Complete GenAI in 5 hours For Free 🔥 | RAG System Course

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  • Опубліковано 23 гру 2024

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  • @AyushSinghSh
    @AyushSinghSh  9 днів тому +5

    Make production grade systems or scale it via Self-host or try Timescale Cloud for free here: github.com/timescale/pgai

  • @Kamalsai369
    @Kamalsai369 8 днів тому +23

    Timestamps
    00:05 - Most students fail to master GenAI fundamentals through quick tutorials.
    02:02 - Learn to build production-grade systems in GenAI with a focus on NLP foundations.
    06:09 - Understanding text processing for computer efficiency is crucial.
    08:31 - Text processing fundamentals for NLP and machine learning.
    12:47 - Preprocessing text by removing punctuation, emojis, and stop words for NLP.
    14:55 - Overview of text processing techniques, including tokenization.
    19:17 - Understanding tokenization and embeddings for NLP models.
    21:28 - Embeddings convert words into vectors to reveal their relationships.
    25:44 - Understanding word embeddings and their relationships.
    27:44 - Understanding tokenization, embeddings, and attention mechanisms in language processing.
    31:39 - The attention mechanism helps models focus on key words in sentences.
    33:43 - Attention mechanisms highlight key words for better model performance.
    37:42 - Attention models prioritize words by retention scores for better comprehension.
    39:48 - Attention mechanisms focus on key information in data for effective NLP.
    43:51 - Transformers improve language processing by analyzing entire sentences at once.
    45:54 - Transformers use attention mechanisms for effective understanding of language.
    50:05 - Transformer models refine understanding through layered processing.
    52:02 - Understanding Transformers and text similarity measurement.
    55:57 - Explains various similarity measures for comparing text meaning.
    57:50 - Understanding mathematical similarity and information retrieval for text analysis.
    1:01:52 - Representing and ranking documents for effective information retrieval.
    1:03:45 - Indexing enhances information retrieval efficiency in systems like Google Search.
    1:07:46 - Retrieval models act as expert librarians for finding specific information.
    1:09:44 - Retrieval models are crucial for accurate answers in generative AI systems.
    1:13:47 - Dense retrieval models improve information search by focusing on meaning instead of exact word matches.
    1:15:53 - Retrieval models enhance generative AI by connecting relevant data.
    1:19:34 - Understanding TF-IDF for keyword relevance in search queries.
    1:21:39 - Understanding Inverse Document Frequency in TF-IDF for word importance.
    1:25:37 - BM25 improves retrieval with frequency saturation and document length normalization.
    1:27:45 - Document length normalization ensures fair importance across varying document lengths.
    1:32:09 - Dense retrieval models enhance relevance beyond keyword matching.
    1:34:05 - Dense retrieval models enhance search relevance by understanding meaning over exact word matches.
    1:38:13 - Embeddings in Dual Encoder Architecture enhance understanding of meaning in document retrieval.
    1:40:24 - DPR training uses positive and negative document-query pairs for relevance.
    1:44:21 - Generative models address limitations of retrieval models in processing queries.
    1:46:23 - Importance of clear and coherent responses in generative models.
    1:50:26 - Generative models build coherent responses word by word.
    1:52:26 - Generative models adapt responses based on user knowledge levels.
    1:56:26 - Understanding retrieval and generative steps in AI models.
    1:58:21 - Understanding photosynthesis through vector similarity matching.
    2:02:18 - Understanding the retrieval and generation process in GenAI.
    2:04:19 - Understanding RAG architecture enhances AI's response accuracy and clarity.
    2:08:11 - Understanding Retrieval-Augmented Generative Systems for accurate AI responses.
    2:10:09 - Understanding the RAG pipeline for effective information retrieval and generation.
    2:13:52 - Text chunks are converted into 1536-dimensional embeddings for better understanding.
    2:15:53 - Overview of the retrieval process in RAG systems.
    2:19:53 - Efficient operations for embedding and similarity search using PG Vector.
    2:22:39 - Introduction to essential libraries for GenAI integration.
    2:26:30 - Creating and storing embeddings using OpenAI and PostgreSQL.
    2:28:17 - Utilizing PG Vector for efficient question retrieval and embedding similarity.
    2:32:01 - Using prompts to enhance AI chat responses effectively.
    2:33:53 - Understanding randomness in model outputs through temperature adjustment.
    2:38:02 - Setting up the database is crucial for project functionality.
    2:40:00 - Setting up a database for document storage and categorization.
    2:44:04 - Overview of document and tax metadata storage in the database.
    2:45:48 - Establishing a many-to-many relationship between documents and tags.
    2:49:49 - Understanding document chunking and embeddings for efficient data storage.
    2:51:44 - Overview of generative models and database integration using SQL.
    2:55:31 - Overview of document deletion and chunking processes in AI.
    2:57:37 - Using AI to extract key facts from larger text chunks.
    3:01:21 - Handling retries and processing PDF chunks with AI.
    3:03:11 - Role of the system in generating and validating facts from user input.
    3:07:05 - Function extracts and matches semantic tags from documents asynchronously.
    3:09:04 - The process of tag matching and asynchronous API calls for document handling.
    3:12:58 - Extract and validate PDF text using API for generating matching tags.
    3:14:59 - Integrating document upload with tagging and chunking processes.
    3:19:02 - Splits long text into smaller, manageable chunks for processing.
    3:21:01 - PDF chunk processing involves creating asynchronous tasks for data extraction.
    3:25:18 - Understanding asynchronous task execution for efficient data processing.
    3:27:11 - Utilizing asynchronous methods for efficient data processing in GenAI.
    3:31:36 - Overview of document chunking and embedding in the RAG system.
    3:33:23 - Create an interactive Streamlit interface for PDF management.
    3:37:10 - Document management system allows uploads and deletions with user prompts.
    3:38:49 - Understanding the message class definition for chat applications.
    3:42:24 - Utilizing vector similarity for efficient document retrieval.
    3:44:26 - Document management for enhanced conversation input.
    3:49:19 - Managing message handling in a conversational AI system.
    3:51:20 - Managing references and messages in a chat application workflow.
    3:54:56 - Enhancing document analysis with multi-file support and machine learning.
    3:56:42 - Transform Lexi chat into a faster, smarter, production-ready system.
    4:00:13 - Automating PGI vectorizer for efficient database interaction and retrieval.
    4:01:55 - Setting up Python environment and Docker for GenAI project.
    4:05:47 - Install and set up PG Vector for enhanced embedding generation.
    4:07:39 - Challenges of previous document chunking and embedding processes.
    4:11:34 - Automated embedding creation enhances efficiency using OpenAI with PostgreSQL.
    4:13:25 - Automating document chunking and embedding with scheduled checks.
    4:17:02 - Utilizing PGI vectorizer for efficient data handling and storage.
    4:19:09 - Understanding optimization challenges in large datasets.
    4:22:47 - Utilizing disk-based storage enhances data organization and retrieval speed.
    4:24:36 - Efficient chunk retrieval enhances speed and accuracy in the RAG system.
    4:28:06 - PG Vector Scale optimizes SQL queries for faster data retrieval.
    4:30:00 - Optimizing database interactions reduces latency and errors in retrieval systems.
    4:33:43 - Using PGI for efficient data retrieval and response generation.
    4:35:37 - Understanding automatic chat completions and embedding creation.

