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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This Edureka video on "Penetration Testing" will help you understand all about penetration testing, its methodologies, and tools. Below is the list of topics covered in this session:
00:00:00 Introduction
00:00:47 What is Penetration Testing?
00:03:44 Phases of Penetration Testing
00:07:32 Penetration Testing Types
00:12:49 Penetration Testing Tools
00:14:45 How to perform Penetration Testing on Kali Linux?

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React: Set State with this.setState


#100DaysOfCode

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐞𝐚𝐜𝐭 𝐉𝐒 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 : https://www.edureka.co/reactjs....-redux-certification (Use code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
This Edureka React tutorial on ES5 to ES6 Refactoring will help you understand the current syntax being used in React and what new features you can use in the upgraded version. This video helps you to learn the following topics:
00:00:00 Introduction
00:00:30 Agenda
00:01:45 Introduction to React components
00:02:45 Component Structure using ES5
00:03:46 Rendering a Component
00:05.48 Component Structure in Facebook
00:06:27 Component Structure using ES5
00:08:00 Pros and Cons of ES5 Syntax
00:09:02 Building Our Application
00:09:55 React Components using ES5 Code example
00:17:12 Benefits of ES6
00:19:38 Advantages of ES6
00:24:50 ES5 vs ES6
00:29:19 ES6 restructuring of code example
00:31:26 Building Tic Tac Toe game in React using ES6

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This video is a crash course on understanding how finetuning on LLM models can be performed uing QLORA,LORA, Quantization using LLama2, Gradient and Google Gemma model. This crash course includes both theoretical intuition and practical intuition on making you understand how we can perform finetuning.

Timestamp:

00:00:00 Introduction
00:01:20 Quantization Intuition
00:33:44 Lora And QLORA Indepth Intuition
00:56:07 Finetuning With LLama2
01:20:16 1 bit LLM Indepth Intuition
01:37:14 Finetuning with Google Gemma Models
01:59:26 Building LLm Pipelines With No code
02:20:14 Fine tuning With Own Cutom Data

Code Github: https://github.com/krishnaik06/Finetuning-LLM
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🔥Edureka Machine Learning Engineer Masters Program: https://www.edureka.co/masters....-program/machine-lea
This Edureka video on Machine Learning Python is a part of the Python Tutorial Series which will give you an introduction to Machine Learning. Python is a powerful language and is a widely used high-level language for general-purpose programming. With features like open-sourced, clear syntax, object-oriented, interpreted, and interactive programming language Python has become the number one choice when it comes to Machine learning. This Python Tutorial on Machine Learning Python also talks about how to implement machine learning algorithms in Python along with the topics like:
00:00 Introduction to Python Tutorial
00:54 Why Machine Learning with Python?
02:39 What is Machine Learning?
05:09 Types of Machine Learning
06:25 Supervised Learning
07:28 KNN algorithm
18:10 Unsupervised Learning
19:03 K-means Clustering Algorithm

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#edureka #pythonEdureka #machinelearningPython #Python #PythonTutorial #PythonMachineLearning #PythonTraining #machinelearningTutorial #machinelearning
------------------------------------------------------------------------

About the ML Master's Program

Edureka’s Machine Learning 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.

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

Why Learn Machine Learning?

Machine Learning is a concept that allows the machine to learn from examples and experience, and that too without being explicitly programmed. So instead of you writing the code, what you do is you feed data to the generic algorithm, and the algorithm/machine builds the logic based on the given data.
-------------------------------------

Edureka's Machine Learning Masters Program Curriculum

🟠 Live Courses 🟠
Python Programming Certification Course: https://www.edureka.co/python-....programming-certific
Machine Learning Certification Training using Python: https://www.edureka.co/machine....-learning-certificat
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Natural Language Processing with Python Certification: https://www.edureka.co/python-....natural-language-pro
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🟠 Elective Course 🟠
Python Scripting Certification Training: https://www.edureka.co/python-scripting

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

Prerequisites

There are no prerequisites for enrollment 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.

