and installing necessary libraries. 1:16:36 Loading AI model with Torch and using CPU 1:19:17 Theme classification with zero-shot classifier 1:25:03 Cleaning and combining sentences in NLP text processing 1:27:50 Creating a function to load and clean subtitles data 1:33:35 Demonstration on dividing script into sentences and batching for NLP tokenization 1:36:32 Running and analyzing the batched sentences output 1:42:15 Wrangling the output and calculating mean using numpy library 1:45:01 Perform theme inference on TV show scripts using Python libraries 1:50:39 Initializing AI/NLP TV Series Analysis System 1:53:37 Setting up the classifier and necessary functions 1:59:08 Setting up a GUI using Gradio for machine learning applications 2:01:32 Creating a TV series analysis system using Hugging Face and Spacy 2:07:20 Building a system to analyze TV series using AI/NLP tools 2:10:02 Exposing classifier outside the folder 2:15:41 Building and using a theme classifier with Gradio 2:18:17 Customizing display settings and running local tests 2:23:07 Configuring the system to avoid exposing sensitive information 2:25:47 Setting up AI/NLP TV Series Analysis System 2:31:14 Uploading and running the TV series analysis system 2:33:55 Setting up output classifier and running it with Gradio 2:38:52 Installing Spacy for named entity recognition 2:41:41 Loading and processing data using Python modules 2:47:34 Extracting and processing entity names using NLP 2:50:14 Creating a character network using Python libraries 2:55:47 Sorting entities for standardized representation 2:58:24 Create a character relationship analysis system 3:03:30 Visualizing network analysis in Python 3:06:10 Implementing named entity recognition using Spacy in Python 3:12:10 Creating a character network graph. 3:14:55 Setting up HTML source code for character network 3:20:21 Implementing AI/NLP TV Series Analysis System 3:22:59 Setting up script and output paths for AI/NLP TV series analysis 3:28:08 Building an AI/NLP TV Series Analysis System 3:30:43 Setting up a text classification model for Jutsu techniques 3:35:48 Simplify Jutsus into different categories for analysis 3:38:16 Creating a cleaner function for scraping and cleaning data 3:43:37 Creating and correcting labels for TV series analysis 3:46:28 Data preparation for AI model training 3:51:25 Convert Pandas DataFrame to Hugging Face Dataset for TV series analysis. 3:54:05 Setting up the Jutsu classifier with parameters 3:59:36 Loading dataset and preprocessing data using Python Pandas and Hugging Face 4:02:26 Implementing pre-processing using labels and train test split 4:07:27 Understanding Class Weights Calculation 4:10:13 Building a function to train and load models for TV series analysis. 4:15:01 Training strategies and custom trainer in AI/NLP model building 4:17:44 Implementing forward and backward propagation in AI model training 4:23:03 GPU memory management in training and model loading 4:25:41 Creating a text classification function in Python 4:31:23 Define and implement the classify text function 4:33:41 Create and manage a secret token for Hugging Face integration 4:38:45 Troubleshooting and correcting errors in the training data and code customization 4:41:35 Setting up a text classification system using Hugging Face and Spacy 4:46:28 Removing duplicate models and troubleshooting training issues 4:48:50 Implementing Character Chatbot Module using Gradio 4:53:03 Setting up a notebook to demonstrate data wrangling for an AI character chatbot 4:55:37 Creating a chatbot without actions using Gradio 5:00:43 Using Python to count and filter words in a dataframe 5:03:03 Setting up prompts for AI chatbot to imitate Naruto character responses 5:08:13 Data wrangling and model training process with Hugging Face 5:10:31 Creating a character chatbot using Hugging Face and Python 5:15:43 Creating a function to load data for AI/NLP analysis 5:18:13 Define and explain the train function with key parameters 5:23:22 Setting up AI/NLP TV Series Analysis System 5:25:52 Installing necessary components for Laura config 5:31:04 Setting up the AI/NLP TV series analysis system 5:33:39 Setting up base model and tokenizer for AI/NLP analysis 5:39:02 Setting up the pipeline for AI model with Hugging Face 5:41:37 Adding system prompts and messages 5:47:02 Setting up character chatbot in Gradio UI 5:49:54 Setting up the AI system with Gradio and Python 5:55:22 Troubleshooting and fixing issues during AI model training 5:58:42 Creating a character chatbot with state-of-the-art models 6:04:03 Built AI/NLP TV series analysis system with key tools 6:06:21 Key NLP and AI skills are crucial for success. Crafted by Merlin AI.
