[NEW] 2024:Mastering Generative AI-From LLMs to Applications

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1 - Introduction

1 - What is Generative AI.mp4

04:19

2 - What was before GENAI.mp4

03:02

3 - GEN AI TOOLS.mp4

08:52

4 - Better use of GEN AI.mp4

07:38

5 - GENAI USE CASE WRITING.mp4

09:59

6 - GEN AI Reading use cases.mp4

04:05

7 - gen AI Usecase chatting.mp4

08:51

8 - How to get Better Results from LLM.mp4

10:02

9 - Responsible AI.mp4

09:12

2 - LLM Shape size Resources needs

10 - Augmentation vs Automation.mp4

05:35

11 - The Kalpan Paper.mp4

07:01

12 - The Chinchilla Paper.mp4

13:39

13 - Transformers.mp4

23:29

3 - Generative AI LLM lifecycle

14 - GEN AI LIFE CYCLE.mp4

10:52

15 - RAG INTRO.mp4

04:14

16 - Fine tuning model intuition.mp4

11:27

17 - RLHF INTUTION.mp4

02:41

18 - Tools Agents.mp4

04:24

4 - Prompt Engineering set up and Prompt template

19 - Prompt Engineering Introduction.mp4

08:43

20 - LLM configuration parameters.mp4

02:10

20 - PROMPT-ENGINEERING.pdf

20 - Prompt-Enginnering-Complete-2-1.zip

21 - Lecture 2 Llama 2 vs Llama 2 chat.mp4

09:34

22 - Set up using Lamma 2.mp4

20:49

5 - LLM Properties

23 - Stateless LLMs.mp4

14:16

24 - Base LLM VS Fine Tuned LLM.mp4

06:57

25 - System Prompts.mp4

04:09

26 - Quantized models.mp4

18:17

27 - Quantized Models Notebook.mp4

26:26

28 - AWQ SETUP and usage of notebook.mp4

10:29

Files.zip

6 - Prompt Engineering Basic Guidelines

29 - Check Conditions assumptions.mp4

05:19

30 - Clear Instructions Delimiters.mp4

11:39

31 - Specific Output Structure.mp4

02:29

32 - Few Shot Prompting.mp4

05:02

33 - Give time to think.mp4

15:39

34 - Hallucination.mp4

07:01

7 - Better Prompting Techniques

35 - Iterative Prompting.mp4

03:42

35 - Prompt-Enginnering-Complete-2-1.zip

36 - Issues While summarizing.mp4

17:34

37 - summarize.mp4

10:36

38 - Inference.mp4

21:53

39 - Transformation.mp4

21:36

40 - Expanding.mp4

23:30

41 - Prompt Tuning.mp4

11:47

8 - Full Fine Tuning

42 - LLM FINE TUNING.mp4

11:23

43 - GLUE SUPER GLUE.mp4

04:48

44 - HELM.mp4

07:43

45 - LLM FINE TUNING Implementation.mp4

31:22

45 - fine-tune-Evaluate-PEFT-LORA-generative-ai-model-1-1.zip

9 - PEFT LORA

46 - PEFT.mp4

13:53

47 - QLORA.mp4

04:12

48 - PEFT Implementation.mp4

16:45

10 - RLHF

49 - PPO.mp4

09:41

50 - DPO VS ORPO.mp4

09:45

11 - RAG

51 - Using Langchain with Ollama to perform RAG with PDFs.mp4

34:52

52 - RAG With CSV File.mp4

21:31

Files.zip

12 - GEN AI for Vision up next

53 - Image prompt engineering.mp4

31:12

54 - Stable Diffusion.mp4

25:47

55 - Stable diffusion model train methods.mp4

19:00

56 - Stable Diffusion Resources.mp4

30:02

57 - FORGE setup.mp4

11:13

Description

LLM Lifecycle, Prompt Engineering, LLM Properties, Fine-tuning, PEFT LORA, RLHF, RAG, PPO,DPO,ORPO, AI for Vision

What You'll Learn?

