🖥️ Download the free AI Agents PDFs: https://clickhubspot.com/f865c2

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Summary:
AI Agents seem overwhelming, but in 2026, we've gotten to the point that any non-technical person can create and manage their own AI agents to accomplish tasks. I cover everything simply: what an agent actually is, what to automate, how to start, and build two agents step by step using two of the leading platforms. Then dive into common pitfalls and how to avoid them. This is everything you need to get started with building AI agents in 2026, no coding required.

Chapters
0:00 Intro
0:50 What is an agent?
1:37 Where we're at right now
2:08 What to automate first
4:58 How to start
7:01 Time to build
7:24 Build 1
14:04 Build 2
20:43 More complex agents
22:03 Zapier vs n8n
22:40 Common Pitfalls (and how to avoid them)
24:44 The real skill

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Live n8n workflow link: https://n8n.srv1219917.hstgr.c....loud/form/2c68eba8-4

Get Gemini Nano Banana Pro API: https://ai.studio/

Sign up for free on Apify and get $5 worth of free credits: https://apify.com/
YT Transcript API: https://apify.com/pintostudio/youtube-transcript-scraper
POST Method API Endpoint: https://api.apify.com/v2/acts/....faVsWy9VTSNVIhWpR/ru

Sign up on Cloudinary: https://cloudinary.com/

This is the ultimate, no-code guide to building and deploying your first AI agent in under 30 minutes using N8N!

In this step-by-step tutorial, you'll learn how to connect the world's best AI models with a powerful no-code automation platform (N8N) to create an agent that works for you 24/7.

What you will build:
- A completely automated AI Agent that can consume a long piece of content, like a YouTube video (or article/document), and instantly process it.
- We'll specifically create an agent that summarizes a video and generates polished infographics from the summary, all automatically!

You will learn:
- The no-code stack for building reliable AI agents.
- How to connect N8N to different AI models and APIs.
- The complete process from concept to deployment.

No coding is required. If you want to get ahead in the AI revolution, this is an in-depth guide that can help you.

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CHAPTERS:-
00:00 Intro
00:24 Workflow
01:32 Setup Hostinger VPS
02:51 Creating nodes
04:18 On form submission
06:31 Extract YouTube transcript
10:35 Turn into Image
12:22 Prompt
15:30 Host image
17:46 Display Image
18:42 Publish workflow
20:52 Conclusion


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For more information about Stanford's online Artificial Intelligence programs visit: https://stanford.io/ai

To learn more about enrolling in this course visit: https://online.stanford.edu/co....urses/cs336-language

To follow along with the course schedule and syllabus visit: https://stanford-cs336.github.io/spring2025/

Percy Liang
Associate Professor of Computer Science
Director of Center for Research on Foundation Models (CRFM)

Tatsunori Hashimoto
Assistant Professor of Computer Science
For more information about Stanford's online Artificial Intelligence programs visit: https://stanford.io/ai

To learn more about enrolling in this course visit: https://online.stanford.edu/co....urses/cs336-language

To follow along with the course schedule and syllabus visit: https://stanford-cs336.github.io/spring2025/

Percy Liang
Associate Professor of Computer Science
Director of Center for Research on Foundation Models (CRFM)

Tatsunori Hashimoto
Assistant Professor of Computer Science

View the entire course playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

An overview of Deep Learning, including representation learning, families of neural networks and their applications, a first look inside a deep neural network, and many code examples and concepts from TensorFlow. This talk is part of a ML speaker series we recorded at home. You can find all the links from this video below. I hope this was helpful, and I'm looking forward to seeing you when we can get back to doing events in person. Thanks everyone!

Chapters:
0:00 - Intro and outline
1:42 - TensorFlow.js demos + discussion
3:58 - AI vs ML vs DL
7:55 - What’s representation learning?
8:40 - A cartoon neural network (more on this later)
9:20 - What features does a network see?
10:47 - The “deep” in “deep learning”
12:48 - Why tree-based models are still important
13:38 - How your workflow changes with DL
14:02 - A couple illustrative code examples
17:59 - What’s a hyperparameter?
19:44 - The skills that are important in ML
20:48 - An example of applied work in healthcare
21:58 - Families of neural networks + applications
28:55 - Encoder-decoders + more on representation learning
32:45 - Families of neural networks continued
35:50 - Are neural networks opaque?
38:29 - Building up from a neuron to a neural network
49:11 - A demo of representation learning in TF Playground
53:24 - Importance of activation functions
54:36 - What’s a neural network library?
58:43 - Overfitting and underfitting
1:02:38 - Autoencoders (and anomaly detection) screencast and demo
1:12:13 - Book recommendations

