I put three systems to the local LLM test.

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#machinelearning #llm #softwaredevelopment

Want to learn more? I’m hosting a 6-week live BootCamp for AI Builders.
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This is the 6th video in a series on using large language models (LLMs) in practice. Here, I review key aspects of developing a foundation LLM based on the development of models such as GPT-3, Llama, Falcon, and beyond.

More Resources:
▶️ Series Playlist: https://www.youtube.com/playli....st?list=PLz-ep5RbHos Read more: https://towardsdatascience.com..../how-to-build-an-llm

[1] BloombergGPT: https://arxiv.org/pdf/2303.17564.pdf
[2] Llama 2: https://ai.meta.com/research/p....ublications/llama-2-
[3] LLM Energy Costs: https://www.statista.com/stati....stics/1384401/energy
[4] arXiv:2005.14165 [cs.CL]
[5] Falcon 180b Blog: https://huggingface.co/blog/falcon-180b
[6] arXiv:2101.00027 [cs.CL]
[7] Alpaca Repo: https://github.com/gururise/AlpacaDataCleaned
[8] arXiv:2303.18223 [cs.CL]
[9] arXiv:2112.11446 [cs.CL]
[10] arXiv:1508.07909 [cs.CL]
[11] SentencePience: https://github.com/google/sent....encepiece/tree/maste
[12] Tokenizers Doc: https://huggingface.co/docs/tokenizers/quicktour
[13] arXiv:1706.03762 [cs.CL]
[14] Andrej Karpathy Lecture: https://www.youtube.com/watch?v=kCc8FmEb1nY&t=5307s
[15] Hugging Face NLP Course: https://huggingface.co/learn/n....lp-course/chapter1/7
[16] arXiv:1810.04805 [cs.CL]
[17] arXiv:1910.13461 [cs.CL]
[18] arXiv:1603.05027 [cs.CV]
[19] arXiv:1607.06450 [stat.ML]
[20] arXiv:1803.02155 [cs.CL]
[21] arXiv:2203.15556 [cs.CL]
[22] Trained with Mixed Precision Nvidia: https://docs.nvidia.com/deeple....arning/performance/m
[23] DeepSpeed Doc: https://www.deepspeed.ai/training/
[24] https://paperswithcode.com/method/weight-decay
[25] https://towardsdatascience.com..../what-is-gradient-cl
[26] arXiv:2001.08361 [cs.LG]
[27] arXiv:1803.05457 [cs.AI]
[28] arXiv:1905.07830 [cs.CL]
[29] arXiv:2009.03300 [cs.CY]
[30] arXiv:2109.07958 [cs.CL]
[31] https://huggingface.co/blog/ev....aluating-mmlu-leader
[32] https://www.cs.toronto.edu/~hi....nton/absps/JMLRdropo

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Intro - 0:00
How much does it cost? - 1:30
4 Key Steps - 3:55
Step 1: Data Curation - 4:19
1.1: Data Sources - 5:31
1.2: Data Diversity - 7:45
1.3: Data Preparation - 9:06
Step 2: Model Architecture (Transformers) - 13:17
2.1: 3 Types of Transformers - 15:13
2.2: Other Design Choices - 18:27
2.3: How big do I make it? - 22:45
Step 3: Training at Scale - 24:20
3.1: Training Stability - 26:52
3.2: Hyperparameters - 28:06
Step 4: Evaluation - 29:14
4.1: Multiple-choice Tasks - 30:22
4.2: Open-ended Tasks - 32:59
What's next? - 34:31

LLMs or Large Language Models are everywhere these days. But how do they work exactly? In this video, a deep-fake version of Ryan Gosling (created with ElevenLabs and SyncLabs) explains the high-level workings of an LLM like ChatGPT.