  • @iMadHuB
    @iMadHuB 2 дні тому

    It's a crisp and concise that has everything needed to kickstart GenAI journey. Thanks for your time and efforts.

  • @vijaysonawane4071
    @vijaysonawane4071 15 годин тому +2

    02:23:58 Where is link of the form to get openai credits?

  • @abdrawing4660
    @abdrawing4660 День тому +1

    you should start from scratch database setup, pgvector installation and all

  • @AnkitYadav-dm7fp
    @AnkitYadav-dm7fp 9 днів тому +3

    ❤👍 just now thought of learning GenAI and here it is

  • @Jash-o7d
    @Jash-o7d День тому +2

    where can i find the ppt of the video . thanks for the course man

  • @user-gz6yn2sl6q
    @user-gz6yn2sl6q 9 днів тому +10

    It's true, from last 2 weeks I working to train llm model for conversation chat bot, but the training model is simple 10 minutes code, but it won't work as expected

  • @prasadjoshi007
    @prasadjoshi007 8 днів тому

    Thanks ayush for bringing such a quality content in such a chaos of sources in new tech AI , Gen AI

  • @vedanshbindal1121
    @vedanshbindal1121 9 днів тому +3

    Hi, I’m 25 years old and currently working as a Java backend developer with nearly 3 years of experience. I’m looking to transition into the field of AI development, specifically focusing on generative AI technologies. My goal is to build generative AI applications that solve real-world problems and create impactful solutions.
    That’s how I came across your UA-cam channel! I really admire your work, and I was hoping you could provide some guidance. From a career perspective, how should I begin this transition into AI and generative AI development?
    Your advice would mean a lot to me, and I’d be truly grateful for your help!