Here are three helpful classes you can check out to learn more:

Intro to Deep Learning from MIT → http://goo.gle/3sPj8To
MIT Deep Learning and Artificial Intelligence Lectures → https://goo.gle/3qh7H54
Convolutional Neural Networks for Visual Recognition from Stanford → http://goo.gle/3bbC34I

And here are all the links to demos and code from the video, in the order they appeared:

Face and hand tracking demos → http://goo.gle/2WTCwSc
Teachable machine demo → https://goo.gle/3bSCzCi
What features does a network see? → http://goo.gle/3e2zpA5
DeepDream tutorials → http://goo.gle/3bYIBTp and http://goo.gle/384B6JC
Hyperparameter tuning with Keras Tuner → http://goo.gle/2InBK7J
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs → http://goo.gle/309pMY5
Linear (and deep) regression tutorial → http://goo.gle/3sKxkN7
Image classification with a CNN tutorial → http://goo.gle/3qdD2Wb
Audio recognition tutorial → http://goo.gle/3kFpl1j
Transfer learning tutorial → http://goo.gle/3bV7D60
RNN tutorial (sentiment analysis / text classification) → http://goo.gle/3bVM1X7
RNN tutorial (text generation with Shakespeare) → http://goo.gle/3qmnrnz
Timeseries forecasting tutorial (weather) → http://goo.gle/3ecdYg9
Sketch RNN demo (draw together with a neural network) → http://goo.gle/3bbHTTy
Machine translation tutorial (English to Spanish) → http://goo.gle/3e7IJme
Image captioning tutorial → http://goo.gle/3sKFNQz
Autoencoders and anomaly detection tutorial → http://goo.gle/30aD0UA
GANs tutorial (Pix2Pix) → http://goo.gle/3kI1ZrB
A Deep Learning Approach to Antibiotic Discovery → https://goo.gle/3e7ivQD
Integrated gradients tutorial → http://goo.gle/2PxfRtq and http://goo.gle/3sE0bmq
TensorFlow Playground demos → http://goo.gle/2Px6rhB
Introduction to gradients and automatic differentiation → http://goo.gle/3sFVybo
Basic image classification tutorial → http://goo.gle/3c2AF3o
Overfitting and underfitting tutorial → http://goo.gle/3cdA9Qv
Keras early stopping callback → http://goo.gle/308XQUj
Interactive autoencoders demo (anomaly detection) → http://goo.gle/3kPfW7q
Deep Learning with Python, Second Edition → http://goo.gle/3qcQ5Y5
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition → http://goo.gle/386DKP4
Deep Learning book → http://goo.gle/3c2VQmd

Find Josh on Twitter → https://goo.gle/308Ve8P

Subscribe to TensorFlow → https://goo.gle/TensorFlow

Provides an intuitive explanation for what deep learning models are doing when they find an 'efficient feature representation'
Full course playlist here: https://www.youtube.com/playli....st?list=PLnrO0TOwDbu

NVIDIA announced its new CPU today, a collaboration with ARM yielding the "Grace" CPU. The company is also releasing new RTX GPUs (the A4000, A5000, A6000).
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We're already half-way through our initial new GN 'Volt' Large Modmat inventory. Grab one on the store if you want it this round: https://store.gamersnexus.net/....products/modmat-volt
You can also pick up an Original Modmat (also in stock & shipping) on the store: https://store.gamersnexus.net/products/modmat

NVIDIA's GTC 2021 conference saw the unveil of a series of new GPUs for professionals and data center applications, but all of these were overshadowed by NVIDIA's renewed interest in the CPU space. NVIDIA has been trying to purchase ARM since 2020 (currently pending regulatory approval), and in the meantime, the two organizations are working together to produce the new NVIDIA Grace CPU for data center and server applications. The NVIDIA Grace CPU aims to improve bandwidth of CPU-to-GPU communications and CPU-to-CPU communications, outperforming traditional PCIe signaling with NVIDIA's NVLink.

The GPUs announced are part of the former Quadro line, branding that NVIDIA is slowly moving away from. The A5000 and A4000 GPUs are in the high-end of Ampere RTX cards, with mobile versions also available in Max-Q laptops.