🔵TOPICS 🔵
0:42 What are Large Language Models (a.k.a. LLMs)
1:10 Well known commercial and open source examples of LLMs
1:41 The inner workings of an LLM
3:00 What embeddings are
6:34 LLMs compared to how our live is generated
7:43 What the acronym GPT stands for

🔵ATTRIBUTION 🔵
Ground Truth Video Used to Create this video
KEN Things Ryan Gosling Can't Live Without | GQ
https://youtu.be/aNJ_JPkbH2M?si=rPGZEJndw-JP_lMw

🔵FAIR USE 🔵
This video is for educational purposes and is in line with the guidelines for Fair Use
https://support.google.com/youtube/answer/9783148?hl=en&ref_topic=2778546

#lipsync #chatgpt #llm #llms #generativeai #voicecloning #claude3 #perplexityai #perplexity #autonomousagents #autogen #crewai #facefusion

Transformer Neural Networks are the heart of pretty much everything exciting in AI right now. ChatGPT, Google Translate and many other cool things, are based on Transformers. This StatQuest cuts through all the hype and shows you how a Transformer works, one-step-at-a time.

NOTE: If you're interested in learning more about Backpropagation, check out these 'Quests:
The Chain Rule: https://youtu.be/wl1myxrtQHQ
Gradient Descent: https://youtu.be/sDv4f4s2SB8
Backpropagation Main Ideas: https://youtu.be/IN2XmBhILt4
Backpropagation Details Part 1: https://youtu.be/iyn2zdALii8
Backpropagation Details Part 2: https://youtu.be/GKZoOHXGcLo

If you're interested in learning more about the SoftMax function, check out:
https://youtu.be/KpKog-L9veg

If you're interested in learning more about Word Embedding, check out: https://youtu.be/viZrOnJclY0

If you'd like to learn more about calculating similarities in the context of neural networks and the Dot Product, check out:
Cosine Similarity: https://youtu.be/e9U0QAFbfLI
Attention: https://youtu.be/PSs6nxngL6k

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0:00 Awesome song and introduction
1:26 Word Embedding
7:30 Positional Encoding
12:53 Self-Attention
23:37 Encoder and Decoder defined
23:53 Decoder Word Embedding
25:08 Decoder Positional Encoding
25:50 Transformers were designed for parallel computing
27:13 Decoder Self-Attention
27:59 Encoder-Decoder Attention
31:19 Decoding numbers into words
32:23 Decoding the second token
34:13 Extra stuff you can add to a Transformer

#StatQuest #Transformer #ChatGPT

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Oftentimes, GAI and RAG discussions are interconnected. Learn more about about RAG is and how it works alongside your databases, LLMs and vector databases for better results with Luv Aggarwal and Shawn Brennan.

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This Gen AI tutorial for beginners is sort of like a Gen AI mini-course where a person can start learning the fundamentals of Gen AI and in the end, we build two end-to-end projects. Below is the outline of the exact topics that are covered.

Code for project 1: https://github.com/codebasics/....langchain/tree/main/
Code for project 2: https://github.com/codebasics/....langchain/tree/main/
LangChain source code: https://codebasics.io/resource....s/langchain-crash-co
Vector database article: https://www.pinecone.io/learn/vector-database/

00:00 Overview
00:29 What is Gen AI or Generative AI?
01:23 Gen AI evolution
10:00 What is LLM (Large Language Model)?
13:55 Embeddings, Vector Database
21:24 Retrieval Augmented Generation
28:16 Tooling for Gen AI
29:14 Langchain Fundamentals
1:14:49 End-to-End Project 1: Equity Research Tool
2:28:25 End-to-End Project 2: Retail Q&A Tool

Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses.

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🔍 In this video, we unravel the layers of AI, Machine Learning, Deep Learning, and their applications in tools like #ChatGPT and Google #Bard

We first go through how AI is a broad field of study that encompasses #MachineLearning as a sub-field.

We then break down Machine Learning into supervised and unsupervised models, using real-world examples to illustrate their functions and differences.

We move deeper into Deep Learning: Learn about artificial neural networks and the power of semi-supervised learning in applications like fraud detection in banking.

Then we delve into Generative AI, differentiating it from discriminative models and demonstrating its capabilities in creating new, innovative outputs.

Finally we walk through Large Language Models (LLMs) and uncover the significance of LLMs in AI, their pre-training processes, and their customization for specific industry applications

*TIMESTAMPS*
00:00 Google’s AI Course in 10 Minutes
00:38 What is Artificial Intelligence?
01:27 What is Machine Learning?
03:28 What is Deep Learning?
05:15 What is Generative AI?
07:05 What are Large Language Models?