I haven't worked with genAI with Azure. But there were some new releases in GCP and AWS that I played around with. They make utilizing and deploying LLMs very easy. I'm not sure whether Azure has similar solutions or note yet.
This is the the for loop line. It's a little different from what you wrote. The code is in the Github repo and I just reran and it's working fine. Maybe just copy paste it from there. for href in response.css('.smw-columnlist-container')[0].css("a::attr(href)").extract():
I have a Laptop configuration of 8 GB RAM and Nvidia RTX 1650, I am planning to upgrade the RAM to 16, will I be able to run these LLMs locally or should I try using a less powerful model. kindly, provide your suggestion, please
I think, RTX 1650 have 4GB of GPU memory which is enough for 3 of the 4 models I use. Actually those 3 models will work with the 8 GB of RAM. but Llama is big and requires at least 12 GB of GPU memory. I show you how to run this on Google Colab so you don't have to run it locally.
@@codeinajiffy Hey please reply! Does this chatbot work for any series or shows? Like, anything? Or if I just feed it one show, it only works for that?
everytime i try to get a graph in the gradio app with colab i get this error : " error : Connection errored out." , idk if the problem is with my code or what
at 2:33:22 while running on the colab i am getting connection errored out error in my pc , is there any way to solve this issue,anyone please help me on this issue
OSError: [WinError 127] The specified procedure could not be found. Error loading "C:\Users\....\AppData\Local\Programs\Python\Python312\Lib\site-packages\torch\lib\torch_cuda.dll" or one of its dependencies. unfortunately i had to give up with your tutorial as getting pytorch to work is a nightmare. it doesnt seem to work. i've tried different versions and different python kernals. i installed everything using pip. just doesnt work
I get the frustration of installing things. Especially on windows. I use WSL to simulate a Linux environment so that I don't have to deal with this. I recommend you to run it Google colab directly like I'm doing in the tutorial. You will just have to skip running locally. Another longer solution is to download WSL and set up your environment there. But that can be a long process.
JazakAllah brother. First the football one and now this, You have no idea how many jobless people you are helping to get a job.
R u using that in your resume !?
@@Tothefutureand why not?
@@meu22422 they will know 🤣 almost half a million people watched it so think abt it
Is it wrong to use those on the Resume
@@emileeid6347 no as long as you understand it they will definetly qask about it and stuff. maybe add something once you finish which can make it unique
When you see the Activate Windows phrase, you know that it being such a wonderful video.
This is by far the most insightful content I have come across. It has enhanced my practical understanding of NLP. I can't wait to see what you share next! Many thanks man, wishing you the best of luck.
Hey please reply! Does this chatbot work for any series or shows? Like, anything? Or if I just feed it one show, it only works for that?
One of the best single CS related videos on TH-cam. I have watched all of your computer vision videos so far. Amazing work keep at it. Maybe develop an AI Golf coach program where they give feedback on your posture when taking a swing. That would be a massive project😅
Thanks for your contribution. Hope you will come up with more projects about chatbot, or maybe image generation. I am looking forward to it!! 👏👏👏
Bro, the amount of skills i gained from your 3 projects is impressive! You are helping people to convert their ml/dl knowledge into something "material". teniss/football analyzer, chatbot. Keep it that way.
Hey please reply! Does this chatbot work for any series or shows? Like, anything? Or if I just feed it one show, it only works for that?
im so glad i found your channel! its a treasure honestly!
