Dive into the world of Generative AI with this beginner-to-advanced course by Intellipaat. Learn everything from RNN and LSTM to advanced architectures like Transformers, BERT, LLMs, and LangChain with hands-on projects and real-world demos. Start with MCP Server, vibe coding, and RNN challenges, then explore Self-Attention, Encoder-Decoder architecture, and Multi-Head Attention. Master BERT, its training process, NSP, and implementation using Hugging Face.
Get practical with tokenization, classification models, and the use of tools like OpenAI, LangChain, RAG, vector databases, and effective prompting techniques. By the end, you’ll understand the complete AI workflow and be ready to build your own Generative AI applications.
📖 Below are the concepts covered in the video on 'Generative AI Full Course ': 00:00:00 - Introduction Generative AI Course 00:01:30 – What is MCP Server? 00:11:22 – Vibe Coding 00:21:56 – Recurrent Neural Networks (RNN) 00:31:45 – Problems in RNN 00:42:05 – What is LSTM? 00:46:38 – Putting It All Together 00:56:19 – RNN Model Hands-on 01:09:17 – Why Are Transformers Better Than LSTM? 01:09:56 – High-Level Overview 01:11:11 – Breaking the Black Box 01:12:28 – Expanding the Encoders and Decoders Block Further 01:13:52 – Self-Attention 01:16:52 – Self-Attention in Detail 01:25:51 – The Bigger Picture 01:34:24 – What is BERT? 01:37:10 – How BERT Works? 01:42:26 – BERT Uses Unsupervised Training Approach 01:48:17 – Hugging Face Small Example 01:53:22 – Transformer Hands-on 02:00:12 – BERT: Next Sentence Prediction (NSP) 02:02:33 – BERT Size and Architecture 02:13:56 – BERT Working 02:14:45 – Hands-on: Import Transformer 02:17:56 – Tokenizer Model Hands-on 02:39:55 – Binary Classification Using Logistic Regression 02:45:32 – Overview of Transformers 02:47:40 – Transformer Encoder and Decoder 02:50:36 – The Encoder Workflow 02:53:11 – Step 1: Input Embeddings 02:54:43 – Step 2: Positional Encoding 02:58:58 – Step 3: Stack of Encoder Layers 03:02:20 – Step 3.1: Multi-Headed Self-Attention Mechanism 03:08:56 – Step 3.2: Normalization and Residual Connection 03:13:11 – The Decoder Workflow 03:16:34 – Step 3.1: Masked Self-Attention Mechanism 03:17:40 – Step 3.2: Encoder-Decoder Multi-Head Attention (Cross-Attention) 03:18:47 – Output of the Decoder 03:32:13 – Transition to Transformer 03:58:53 – What is Hugging Face? 04:02:27 – Core of Hugging Face 04:07:05 – Models 04:09:24 – Datasets 04:11:15 – Spaces 04:12:32 – Open Source Libraries and Documentation 04:16:55 – Hugging Face Accounts and Tokens 04:40:33 – Hands-on: Hugging Face 05:21:16 – LLM Evolutionary Tree 05:38:51 – What is Generative AI? 05:50:14 – How to Use/Access Generative AI Models? 06:19:26 – OpenAI 06:32:22 – How to Access Models Like These 06:53:40 – LangChain Model 07:29:40 – What is the POTUS? 07:38:13 – Prompts 07:42:45 – Hands-on: LLM 08:48:31 – Retrieval Augmented Generation 08:56:38 – RAG Architecture 09:33:42 – Vector Database 09:58:26 – Prompting Techniques 11:28:47 – Core AI Workflow
➡️ About the Course Have you ever wondered how ChatGPT is trained to mimic human intelligence? Imagine you being able to do that. Our Generative AI and Prompt Engineering course is what you need. This Generative AI and Prompt Engineering course helps you master the language of AI and lets you design thoughtful prompts. You will be able to mitigate biases and deploy techniques for interaction with AI systems. Upon completion of this Generative AI and Prompt Engineering course you will create better prompts that help Gen AI models deliver better and more accurate results.
✅ Key Features - (Course Features) 👉🏼 35+ live sessions across 5 months 👉🏼 100+ hrs of top self-paced videos 👉🏼 50+ hrs project work and exercises 👉🏼 Top industry experts 👉🏼 1:1 session with industry mentors 👉🏼 Resume building & LinkedIn profile 👉🏼 24/7 support for query resolution 👉🏼 No-cost EMI option available now
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