Separately from all of this, NVIDIA talked more about its Omniverse software and a few RTX games, including Black Myth: Wukong, Boundary, Narak: Bladepoint, and Bright Memory. Cyberpunk also got a comically short pseudo-reference.

Like our content? Please consider becoming our Patron to support us: http://www.patreon.com/gamersnexus

TIMESTAMPS

00:00 - NVIDIA GTC 2021 Announcements
01:03 - NVIDIA's New "Grace" ARM CPU
05:11 - New GPUs from NVIDIA: A4000, A5000
10:51 - A10, A16, & Omniverse

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Editorial, Host: Steve Burke
Editorial: Patrick Lathan
Video: Andrew Coleman

In this tutorial you will learn everything you need to fine tune (train) your GPT-2 Model. By training the model on specific texts you can improve the results quality by a large margin. Fine Tuning your GPT-2 Model is quite easy and can be done in a few simple steps.

First you need to download the repository from Github. After that you need to create a training.txt file including the data on which your model should be trained on. The next step is to convert the file into an npz format from which you can then train the model. After the GPT-2 model has been trained you can then copy the files into your final model and just run the sample code.


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Disclaimer:
All videos are for educational purposes and use them wisely. Any video might have inaccurate or outdated information. I give my best to research every topic thoroughly but please be aware that videos can contain mistakes.

In this video I show you how to setup and install GPT4All and create local chatbots with GPT4All and LangChain! Privacy concerns around sending customer and organizational data to OpenAI APIs is a huge issue in AI right now but using local models like GPT4All, LLaMa, Alpaca etc can be a viable alternative.

I did some research and figured out how to make your own version of ChatGPT trained on your own data (PDFs, docs etc) using only open source models like GPT4All which is based on GPT-J or LLaMa depending on the version you use. LangChain has great support for models like these so in this video we use LangChain to integrate LLaMa embeddings with GPT4All and a FAISS local vector database to store out documents.

My AI Consulting Services 👋🏼
Want to speak with me about your next big project? Book here:
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Find top shelf co-founders for your next AI venture:
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My Links 🔗
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👉🏻 Instagram: https://instagram.com/liamottley

Mentioned in the video:
Code: https://github.com/wombyz/gpt4....all_langchain_chatbo
GPT4ALL (Pre-Converted):
https://huggingface.co/mrgaang..../aira/blob/main/gpt4
Embedding Model:
https://huggingface.co/Pi3141/....alpaca-native-7B-ggm
Mac User Troubleshooting: https://docs.google.com/docume....nt/d/1JDMBTOjbRtJo49

Timestamps:
0:00 - What is GPT4All?
2:28 - Setup & Install
7:13 - LangChain + GPT4All
11:42 - Custom Knowledge Query
19:42 - Chatbot App
24:19 - My Thoughts on GPT4All

About Me 👋🏼
Hi! My name is Liam Ottley and I’m an AI developer and entrepreneur, founder of my own AI development company: Morningside AI (https://morningside.ai). You’ve found my YouTube channel where I help other entrepreneur’s wrap their heads around AI with tutorials on how to get started with building AI apps!

With developments in AI moving so fast, I’m constantly working on my own projects and those of clients and sharing my learnings with my viewers 2-3x per week.

I don’t have anything to sell you, my only hope is that one day some of you will learn enough from my videos to see an exciting opportunity in the AI space and want to work with my development company to make it a reality.

AI teaches itself to play flappy bird
huge thanks to Brilliant.org for sponsoring this video
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In this video, we delve into how ChatGPT, a powerful language model, can be a game-changer for interpreting and extracting insights from your data. From understanding its capabilities to implementing it in real-world scenarios, we've got you covered. Whether you're a seasoned data analyst or just getting started, join us to explore the fusion of AI and data analytics. Subscribe for in-depth tutorials and stay ahead in the world of data science!

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➡️ About Post Graduate Program In Data Analytics
This Data Analytics Program is ideal for all working professionals and prior programming knowledge is not required. It covers topics like data analysis, data visualization, regression techniques, and supervised learning in-depth via our applied learning model with live sessions by leading practitioners and industry projects.

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- Post Graduate Program certificate and Alumni Association membership
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- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Resume preparation and LinkedIn profile building
- 1:1 mock interview
- Career accelerator webinars

✅ Skills Covered
- Data Analytics
- Statistical Analysis using Excel
- Data Analysis Python and R
- Data Visualization Tableau and Power BI
- Linear and logistic regression modules
- Clustering using kmeans
- Supervised Learning

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🔥Edureka Data Science Master Program: https://www.edureka.co/masters....-program/data-scient
This Edureka session on Machine Learning with Tree-Based Models in Python will help you understand the Bagging & Boosting concepts in detail.