This video offers a fascinating dive into TV series analysis with a smorgasbord of AI techniques. From scraping data with Scrapy to crafting character networks and building chatbots, it’s a treasure trove for anyone eager to explore AI's creative potential.
Here before this blows up!! bro you are a legend🙌
Great Bro your channel is one of my favourite i'm always waiting for your videos!!!! God Bless you
Brooo thank you for blessing my weekend!!! I am gonna work on it this weekend!
Allah bless you my friend. Your channel is my favorite among my 3 favorite channels on TH-cam. Andrej Karpathy, Umar Jamil and You. How can donate you on the youtube?
just saw this in my feed, gonna complete this now
I just found out what I am doing this weekend :D. Thanks a lot brother!
Great video mate! Thanks for sharing your knowledge!
Thanks bro! Your tutorials are really gr8 and detailed. I always enjoy following them. More of the Gen AI and LLM projects. This is in vogue.
u a life saver in this tough job market
wanna give you a BIG THANKS FOR SHARING !
Assalamu alaykum brother. I was watching your tutorial on football detection. Thank you. If I don’t understand something, I will ask you.
Amazing as always!
ALways love unique projects. Thanks!
Incredible video. WiIl do this on a chill weekend. Would you consider doing a recommender system project?
Could you create one for boxing like you did for tennis? I would love a tutorial to follow along with!
you are a saviour bro
ur the best
thx so much for this insights..
Timestamps (Powered by Merlin AI)
00:05 - Build an AI/NLP TV Series Analysis System
02:07 - Setting up data sets for TV series analysis
07:11 - Ninjutsu, Genjutsu, and physical attacks are three types of combat techniques in the TV series analysis system.
09:45 - Building an HTML page with tags
15:29 - Creating and styling containers using classes and divs
18:10 - Using Scrapy library for web scraping
22:57 - Iterating over web pages to extract data using Python
25:41 - Extracting website links and crawling relevant information using Python
31:04 - Extract classification information from a specific div
34:03 - HTML aside section and its components
39:40 - Extracting Jutsu description from webpage
42:13 - Running Scrapy for data extraction
47:25 - Neural network predicting defined classes like cat or dog
49:44 - Training involves forward and backward propagation and batch processing for efficient model improvement.
54:31 - Analyzing text using mathematical functions in AI
56:55 - Introduction to LSTM and challenges with RNN
1:02:02 - Introduction to attention mechanism and transformation model
1:04:29 - Training GPT model using semi-supervised learning
1:09:11 - Implementing zero-shot classifiers for flexible theme classification
1:11:21 - Setting up AI/NLP environment and Transformers installation.
1:16:36 - Setting up Torch for GPU usage and loading AI model
1:19:17 - Theme classification using zero-shot classifier
1:25:03 - Cleaning text data and preparing it for classification
1:27:50 - Cleaning and processing subtitles for TV series analysis
1:33:35 - Demonstrating how to run NLP on Pandas data frame
1:36:32 - Executing batched AI/NLP TV series analysis
1:42:15 - Wrangling and calculating mean scores for themes using numpy
1:45:01 - Using the apply function to run a function on all rows for a specific column.
1:50:39 - Initializing theme list and loading model in AI/NLP TV Series Analysis System
1:53:37 - Building a function called get themes for running inference on the whole Pandas data frame
1:59:08 - Utilizing os.path.join for folder paths in a Python project.
2:01:32 - Creating AI/NLP TV Series Analysis System
2:07:20 - Creating a function to analyze TV series themes and generate plot output.
2:10:02 - Setting up and visualizing a theme classifier in Python.