LLAMA 2
CHATGPT
LARGE LANGUAGE MODEL
PROMPT ENGINEERING
LLM FINE TUNING
RAG
RLHF
LLM USE CASES
LLM BASICS
LLM FOR EVERYONE
LLM Based chatbot
chatbot
Instruction fine tuning
in context learning
few shot inference
hallucination
Reinforcement learning from human feedback
Retrieval Augmentation Generation
Tools for reasoning
Agents
Augmentation
Automation
Transformers
GEN-AI
GENERATIVE AI
ARTIFICIAL INTELLIGENCE
DATA SCIENCE
MACHINE LEARNING
DEEP LEARNING
LANGCHAIN
LAMMAINDEX
Low-Rank Adaptation
LORA
METRICS
PPO
DPO
ORPO
PDF RAG
CSV RAG
GEN AI Lifecycle

Who is this for?

DATA SCIENTISTS

ML Practitioners

What You Need to Know?

PYTHON

NLP

MACHINE LEARNING BASICS

More details

Description
Generative AI: From Fundamentals to Advanced Applications
This comprehensive course is designed to equip learners with a deep understanding of Generative AI, particularly focusing on Large Language Models (LLMs) and their applications. You will delve into the core concepts, practical implementation techniques, and ethical considerations surrounding this transformative technology.
What You Will Learn:
Foundational Knowledge: Grasp the evolution of AI, understand the core principles of Generative AI, and explore its diverse use cases.
LLM Architecture and Training: Gain insights into the architecture of LLMs, their training processes, and the factors influencing their performance.
Prompt Engineering: Master the art of crafting effective prompts to maximize LLM capabilities and overcome limitations.
Fine-Tuning and Optimization: Learn how to tailor LLMs to specific tasks through fine-tuning and explore techniques like PEFT and RLHF.
RAG and Real-World Applications: Discover how to integrate LLMs with external knowledge sources using Retrieval Augmented Generation (RAG) and explore practical applications.
Ethical Considerations: Understand the ethical implications of Generative AI and responsible AI practices.
By the end of this course, you will be equipped to build and deploy robust Generative AI solutions, addressing real-world challenges while adhering to ethical guidelines. Whether you are a data scientist, developer, or business professional, this course will provide you with the necessary skills to thrive in the era of Generative AI.
Course Structure:
The course is structured into 12 sections, covering a wide range of topics from foundational concepts to advanced techniques. Each section includes multiple lectures, providing a comprehensive learning experience.
Section 1: Introduction to Generative AI
Section 2: LLM Architecture and Resources
Section 3: Generative AI LLM Lifecycle
Section 4: Prompt Engineering Setup
Section 5: LLM Properties
Section 6: Prompt Engineering Basic Guidelines
Section 7: Better Prompting Techniques
Section 8: Full Fine Tuning
Section 9: PEFT - LORA
Section 10: RLHF
Section 11: RAG
Section 12: Generative AI for Vision (Preview)
Who this course is for:
DATA SCIENTISTS
ML Practitioners

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Instructor's Courses

I have done B.tech in Computer Science Engineering and 10 + years of experience as a professional instructor and trainer for Data Science and programming. During the course of my career I have developed a skill set in analyzing data and I love sharing my knowledge to help other people learn the power of programming, the ability to analyze data, as well as present the data in clear and beautiful visualizations.I am a Data Scientist and have experience in python, Deep learning, NLP and Big Data. I provide in-person data science, Machine Learning and Deep Learning training to Data science enthusiasts with 0 to 30+ years of Experience. I believe in learning by doing, hence all of my courses will give an in-depth knowledge of concepts followed by detailed explanations of codes, tips and tricks which I have learnt over years. The sample problems and examples will allow you to explore more and give you enough practice to gain confidence at each and every concept. I am here to help you stay on the cutting edge of Data Science and Technology.To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you!

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