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

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#Edureka #DataScienceEdureka #MLTreeBasedModelsPython #MLForBeginners #ML #Python #DataScience #DataScienceUsingPython #learnDataScience #withMe
-------------------------------------

About the Masters Program

Edureka’s Machine Learning Engineer Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised Learning and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning.

The Master's Program Covers Topics LIke:

Python Programming
PySpark
HDFS
Spark SQL
Machine Learning Techniques and Artificial Intelligence Types
Tokenization
Named Entity Recognition
Lemmatization
Supervised Algorithms
Unsupervised Algorithms
Tensor Flow
Deep learning
Keras
Neural Networks
Bayesian and Markov’s Models
Inference
Decision Making
Bandit Algorithms
Bellman Equation
Policy Gradient Methods

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

Prerequisites

There are no prerequisites for enrolment to the Masters Program. However, as a goodwill gesture, Edureka offers a complimentary self-paced course in your LMS on SQL Essentials to brush up on your SQL Skills. This program is designed and developed for an aspirant planning to build a career in Machine Learning or an experienced professional working in the IT industry.

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

For online Data Science training write to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).

Don't forget to take the quiz at 04:22! Comment below what you think is the right answer, to be one of the 3 lucky winners who can win Amazon vouchers worth INR 500 or $10! (Depending on your location). What are you waiting for? Winners will be announced on Nov 13th, 2024.
In this video on Introduction to Generative AI video, you’ll join Emma, a graphic designer, as she learns about Generative AI—a type of AI that can create new things like images, text, music, and even videos. Emma explores tools like ChatGPT and DALL·E to see how they make original designs, stories, and more. We’ll break down what makes Generative AI different from other types of AI. Instead of just analyzing data, it creates something new based on patterns it learns from large datasets.

You’ll see how AI is used in:
Writing and Content: Tools that generate blog posts, stories, or essays from simple prompts.
Art and Design: AI that can make unique images based on text descriptions.
Music and Audio: How AI can compose music or even replicate voices.
Healthcare: AI models that help simulate medical data for research.

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We’ll also explain how Generative AI works in simple steps—from learning with large datasets to improving through feedback.

In just 5 minutes you'll learn
- What is Generative AI with an example
- Generative Ai Applications
- How does generative AI Work?
- How AI is simplifying complex tasks in everyday life.

At the end, there’s a quick quiz to test what you learned, so drop your answer in the comments below!
Chapters :
00:00 Introduction To Generative aI
00:22 What Is Generative AI?
01:05 Generative aI Applications
01:44 How Generative AI Works?
04:22 Quiz

✅ Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH

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#GenerativeAI #AI #GenAI #ArtificialIntelligence #simplilearn #2025

➡️ About Professional Certificate Program in Generative AI and Machine Learning

Dive into the future of AI with our Generative AI & Machine Learning course, in collaboration with E&ICT Academy, IIT Guwahati. Learn tools like ChatGPT, OpenAI, Hugging Face, Python, and more. Join masterclasses led by IITG faculty, engage in hands-on projects, and earn Executive Alumni Status.

Key Features:
✅ Program completion certificate from E&ICT Academy, IIT Guwahati
✅ Curriculum delivered in live virtual classes by seasoned industry experts
✅ Exposure to the latest AI advancements, such as generative AI, LLMs, and prompt engineering
✅ Interactive live-virtual masterclasses delivered by esteemed IIT Guwahati faculty
✅ Opportunity to earn an 'Executive Alumni Status' from E&ICT Academy, IIT Guwahati
✅ Eligibility for a campus immersion program organized at IIT Guwahati
✅ Exclusive hackathons and “ask-me-anything” sessions by IBM
✅ Certificates for IBM courses and industry masterclasses by IBM experts
✅ Practical learning through 25+ hands-on projects and 3 industry-oriented capstone projects
✅ Access to a wide array of AI tools such as ChatGPT, Hugging Face, DALL-E 2, Midjourney and more
✅ Simplilearn's JobAssist helps you get noticed by top hiring companies

Skills Covered:
✅ Generative AI
✅ Prompt Engineering
✅ Chatbot Development
✅ Supervised and Unsupervised Learning
✅ Model Training and Optimization
✅ Model Evaluation and Validation
✅ Ensemble Methods
✅ Deep Learning
✅ Natural Language Processing
✅ Computer Vision
✅ Reinforcement Learning
✅ Machine Learning Algorithms
✅ Speech Recognition
✅ Statistics