2:15:41 - Demonstrating theme classification and visualization using Gradio
2:18:17 - Setting up a vertical chart with specific dimensions
2:23:07 - Setting up AI/NLP project in GitHub
2:25:47 - Setting up AI/NLP analysis system on Google Colab
2:31:14 - Uploading data set and running the application
2:33:55 - Setting up and managing data and tools for analysis
2:38:52 - Utilizing Spacy for word-level named entity classification
2:41:41 - Loading and processing data sets in the analysis system
2:47:34 - Extracting entity names and labeling in sentences
2:50:14 - Creating a character network for TV series analysis.
2:55:47 - Implementing entity relationship extraction with sorting for standardization
2:58:24 - Creating a character relationship analysis system using Python libraries
3:03:30 - Setting up a TV series analysis system in Python
3:06:10 - Setting up NER model and defining functions in Python notebook
3:12:10 - Creating a character network visualization in Python
3:14:55 - Creating HTML and exposing modules outside the folder
3:20:21 - Setting up character network analysis with Gradio and Python
3:22:59 - Configuring paths and resolving errors in the script
3:28:08 - Setting up and running NLP analysis using Hugging Face
3:30:43 - Learned about NLP and building a text classification model
3:35:48 - Simplifying Jutsu into different types for analysis
3:38:16 - Implement a cleaner function to remove HTML tags and format text
3:43:37 - Troubleshooting label encoding and mapping errors
3:46:28 - Transforming text data into numerical format
3:51:25 - Convert a Pandas dataframe to a Hugging Face dataset for analysis.
3:54:05 - Creating a cleaner function and a Jutsu classifier file
3:59:36 - Loading data and preprocessing with Hugging Face models
4:02:26 - Import pre-processing and labels, train test split, and dataset
4:07:27 - Obtaining class weights for data analysis
4:10:13 - Building AI/NLP TV Series Analysis System
4:15:01 - Building a custom trainer for AI model with Hugging Face.
4:17:44 - Neural Network Training Process Overview
4:23:03 - Training code is complete, now focus on loading and inference
4:25:41 - Creating a text classification function with post-processing
4:31:23 - Creating a hugging face token for model deployment
4:33:41 - Create a token and add it to .env file for security
4:38:45 - Fixing and updating the dataset path for training purposes
4:41:35 - Setting up the AI/NLP TV Series Analysis System
4:46:28 - Keep your model files up to date to avoid errors during training.
4:48:50 - Building a character chatbot module using pre-existing models in Gradio
4:53:03 - Building a character chatbot with Hugging Face and Python
4:55:37 - Using Gradio for character selection and text processing
5:00:43 - Creating a flag to determine relevant rows
5:03:03 - Selecting indices for conversation analysis
5:08:13 - Data wrangling and model training with Hugging Face
5:10:31 - Creating a character chatbot in Python using Hugging Face
5:15:43 - Creating data set and loading model in Python
5:18:13 - Defining the train function with parameters and settings
5:23:22 - Configuring and initiating the model for sequence classification
5:25:52 - Setting up required configurations for Hugging Face and TRL
5:31:04 - Setting up model training and saving process
5:33:39 - Loading and configuring the pre-trained model and tokenizer.
5:39:02 - Setting up the pipeline for text generation using Transformers
5:41:37 - Adding system prompt and messages
5:47:02 - Creating a character chatbot with Gradio
5:49:54 - Setting up character chatbot and examining edits in the code
5:55:22 - Ensure correct spelling in quantization error config
5:58:42 - Creating a character chatbot using state-of-the-art models
6:04:03 - Built AI/NLP TV Series Analysis System with various tools
6:06:21 - Learned skills are important for NLP and AI.