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Large language models usually give great answers, but because they're limited to the training data used to create the model. Over time they can become incomplete--or worse, generate answers that are just plain wrong. One way of improving the LLM results is called "retrieval-augmented generation" or RAG. In this video, IBM Senior Research Scientist Marina Danilevsky explains the LLM/RAG framework and how this combination delivers two big advantages, namely: the model gets the most up-to-date and trustworthy facts, and you can see where the model got its info, lending more credibility to what it generates.

Get weekly AI, cloud, security and sustainability industry news, events and insights. → https://ibm.biz/BdK6UY

If you’re interested in using Machine Learning and LLMs at your organization, or just want some insight into best practices, this one is for you!

In this live session with Machine Learning Upgrade author Kristen Kehrer, you’ll learn about key concepts like MLOps, LLMs, ML Infrastructure, and how all of these things can fit together in a holistic, end-to-end system.

You’ll leave with a better understanding of cutting edge Machine Learning tech and organizational best practices so you can help forge the future in data.

What You'll Learn:
1) How LLMs are being used in the world of unstructured data
2) How you can leverage MLOps techniques when building LLM-powered applications
3) Best practices for prompt writing, prompt testing, and evaluating LLMs

This session was part of our OPEN CAMPUS week in October, which included 6 days of live expert sessions.

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This is a 1 hour general-audience introduction to Large Language Models: the core technical component behind systems like ChatGPT, Claude, and Bard. What they are, where they are headed, comparisons and analogies to present-day operating systems, and some of the security-related challenges of this new computing paradigm.
As of November 2023 (this field moves fast!).

Context: This video is based on the slides of a talk I gave recently at the AI Security Summit. The talk was not recorded but a lot of people came to me after and told me they liked it. Seeing as I had already put in one long weekend of work to make the slides, I decided to just tune them a bit, record this round 2 of the talk and upload it here on YouTube. Pardon the random background, that's my hotel room during the thanksgiving break.

- Slides as PDF: https://drive.google.com/file/....d/1pxx_ZI7O-Nwl7ZLNk (42MB)
- Slides. as Keynote: https://drive.google.com/file/....d/1FPUpFMiCkMRKPFjhi (140MB)

Few things I wish I said (I'll add items here as they come up):
- The dreams and hallucinations do not get fixed with finetuning. Finetuning just "directs" the dreams into "helpful assistant dreams". Always be careful with what LLMs tell you, especially if they are telling you something from memory alone. That said, similar to a human, if the LLM used browsing or retrieval and the answer made its way into the "working memory" of its context window, you can trust the LLM a bit more to process that information into the final answer. But TLDR right now, do not trust what LLMs say or do. For example, in the tools section, I'd always recommend double-checking the math/code the LLM did.
- How does the LLM use a tool like the browser? It emits special words, e.g. |BROWSER|. When the code "above" that is inferencing the LLM detects these words it captures the output that follows, sends it off to a tool, comes back with the result and continues the generation. How does the LLM know to emit these special words? Finetuning datasets teach it how and when to browse, by example. And/or the instructions for tool use can also be automatically placed in the context window (in the “system message”).
- You might also enjoy my 2015 blog post "Unreasonable Effectiveness of Recurrent Neural Networks". The way we obtain base models today is pretty much identical on a high level, except the RNN is swapped for a Transformer. http://karpathy.github.io/2015..../05/21/rnn-effective
- What is in the run.c file? A bit more full-featured 1000-line version hre: https://github.com/karpathy/ll....ama2.c/blob/master/r

Chapters:
Part 1: LLMs
00:00:00 Intro: Large Language Model (LLM) talk
00:00:20 LLM Inference
00:04:17 LLM Training
00:08:58 LLM dreams
00:11:22 How do they work?
00:14:14 Finetuning into an Assistant
00:17:52 Summary so far
00:21:05 Appendix: Comparisons, Labeling docs, RLHF, Synthetic data, Leaderboard
Part 2: Future of LLMs
00:25:43 LLM Scaling Laws
00:27:43 Tool Use (Browser, Calculator, Interpreter, DALL-E)
00:33:32 Multimodality (Vision, Audio)
00:35:00 Thinking, System 1/2
00:38:02 Self-improvement, LLM AlphaGo
00:40:45 LLM Customization, GPTs store
00:42:15 LLM OS
Part 3: LLM Security
00:45:43 LLM Security Intro
00:46:14 Jailbreaks
00:51:30 Prompt Injection
00:56:23 Data poisoning
00:58:37 LLM Security conclusions
End
00:59:23 Outro

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Large Language Models (LLMs) and Generative AI intersect and they are both part of deep learning. Watch this video to learn about LLMs, including use cases, Prompt Tuning, and GenAI development tools.