Learning Path:
✅ IITG AI: Program Induction
✅ IITG AI: Programming Fundamentals
✅ IITG AI: Python for Data Science (IBM)
✅ IITG AI: Applied Data Science with Python
✅ IITG AI: Machine Learning
✅ IITG AI: Deep Learning with TensorFlow (IBM)
✅ IITG AI: Deep Learning Specialization
✅ IITG AI: Essentials of Generative AI, Prompt Engineering & ChatGPT
✅ IITG AI: Advanced Generative AI
✅ IITG AI: Capstone
Electives:
✅ IITG AI: ADL & Computer Vision
✅ IITG AI: NLP and Speech Recognition
✅ IITG AI: Reinforcement Learning
✅ IITG AI: Academic Masterclass
✅ IITG AI: Industry Masterclass

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Devil may cry 3 all cutscenes full game movie.

devil may cry 3: Dante's Awakening, released in Japan as Devil May Cry 3 (Japanese: デビル メイ クライ 3 Hepburn: Debiru Mei Kurai Surī), is an action-adventure hack and slash video game directed by Hideaki Itsuno, developed and published by Capcom, released in 2005 for the PlayStation 2 and ported to Microsoft Windows in 2006. The game is a prequel to the original Devil May Cry, with a younger Dante.[3] It introduces new combat mechanics with an emphasis on combos and fast paced action. The story is told primarily in cutscenes using the game's engine, with several pre-rendered full motion videos.

Set a decade before the events of the first Devil May Cry in an enchanted tower, Temen-ni-gru, the story centers on the dysfunctional relationship between Dante and his brother Vergil.

Devil May Cry 3 was widely praised by critics, hailing it as a return to form for the series, yet did receive some criticisms for its high level of difficulty in the North American version. This was later addressed by the release of the rebalanced Special Edition of the game.[4][5]. The game was re‐released in 2006 as Devil May Cry 3: Dante’s Awakening, Special Edition (デビル メイ クライ 3 Special Edition) with retooled difficulty levels, the addition of mid-mission checkpoints, "GOLD mode", and Vergil as a playable character. Combined sales of both versions were over 2.3 million and garnering a review aggregate score of 88% on Gamerankings. It has been cited as one of the greatest video games of all time.

A 2005 manga prequel to Devil May Cry 3's storyline was published in Japan and later the United States.

Prologue Vergil - 00:00
Prologue Vergil - 2 01:36
Prologue - 04:05
Mission 1 Opening - 06:09
Mission 1 Break out - 10:25
Mission 2 Temen-ni-gru - 11:25
Mission 2 Chance Meeting - 12:08
Mission 3 Arkham & Vergil - 13:04
Mission 3 The Lady - 14:50
Mission 3 Cerberus - 15:20
Mission 3 Ice-chucks -16:36
Mission 3 First Contact - 17:31
Mission 4 The Uninvited - 18:43

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00:34:53 What is Scrum?
01:10:23 Agile Scrum Master
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02:50:05 Scrum vs Agile
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Fine-tuning GPT-3.
In this video we demonstrate how to use APIs and audio to create prompts and completions that can be used to fine-tune Transformers such as GPT-3. We show how to use a News API with Python to extract news articles and create a dataset for training and fine-tuning models.

We explain what prompt and completion datasets are, and the role they play in natural language processing in fine-tuning transformers. We then discuss using video and audio streams to build data pipelines for fine-tuning and transcribing speech to text using OpenAI's Whisper.

With over 500 hours of YouTube videos being uploaded every minute video is a rich source of detailed information with which to train AI.

The video first includes step-by-step demonstrations of using the News API to create a prompt and completion dataset and then shows how to build pipelines transcribing speech to text using Whisper.

Foundation models like GPT-3 can be 'Fine-Tuned'. This Fine_tuning allows them to learn the vocabulary and semantics of particular disciplines. If you want your AI solution to be familiar with options pricing algorithms, then you can show it lots of options pricing papers. If you'd like your AI to be able to converse and write about monetary policy then you can expose it to policy papers, books on economic theory, central bank meeting minutes and news conferences.

You train transformer AI models in a particular way. You don't simply feed in a file containing the text of the book or article that you are using to train the AI. You break the text up into a series of "Prompts" and "Completions". Prompts are the beginning of a passage of text and completions are what follow a prompt.