00:05 Building AI/NLP TV Series Analysis System with multiple components
02:07 Setting up data folders and acquiring data sets
07:11 Ninjutsu, physical attack, and genjutsu classification
09:45 Introduction to basic HTML tags
15:29 Using classes to style divs
18:10 Introduction to web scraping using Scrapy library
22:57 Iterating over next pages using anchor tags
25:41 Creating a web scraping function to extract data from a specific website
31:04 Extract classification and relevant data from div using Beautiful Soup
34:03 Exploring HTML structure of aside section
39:40 Using BeautifulSoup to extract and manipulate text data
42:13 Scrapy used to crawl websites and save data in structured format
47:25 Neural networks predict defined classes like cat or dog based on input data
49:44 Discussing the training process of AI models
54:31 Neural network architecture for text analysis
56:55 Introduction to the limitations of RNN in AI models
1:02:02 Introduction to attention mechanism and transition to Transformer model
1:04:29 AI models are trained using semi-supervised learning
1:09:11 Zero shot classifiers allow input of any arbitrary number of classes for classification.
1:11:21 Setting up the environment and installing necessary libraries.
1:16:36 Loading AI model with Torch and using CPU
1:19:17 Theme classification with zero-shot classifier
1:25:03 Cleaning and combining sentences in NLP text processing
1:27:50 Creating a function to load and clean subtitles data
1:33:35 Demonstration on dividing script into sentences and batching for NLP tokenization
1:36:32 Running and analyzing the batched sentences output
1:42:15 Wrangling the output and calculating mean using numpy library
1:45:01 Perform theme inference on TV show scripts using Python libraries
1:50:39 Initializing AI/NLP TV Series Analysis System
1:53:37 Setting up the classifier and necessary functions
1:59:08 Setting up a GUI using Gradio for machine learning applications
2:01:32 Creating a TV series analysis system using Hugging Face and Spacy
2:07:20 Building a system to analyze TV series using AI/NLP tools
2:10:02 Exposing classifier outside the folder
2:15:41 Building and using a theme classifier with Gradio
2:18:17 Customizing display settings and running local tests
2:23:07 Configuring the system to avoid exposing sensitive information
2:25:47 Setting up AI/NLP TV Series Analysis System
2:31:14 Uploading and running the TV series analysis system
2:33:55 Setting up output classifier and running it with Gradio
2:38:52 Installing Spacy for named entity recognition
2:41:41 Loading and processing data using Python modules
2:47:34 Extracting and processing entity names using NLP
2:50:14 Creating a character network using Python libraries
2:55:47 Sorting entities for standardized representation
2:58:24 Create a character relationship analysis system
3:03:30 Visualizing network analysis in Python
3:06:10 Implementing named entity recognition using Spacy in Python
3:12:10 Creating a character network graph.
3:14:55 Setting up HTML source code for character network
3:20:21 Implementing AI/NLP TV Series Analysis System
3:22:59 Setting up script and output paths for AI/NLP TV series analysis
3:28:08 Building an AI/NLP TV Series Analysis System
3:30:43 Setting up a text classification model for Jutsu techniques
3:35:48 Simplify Jutsus into different categories for analysis
3:38:16 Creating a cleaner function for scraping and cleaning data
3:43:37 Creating and correcting labels for TV series analysis
3:46:28 Data preparation for AI model training
3:51:25 Convert Pandas DataFrame to Hugging Face Dataset for TV series analysis.
3:54:05 Setting up the Jutsu classifier with parameters
3:59:36 Loading dataset and preprocessing data using Python Pandas and Hugging Face
4:02:26 Implementing pre-processing using labels and train test split
4:07:27 Understanding Class Weights Calculation
4:10:13 Building a function to train and load models for TV series analysis.
