How to Summarize a Book or a PDF file with GPT-3 and Python

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In today's video, we're going to be showing you how to use GPT-3 to easily summarize a book or paper in PDF form.

Let's face it, sometimes we don't have the time to read a 200 page, 73,000 word document from start to finish. But with GPT-3, you can quickly and easily extract the most important information and summarize it into just 230 words.

This can be a great way to create a step by step guide, research notes, blog posts, or even mid-journey prompts.

In this video, we'll be using the book "Deep Work" by Cal Newport as an example. This book is 190 pages and 73,000 words, so it can be a daunting task to get through it all.

But with the help of GPT-3 and a Python script with the OpenAI API, we'll show you how to automate the process and make summarizing a breeze.
So if you're ready to learn how to use GPT-3 to summarize a book, let's get started!

00:00 Intro Summarize a book or pdf with GPT-3
00:17 Summarizing with GPT-3 and Python Script
04:21 Deep Work Summary with Midjourney V4

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GPT-3 has taken the world by storm, and for good reason: It lets you generate text with just a few prompts (i.e. 'few-shot learning') or even with no prompts and just the instruction (i.e. 'zero-shot learning'). But there are scenarios where the model does not deliver satisfying results out of the box. In these cases we need to train the GPT-3 model with our own dataset to improve the performance. In this talk he explored how this can be done and we saw a real-life application of this.

This speech by Heiko Hotz was held on November 18 at Data Science Conference Europe 2022 in person in Belgrade.

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Change the World Through Data

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DISCLAIMER: This video is not medical, financial, or legal advice. This is just my personal story and research findings. Always consult a licensed professional.

I work to better myself and the rest of humanity.

Get ready for the future of AI with AutoGPT! In this mind-blowing video, we reveal the groundbreaking techniques to transform GPT-4 into a powerful, self-learning AI that's capable of revolutionizing the way we live, work, and play. 🤯
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We also discuss
-What is autogpt
-how to use autogpt
-autogpt vs chatgpt
-How does auto gpt work
-how to set up auto gpt
-How to turn gpt 4 into a powerful self learning autonomous ai
-some exmaples of auto gpt

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309 - Training your own Chatbot using GPT​

Code from this video is available on my GitHub page:
https://github.com/bnsreenu/py....thon_for_microscopis

Direct link to the code: https://github.com/bnsreenu/py....thon_for_microscopis/blob/master/309_Training_your_own_Chatbot_using_GPT%E2%80%8B.ipynb


This video tutorial explains how to implement a chatbot using the GPT-2 language model from Hugging Face's Transformers library.
https://huggingface.co/transfo....rmers/v2.2.0/pretrai

In this video, I will show you how to install PrivateGPT on your local computer. PrivateGPT uses LangChain to combine GPT4ALL and LlamaCppEmbeddeing for information retrieval from documents in different formats including PDF, TXT and CVS. The list of file types can be easily extended with PrivateGPT.

LINKS:
PrivateGPT GitHub: https://github.com/imartinez/privateGPT
Open Embeddings Video: https://youtu.be/ogEalPMUCSY
LLM Playlist: https://www.youtube.com/playli....st?list=PLVEEucA9MYh

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Do you want to see chatGPT coding Rasa rules and stories? In this second episode of the series on Generative AI, We covered, Auto-encoding, and Auto-Regressive models. What is Prompt Engineering with an Example, Drawbacks of GPT, and prompt injection. We also discussed hallucinations in GPT with a demo and training process of chatGPT. At last chatGPT codes Rasa rules and stories.

Links to Papers:
Attention Is All You Need: https://arxiv.org/abs/1706.03762
Training language models to follow instructions with human feedback : https://arxiv.org/abs/2203.02155

Learn more about Rasa: https://rasa.com
Rasa documentation: http://rasa.com/docs
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Chapters:-
0:00 Introduction
0:31 Papers
01:08 Language Models
1:22 Transformers
1:52 Auto-Encoding
2:18 Auto-Regressive
2:48 Hallucination in Generative Models
4:21 chatGPT
4:56 Prompt Engineering
5:18 Prompt engineering examples
6:06 chatGPT training steps
6:30 chatGPT training step-2
7:15 chatGPT training steps-3
7:43 chatGPT codes Rasa Rules and Stories
9:48 Prompt Injection


#generative #ai #chatgpt #chatbot #nlp #opensource

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In this "Digital Marketing Bootcamp 2023" by Simplilearn, we will cover topics on "Digital Marketing Bootcamp for Beginners." we will briefly explain what digital marketing is, the Business models in Digital Marketing, and Digital Marketing vs. Traditional Marketing. Then we'll move ahead with some of the essential topics like the Future of digital marketing, Google Ads Tutorial 2023, Email Marketing strategy, and Social media marketing. We will discuss the essential interview questions and answers of Digital Marketing to help everyone crack an interview.