    • @bishwajeet_b_das
      @bishwajeet_b_das 9 днів тому +8

      Firstly Learn the Basic Mathematics(Linear algebra, calcus, statistics & probability ), Python(Your Java knowledge will help to transistion into different programming) because it has AI library(numpy, pandas and sckitlearn) to easy your hectic work, ML concept and understand the math behind in each of the concepts, DL algorithms and undertand the usecase of each algorithms, error reducing and validation.
      This is basic concept to move into GEN AI.
      then you can explore different LLM models, NLP, transformer etc....
      Statistical and Mathematical Foundations
      Probability and Statistics
      - Descriptive and inferential statistics
      - Probability distributions (Normal, Poisson, Binomial)
      - Hypothesis testing
      - Confidence intervals
      - Central Limit Theorem
      - Bayesian vs. Frequentist approach
      - A/B testing methodology
      Mathematics
      - Linear Algebra
      - Matrix operations
      - Eigenvectors and eigenvalues
      - Vector spaces
      - Calculus
      - Derivatives and gradients
      - Optimization techniques
      - Gradient Descent
      - Regularization techniques
      Machine Learning Algorithms:
      Supervised Learning
      - Linear Regression
      - Logistic Regression
      - Decision Trees
      - Random Forests
      - Gradient Boosting (XGBoost, LightGBM)
      - Support Vector Machines (SVM)
      - K-Nearest Neighbors (KNN)
      Unsupervised Learning
      - K-Means Clustering
      - Hierarchical Clustering
      - Principal Component Analysis (PCA)
      - t-SNE
      - DBSCAN
      Deep Learning
      - Neural Network architectures
      - Convolutional Neural Networks (CNN)
      - Recurrent Neural Networks (RNN)
      - Long Short-Term Memory (LSTM)
      - Transformer models
      - Basics of TensorFlow & PyTorch
      Python Programming Skills:
      Core Python:
      - Data structures (lists, dictionaries, sets)
      - List comprehensions
      - Lambda functions
      - Decorators
      - Error handling
      Data Science in Python:
      - NumPy
      - Array operations
      - Vectorization
      - Pandas
      - Data manipulation
      - Groupby operations
      - Merging and joining datasets
      - Scikit-learn
      - Model training and evaluation
      - Preprocessing techniques
      - Pipeline creation
      - Matplotlib & Seaborn for data visualization
      Practical Interview Preparation Tips:
      1. Build a strong portfolio on GitHub
      2. Practice coding on LeetCode and HackerRank
      3. Participate in Kaggle competitions
      4. Understand the business context of your models
      5. Practice explaining complex concepts simply
      6. Be prepared to discuss model selection, bias-variance tradeoff, and model evaluation metrics
      Key Interview Topics to Master:
      - Feature engineering
      - Model evaluation (precision, recall, F1-score)
      - Overfitting and underfitting
      - Cross-validation techniques
      - Handling imbalanced datasets
      - Model interpretability
      - Basic software engineering principles for data scientists

    • @Md_areef_uddin
      @Md_areef_uddin 9 днів тому

      i want a referal to join as a python developer so can anyone help me in this situation

    • @AjaySharma-dg7cc
      @AjaySharma-dg7cc 8 днів тому

      Same , i am also from java backend with 2.6 yrs and looking to transition in AI dev

    • @anjanab6052
      @anjanab6052 День тому

      @@AjaySharma-dg7cc This is the right time if you want to transition, develop some small projects in this domain and enrich your github. Next start applying for jobs with those experiences.

  • @muhammadijaz6042
    @muhammadijaz6042 7 днів тому

    Excellent conceptual teaching approach. 👍 adding timestamp to the video will be appreciated. Take care.

  • @KrinarChauhan
    @KrinarChauhan День тому +1

    For the beginners it is a little difficult to configure the environment I phase difficulties if possible can u guide us for the environment to set up

  • @TrendyWiz
    @TrendyWiz 8 днів тому +3

    Hey Ayush,
    I'm new to AI and ML, and I came across your course. Does this course cover everything needed to build AI/ML systems from scratch to a mastery level? Also, will it help me build real-world projects and systems by the end?

  • @poulomisen6095
    @poulomisen6095 17 годин тому +1

    Can someone help me with the notes ? I couldnt find the link

  • @rinzindorji4816
    @rinzindorji4816 День тому +1

    hardware requirements??

  • @shubhamsingh-il9nk
    @shubhamsingh-il9nk 8 днів тому +3

    Ayush can u tell the pre-requisites for this

    • @kishorthagunna7731
      @kishorthagunna7731 8 днів тому

      Its covers basics of NLP too. So Python will be requirement

  • @Learningenthusiast-td6eb
    @Learningenthusiast-td6eb 6 днів тому

    Bro just I advise plz!! How do you plan to learn new skill and be consistent throughout ??

  • @NishanthReddyEmmadi
    @NishanthReddyEmmadi 22 години тому

    can anyone share the docker compose file consisting of timsescaledb and pgai

  • @sakshamverma3114
    @sakshamverma3114 8 днів тому

    Can you turn on the transcript? It will help us in great way for making notes.