4:15:01 Training strategies and custom trainer in AI/NLP model building
4:17:44 Implementing forward and backward propagation in AI model training
4:23:03 GPU memory management in training and model loading
4:25:41 Creating a text classification function in Python
4:31:23 Define and implement the classify text function
4:33:41 Create and manage a secret token for Hugging Face integration
4:38:45 Troubleshooting and correcting errors in the training data and code customization
4:41:35 Setting up a text classification system using Hugging Face and Spacy
4:46:28 Removing duplicate models and troubleshooting training issues
4:48:50 Implementing Character Chatbot Module using Gradio
4:53:03 Setting up a notebook to demonstrate data wrangling for an AI character chatbot
4:55:37 Creating a chatbot without actions using Gradio
5:00:43 Using Python to count and filter words in a dataframe
5:03:03 Setting up prompts for AI chatbot to imitate Naruto character responses
5:08:13 Data wrangling and model training process with Hugging Face
5:10:31 Creating a character chatbot using Hugging Face and Python
5:15:43 Creating a function to load data for AI/NLP analysis
5:18:13 Define and explain the train function with key parameters
5:23:22 Setting up AI/NLP TV Series Analysis System
5:25:52 Installing necessary components for Laura config
5:31:04 Setting up the AI/NLP TV series analysis system
5:33:39 Setting up base model and tokenizer for AI/NLP analysis
5:39:02 Setting up the pipeline for AI model with Hugging Face
5:41:37 Adding system prompts and messages
5:47:02 Setting up character chatbot in Gradio UI
5:49:54 Setting up the AI system with Gradio and Python
5:55:22 Troubleshooting and fixing issues during AI model training
5:58:42 Creating a character chatbot with state-of-the-art models
6:04:03 Built AI/NLP TV series analysis system with key tools
6:06:21 Key NLP and AI skills are crucial for success.
Crafted by Merlin AI.
Thank you! 🙏
You are a genius🤖
Do you guys actually put this in resume or is it just for learning purpose? I'm honestly confused.
I think both of them.
Wtf I am just seeing. This is amazing ❤
Where is the website design in the start?? i can't see it in the end
another way of getting the texts in subtitles is spiltting by ",," and taking the last element
Please make a go/rust deployment project
Thank ypu so much ! I'm looking for an genAI project with Azure. Any advice?
I haven't worked with genAI with Azure. But there were some new releases in GCP and AWS that I played around with. They make utilizing and deploying LLMs very easy. I'm not sure whether Azure has similar solutions or note yet.
the selectors in the video dont work. how can i resolve. anytimes i call the selectors from smw-columnlist the output is empty
This is the the for loop line. It's a little different from what you wrote. The code is in the Github repo and I just reran and it's working fine. Maybe just copy paste it from there.
for href in response.css('.smw-columnlist-container')[0].css("a::attr(href)").extract():
I have a Laptop configuration of 8 GB RAM and Nvidia RTX 1650, I am planning to upgrade the RAM to 16, will I be able to run these LLMs locally or should I try using a less powerful model. kindly, provide your suggestion, please
I think, RTX 1650 have 4GB of GPU memory which is enough for 3 of the 4 models I use. Actually those 3 models will work with the 8 GB of RAM. but Llama is big and requires at least 12 GB of GPU memory. I show you how to run this on Google Colab so you don't have to run it locally.
@@codeinajiffy Hey please reply! Does this chatbot work for any series or shows? Like, anything? Or if I just feed it one show, it only works for that?
Need process from development to deployment
everytime i try to get a graph in the gradio app with colab i get this error : " error : Connection errored out." , idk if the problem is with my code or what
i also have the same problem
at 2:33:22 while running on the colab i am getting connection errored out error in my pc , is there any way to solve this issue,anyone please help me on this issue
Is it any possible to contact with you?
You have my email and LinkedIn profile linked in the TH-cam channel's information.
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i am new in ai its worth for me watching out not
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OSError: [WinError 127] The specified procedure could not be found. Error loading "C:\Users\....\AppData\Local\Programs\Python\Python312\Lib\site-packages\torch\lib\torch_cuda.dll" or one of its dependencies. unfortunately i had to give up with your tutorial as getting pytorch to work is a nightmare. it doesnt seem to work. i've tried different versions and different python kernals. i installed everything using pip. just doesnt work
I get the frustration of installing things. Especially on windows. I use WSL to simulate a Linux environment so that I don't have to deal with this.
I recommend you to run it Google colab directly like I'm doing in the tutorial. You will just have to skip running locally.
Another longer solution is to download WSL and set up your environment there. But that can be a long process.