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Large language models-- or LLMs --are a type of generative pretrained transformer (GPT) that can create human-like text and code. There's a lot of talk about GPTs and LLMs lately, but they've actually been around for years! In this video, Martin Keen briefly explains what a LLM is, how they relate to foundation models, and then covers how they work and how they can be used to address various business problems.

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdK65X

#llm #gpt #gpt3 #largelanguagemodel #watsonx #GenerativeAI #Foundationmodels

𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞 (Use Code𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/python-....programming-certific
This "Python Full Course" video provides a comprehensive overview of Python programming. This Python tutorial for beginners has foundational concepts such as variables and data types to advanced topics like functions, modules, and object-oriented programming. This Python for Beginners video offers a step-by-step approach with explanations, examples, and interactive exercises. It's the perfect starting point for individuals eager to dive into Python programming from scratch.

Below are the topics covered in this Python Full Course :
00:00:00 Introduction
00:01:50 Python Basics
00:03:56 Python Installation
00:09:43 Quiz Question
00:10:10 Basics of Programming
01:18:53 Quiz Question
01:19:22 Comments in Python
01:28:42 Quiz question
01:28:58 Operators in Python
01:52:03 Quiz Question
01:52:18 Tuple in Python
02:05:19 Quiz Question
02:05:37 Sets in Python
02:19:45 Quiz Question
02:20:03 Dictionary in Python
02:24:36 Quiz Question
02:34:54 Loops in Python
03:06:19 Quiz Question
03:06:39 Python Libraries - Numpy
03:20:51 Quiz Question
03:38:27 Python Pandas
04:40:47 Quiz Question
04:41:13 Python Matplotlib
05:08:14 Quiz Question
05:08:46 Python Functions
05:45:37 Quiz Question
05:46:06 Python Lamba Functions
06:05:52 Quiz Question
06:06:13 Arrays in Python
06:31:37 Quiz Question
06:32:01 Inheritance in Python
06:45:59 Quiz Question
06:46:20 Exception Handling
07:05:07 Quiz Question
07:05:30 Request in Python
07:20:06 Quiz Question
07:20:34 Date and time in Python
07:34:08 Python regular expression
08:15:25 Python File handling
08:38:16 Python database connections
09:03:22 Web Development frameworks
09:16:38 Python Web Development
09:36:20 Web scraping using Python
10:03:11 How to make a game in Python
10:31:01 Python Interview Questions

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📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

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Please write back to us at [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll-free) for more information.

🔥 Introduction to Generative AI: https://www.edureka.co/introduction-generative-ai?qId=67374d0f-63a8-491e-95e5-3c24926d8968&index_name=prod_search_result_courses&objId=2674&objPos=1
🔥 Generative AI course Masters programme : https://www.edureka.co/masters....-program/generative-
🔥 ChatGPT Training Course: Beginners to Advanced: https://www.edureka.co/openai-....chatgpt-training-cou

In this video, we dive into what NVIDIA is, exploring the company's innovative products. We’ll then cover NVIDIA’s latest news, highlighting recent announcements and advancements, followed by an exploration of core breakthroughs in technology that are driving industry-wide progress. Next, we’ll look at NVIDIA's newly released large language models, which are pushing the boundaries of AI applications, and wrap up with insights from the recent AI Summit, showcasing the latest in NVIDIA’s journey of AI leadership.

✅ 00:00 - Introduction
✅ 01:52 - What is Nvidia?
✅ 03:03- What is the news from Nvidia
✅ 04:10- What is the breakthrough all about?
✅ 04:58- Nvidia New LLM release
✅ 07:14 - What happened in AI Summit
✅ 07:53 - Nvidia worth around the world


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📝Feel free to share your comments below.📝

𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain
📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka
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📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/
📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN
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📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

- - - - - - - - - - - - - -

Why Become Generative AI Engineer?