  • @abubacker737
    @abubacker737 7 днів тому

    Why the customised (task) chatbots were not working as expected ?

  • @akagi937
    @akagi937 8 днів тому

    great course and one thing I think with all those slides u multiple time explained it, made it a drag to get through, I think till rag it could be completed in 30-35 mins

  • @0xN1nja
    @0xN1nja 9 днів тому

    OG AYUSH IS BACK!

  • @Shankarpalya12345
    @Shankarpalya12345 8 днів тому

    can you provide the colab notebook you mention in the video

  • @abdullah.al.noman.innovation
    @abdullah.al.noman.innovation День тому +1

    Again here after year and a half. 7415D 02:07

  • @anjanprasad112-AP
    @anjanprasad112-AP 9 днів тому

    Could you please share the form for key credits??

  • @hemanth2102
    @hemanth2102 5 днів тому

    Slides /Notes kaha milega

  • @NitinVerma_234
    @NitinVerma_234 9 днів тому +1

    what i have to learn first ML or Gen AI or Python

    • @AyushSinghSh
      @AyushSinghSh  9 днів тому +1

      Start with genai basics :) see where you’re at

  • @Afzal3000
    @Afzal3000 9 днів тому

    Real Ayush is back💥✨

  • @gotmynerversblocked
    @gotmynerversblocked 9 днів тому +1

    will you give the python not4ebooks access?

  • @LegendJonny-bq6td
    @LegendJonny-bq6td 9 днів тому +1

    Bro, you once said writing three line of code in jupyter is not ML. Can you please explain a bit

    • @AyushSinghSh
      @AyushSinghSh  9 днів тому

      Yep, ML is more about making something work with explainable approach and it doesn't make sense if we just write it off within 3 lines of code. Consider checking out my course on Core ML on youtube for free.

    • @LegendJonny-bq6td
      @LegendJonny-bq6td 9 днів тому +1

      @AyushSinghSh so what should I actually do, because everywhere they do train() fit() and done.
      Btw, I am 13 years(learned python, libraries,linear algebra, probability and statistics,will learn ML next) just like you. And many people say that getting ML job as a fresher is not possible, then how did you get one

  • @pranavmittal9619
    @pranavmittal9619 9 днів тому

    Companies Asking Masters and Research Publications what to do for that as belonging from decent college get easily the job of avg 12lpa ????

  • @anshulkhandelwal8316
    @anshulkhandelwal8316 9 днів тому

    hey ayush!...can you please attach the ppt...and also the same codes of the notebooks

    • @AyushSinghSh
      @AyushSinghSh  9 днів тому +1

      Will make sure to attach them :)

  • @FinanciMasters
    @FinanciMasters 8 днів тому

    Is it for non technology person?

  • @AyeshaAyesha-j7y
    @AyeshaAyesha-j7y 3 дні тому

    where is form of free chatgpt api key

  • @akagi937
    @akagi937 8 днів тому

    a time stamp for each section would be nice

  • @adityamudugal
    @adityamudugal 8 днів тому

    Can this be done by a non tech people? Thank you so much! 😊❤

  • @ankitgarg7951
    @ankitgarg7951 8 днів тому

    Is this gen ai full course worth it for campus placements??

  • @Anonymus-ef7gu
    @Anonymus-ef7gu 9 днів тому +1

    When will you bring a course in hindi ?

  • @NotesandPens-ro9wx
    @NotesandPens-ro9wx 8 днів тому

    Bro where is time stamp?

  • @pranavmittal9619
    @pranavmittal9619 9 днів тому

    these 5 hours is worth it

  • @poulomisen6095
    @poulomisen6095 20 годин тому

    Thanks a lot Ayush

  • @jatinnsharmaa
    @jatinnsharmaa 2 дні тому

    ua-cam.com/video/IRrhpAXib-Y/v-deo.html what ui/tool are you using when showing us the table's data? I don't understand that. Can you put tool and explanation for that as well please.

  • @smartcricofficial7622
    @smartcricofficial7622 8 днів тому

    How old are you?

  • @monkemon307
    @monkemon307 9 днів тому

    bro wherewrer you

  • @ak-gi3eu
    @ak-gi3eu 6 днів тому

    This is like they added maths on english grammar

  • @gurumurthy4096
    @gurumurthy4096 9 днів тому +1

    💥✨

  • @Chadpritai
    @Chadpritai 8 днів тому

    😅put timestamps bro

  • @CSchief
    @CSchief 8 днів тому +1

    Put some chapter breaks bro

  • @aviguptaa
    @aviguptaa 8 днів тому

    ua-cam.com/video/NWONeJKn6kc/v-deo.htmlsi=LO62KmQ3RQZrfk0a ....is this video is the prerequisite??