In this Digital Marketing Bootcamp 2023, we will cover the following topics. If you are a beginner in digital marketing, this boot camp is the right choice.

00:00- Digital Marketing Bootcamp 2023
08:23-Top 10 Reasons to Learn Digital Marketing
14:02-What Is Digital Marketing?
20:11-customer lifecycle
15:00-Business Models in Digital Marketing
16:04-Digital Marketing vs Traditional Marketing
17:39-Future of Digital Marketing
19:12-Digital Marketing Salary
20:11-What Is Customer Lifecycle?
43:10-Types of Digital Marketing
48:50-What Is SEO?
55:24-keyword research
2:23:53-How to Create Your Google Business Profile
3:01:36-Google Ads BootCamp 2023
5:03:13-What Is Email Marketing?
5:59:40-What Is Social Media Marketing?
6:42:49-Facebook Ads BootCamp
7:46:31-What Is Content Marketing?
8:09:08-Google Analytics BootCamp
11:30:07-Top Digital Marketing IQ and Answers

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✅Digital marketing can be a valuable tool for businesses looking to reach new customers, increase sales, and establish their brand online.

-Therefore, businesses must have a solid digital marketing presence to stay competitive and effectively reach their target audience.



About Post Graduate Program In Digital Marketing:
This digital marketing course in India is meant for professionals looking to pursue a Digital Marketing career, know the fundamentals, and gain hands-on experience, and experienced marketers keen on learning the latest Digital Marketing skills. The Harvard Business case studies and projects on Google and Facebook add real-world experience.

✅ Key Features
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About Post Graduate Program in Digital Marketing Program:
The Post Graduate Program in Digital Marketing in partnership with Purdue University, will help accelerate your digital marketing career. In this digital marketing certification program, you’ll learn the latest tools, strategies, frameworks, and best practices in the field.This Post Graduate Program in Digital Marketing will help you gain a deep understanding of digital marketing strategies and expertise in the most widely used digital marketing tools and channels. Through real-life projects and virtual simulations, this digital marketing certification program will help you master various in-demand digital marketing skills.

Key Features:
✅ Purdue-Simplilearn Joint Post Graduate Program Certification
✅ Purdue Alumni Membership and Masterclasses
✅ Masterclasses from top Purdue instructors
✅ 200+ hours of applied learning
✅ Experiential learning through case studies and theme-based project work
✅ Get noticed by the top hiring companies
✅ Learn 35+ Digital Marketing tools

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In this video, we are going to discuss *Machine Learning Roadmap 2023* which contains topics to be covered for a machine learning engineer, what is the role of a machine learning engineer, and the salary of a machine learning engineer which will help you to understand the steps you need to take up to become Machine Learning Engineer.

✅ 00:00 - Machine Learning Roadmap 2023

✅ 00:35 - What is Machine Learning?
This phase of the video contains the definition of machine learning.

✅ 01:07 - Computer Science for Machine Learning
This part of the video contains the role of computer science for a machine learning engineer.

✅ 01:30 - Programming languages and algorithms for Machine Learning
This part of the video has details about the programming language needed for a machine learning engineer.

✅02:58 - Mathematics and Statistics
This phase of the video is given why the fundamentals of maths and statistics are needed for a machine learning engineer.

✅03:30 - Conclusion

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Machine learning, in general, is a branch of artificial intelligence that uses data patterns to generate judgments or predictions. It allows computers to adapt to repeating processes and results based on past performance without requiring them to be explicitly programmed in advance, as with standard algorithms.

In simple English, it basically allows robots to learn from, predict, and adjust to prior behavior. It is a method of generating artificial intelligence without needing to predetermine all the guidelines and procedures. Now that we have learned the basics of machine learning, it's time to focus on subjects that will help you to become a machine learning expert.

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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.

What skills will you learn from this Machine Learning course?

By the end of this Machine Learning course, you will be able to:
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2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems

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