Generative AI Engineer explores state-of-the-art technologies and make contributions to the field of artificial intelligence. It allows professionals to create models capable of producing authentic content, including images, text, and audio. This role is a combination of technical skills, continuous upskilling, and the prospect of implementing generative AI in various domains, ranging from art and design to healthcare and natural language processing.
- - - - - - - - - - - - - -

Why should I enroll in Generative AI Course?

We have created the Generative AI Certification Course after thorough research and recommendations from industry experts, along with hands-on experience of tools and systems used by industry experts. Edureka will be by your side throughout the learning journey—we're Ridiculously Committed.

Python
Data Science
Artificial Intelliegence
Natural Language Processing
Generative AI
Prompt Engineering
Large Language Models
ChatGPT, etc.

- - - - - - - - - - - - - -

For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US & Others: +18885487823 (toll-free)

🔥 Introduction to Generative AI: https://www.edureka.co/introduction-generative-ai
🔥 Prompt Engineering Course: https://www.edureka.co/prompt-....engineering-generati
🔥 ChatGPT Training Course: https://www.edureka.co/openai-....chatgpt-training-cou

In this video, we dive into *The Future of Generative AI* and the exciting *Job opportunities* it presents. We cover everything from what Generative AI is, its growing importance, and the key skills required to succeed in this field to how you can get started and the career paths emerging within the industry. We also discuss the question on many minds: Is Generative AI eating our jobs? Whether you're new to the field or looking to explore new career opportunities, this video will provide valuable insights into how Generative AI is shaping the job market and your future.

✅ 00:00 - The Future of Generative AI and Job Opportunities
✅ 01:16 - What is Generative AI?
✅ 02:26 - Importance of Generative AI
✅ 04:40 - Skills Required in Generative AI
✅ 06:07 - Getting Started in Generative AI
✅ 08:35 - Job Opportunities in Generative AI
✅ 09:44 - Is Generative AI Eating Our Jobs?

✅Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV

📝Feel free to share your comments below.📝

🔥 Mastering Generative AI Tools Online: https://www.edureka.co/masteri....ng-generative-ai-too
🔥 GitHub Copilot Training Course: https://www.edureka.co/github-....copilot-certificatio
🔥 Generative AI In Retail Certification Course: https://www.edureka.co/generat....ive-ai-retail-certif
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𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🌕 Java Online Training: http://bit.ly/3tRxghg
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🌕 Cloud Architect Masters Program: http://bit.ly/3OvueZy
🔵 Data Scientist Masters Program: http://bit.ly/3tUAOiT
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🔵 Machine Learning Engineer Masters Program: http://bit.ly/3AEq4c4
🌕 Business Intelligence Masters Program: http://bit.ly/3UZPqJz
🔵 Python Developer Masters Program: http://bit.ly/3EV6kDv


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🔵 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🌕 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh

🔵 Advanced Certificate Program in Cloud Computing with E&ICT Academy, IIT Guwahati: https://bit.ly/43vmME8

📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain
📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka
📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/
📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/
📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN
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📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

What is Generative AI?

Generative AI is a branch of artificial intelligence that focuses on producing or creating fresh content, such as images, text, audio, or even videos, that closely resembles data generated by humans. In contrast to conventional AI models that learn to identify patterns or make decisions based on existing data (discriminative models), generative AI models are trained to produce new data that mirrors the original training data.

What is Prompt Engineering?‎

Prompt engineering involves optimizing artificial intelligence engineering for multiple purposes. It includes refining large language models (LLMs) using specific prompts and recommended outputs. Additionally, it focuses on enhancing input to different generative AI services to make text or images.

What kinds of jobs can you get with Prompt Engineering skills?‎

Here are some potential job roles:
• Machine Learning Engineer
• Data Scientist
• Natural Language Processing (NLP) Engineer
• AI Research Scientist
• Software Engineer (AI/ML)
• Data Engineer
• Conversational AI Developer
• AI Product Manager
• AI Ethicist

For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US & Others: +18885487823 (toll-free)

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#generativeaiexamples

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