In this post we leverage the breadth and depth of content on YouTube. We propose a strategy to use selected videos to train our AI language models. These videos are chosen because they are rich in expertise in an area we want to train our language model in. The example used in this video is to train our AI in monetary policy by studying content generated by the Federal Reserve. We devise a strategy to use speech to text transcription of YouTube videos. We use OpenAI's 'Whisper' to perform this transcription. We then break this text up into 'prompts' and completions to train our model.

Following this approach we can easily train our NLP models to have expertise in any field we choose.



#NLP #Python #FineTuning #AI #MachineLearning #NewsAPI #Whisper #Lucidate #chatgpt #gpt3 #gpt-3 #openai #whisper #openai-whisper

Links:
Attention Mechanism in Transformers: https://youtu.be/sznZ78HquPc Transformer playlist: https://youtube.com/playlist?l....ist=PLaJCKi8Nk1hwaMU
Federal reserve Description: https://youtu.be/lJTmCv2kKKs
FOMC Feb 2023 Press Conference: https://www.youtube.com/watch?v=3Iv4aCN0OOo&t=2s
Python code for Transcriber class: https://github.com/mrspiggot/p....rompts_and_completio

=========================================================================
Link to introductory series on Neural networks:
Lucidate website: https://www.lucidate.co.uk/blog/categ...

YouTube: https://www.youtube.com/playlist?list...

Link to intro video on 'Backpropagation':
Lucidate website: https://www.lucidate.co.uk/post/intro...

YouTube: https://youtu.be/8UZgTNxuKzY

'Attention is all you need' paper - https://arxiv.org/pdf/1706.03762.pdf

=========================================================================
Transformers are a type of artificial intelligence (AI) used for natural language processing (NLP) tasks, such as translation and summarisation. They were introduced in 2017 by Google researchers, who sought to address the limitations of recurrent neural networks (RNNs), which had traditionally been used for NLP tasks. RNNs had difficulty parallelizing, and tended to suffer from the vanishing/exploding gradient problem, making it difficult to train them with long input sequences.

Transformers address these limitations by using self-attention, a mechanism which allows the model to selectively choose which parts of the input to pay attention to. This makes the model much easier to parallelize and eliminates the vanishing/exploding gradient problem.

Self-attention works by weighting the importance of different parts of the input, allowing the AI to focus on the most relevant information and better handle input sequences of varying lengths. This is accomplished through three matrices: Query (Q), Key (K) and Value (V). The Query matrix can be interpreted as the word for which attention is being calculated, while the Key matrix can be interpreted as the word to which attention is paid. The eigenvalues and eigenvectors of these matrices tend to be similar, and the product of these two matrices gives the attention score.

=========================================================================

#ai #deeplearning #chatgpt #gpt3 #neuralnetworks #attention #attentionisallyouneed

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In this Linux Administration Tutorial video, you will learn what is Linux, how to install Linux, how to setup kernel parameters, how to install & remove software, RPM in Linux, various commands in Linux, and various Linux services & systems in detail.

#LinuxAdministrationTutorial #LinuxTutorial #LinuxCourse #Linux #LinuxAdministration #LinuxForBeginners #Intellipaat #LinuxCommands #Intellipaat

The following topics are covered in this video:
00:00:00 - Linux Administration Tutorial
00:38:00 - Introduction to Linux
00:11:42 - Basics of Shell
00:17:47 - Top Shells in Linux
00:21:49 - Basics of Kernal
00:28:01- How to Install Linux?
00:29:39 - Quiz
00:59:54 - Quiz
01:17:22 - Basic Linux Commands
01:32:54 - Linux UI vs Terminal
01:37:14 - How to login to remote Linux?
01:41:46 - How to run a command as admin in Linux?
01:45:05 - Good to know commands in Linux
01:53:42 - Displaying - using Echo
02:00:08 - Set and unset a Variable
02:11:51 - Using Expr
02:17:15 - Header File of Shell Script - using Shebang (#!)
02:20:49 - Text Editors and Creating a File
02:25:24 - Hands-on: Creating and saving a file in Vim
02:28:21 - Understanding Linux File Permissions
02:47:17 - Chmod and Chown
03:00:41 - Displaying content in a File
03:11:46 - Unix Process Control Commands
03:34:41 - Linux Shell Scripting Tutorial
04:28:12 - Shell Scripting: Loops and Iterations
05:16:09 - Unix vs Linux
05:22:57 - Linux Interview Questions

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