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

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

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐂𝐨𝐮𝐫𝐬𝐞 - 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 𝐭𝐨 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝: https://www.edureka.co/openai-....chatgpt-training-cou
In this Edureka video on 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥 𝐟𝐨𝐫 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬, you will learn how to use ChatGPT for coding and programming using different languages. ChatGPT can become a very. useful tool for every developer.
00:00:00 ChatGPT for Developers
00:00:40 What is ChatGPT
00:01:37 How ChatGPT Works
00:02:16 ChatGPT vs Codex
00:03:16 How to use ChatGPT
00:03:51 ChatGPT for Programming
00:04:06 ChatGPT for Shell Scripting
00:08:45 ChatGPT for Python Programming
00:10:47 ChatGPT for HTML and CSS Coding
00:12:10 ChatGPT for JavaScript

​Want to learn Microsoft Azure ASAP? Batch Starting Soon for Microsoft Azure. ENROLL NOW!! Click here to get started : https://bit.ly/3j7KaCi

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

If you'd like to support StatQuest, please consider...
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channe....l/UCtYLUTtgS3k1Fg4y5

...buying my book, a study guide, a t-shirt or hoodie, or a song from the StatQuest store...
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...or just donating to StatQuest!
paypal: https://www.paypal.me/statquest
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Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer

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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In 2024, becoming a Data Analyst is one of the smartest career choices you can make! With the demand for data-driven decisions soaring, companies like Google, Amazon, and IBM are on the lookout for skilled data analysts who can turn raw data into actionable insights. This role is future-proof, offering a perfect blend of technology and business. You don’t need to be a coding expert to start; mastering the basics of Python or R, along with tools like Tableau and Power BI, will set you on the right path.

In this video, we’ll walk you through everything you need to know to become a Data Analyst in 2024—from the essential skills and education to industry opportunities and salary expectations. Plus, you’ll get a glimpse into real-world applications of data analysis across various sectors, and how you can advance your career with continuous learning and specialization.
Following are the topics covered in this video:

00:00 - Introduction
01:45 - What does data analyst do
02:45 - How to become Data Analyst
03:30 - Skills
06:22 - Where do they work?
07:07 - How much do they make?
07:27 - How to learn in 2024
09:37 - Networking
11:07 - Job Outlook in 2024

✅Do I need a degree to become a Data Analyst?
Ans : While a degree in fields like statistics or computer science is helpful, practical experience and certifications can also open doors.

✅How much coding do I need to know?
Ans: Basic knowledge of Python or R is enough to get started, with tools like Tableau and Power BI minimizing the need for heavy coding.

✅What is the salary range for Data Analysts in 2024?
Starting salaries typically range from $60,000 to $100,000 or more, depending on experience and location.

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

✅ You can go through the slides here: https://www.slideshare.net/sli....deshow/how-to-become

⏩ Check out the Data Analytics Training Videos: https://youtube.com/playlist?l....ist=PLEiEAq2VkUUKnB4

✅ Know More about Simplilearn here: https://www.simplilearn.com/?utm_campaign=11Aug2024-How-To-Become-A-Data-Analyst-in-2024&utm_medium=Description&utm_source=youtube

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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.

✅ Key Features
- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 8X higher live interaction in live online classes by industry experts
- Capstone from 3 domains and 14+ Data Analytics Projects with Industry datasets from Google PlayStore, Lyft, World Bank etc.
- 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

✅ Enroll Course Now: https://www.simplilearn.com/pgp-data-analytics-certification-training-course?utm_campaign=11Aug2024-How-To-Become-A-Data-Analyst-in-2024&utm_medium=Description&utm_source=youtube

Grokking Machine Learning Book: https://www.manning.com/books/....grokking-machine-lea
40% discount promo code: serranoyt

A friendly introduction to the main algorithms of Machine Learning with examples.
No previous knowledge required.

What is Machine Learning: (0:05)
Linear Regression: (2:25)
Gradient Descent: (4:10)
Naive Bayes: (6:20)
Decision Trees: (10:35)
Logistic Regression: (13:20)
Neural networks: (17:00)
Support Vector Machines: (18:50)
Kernel trick: (20:05)
K-Means clustering: (26:00)
Hierarchical Clustering: (28:30)
Summary: (29:40)

(Thanks to Nick Kartha for breaking down the topics!)

If you like this, there's an extended version in this playlist:
https://www.youtube.com/playli....st?list=PLAwxTw4SYaP

https://github.com/aamini/introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning - aamini/introtodeeplearning

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Learn how to build production-ready AI agents using Google's Agent Development Kit in just 60 seconds! This tutorial covers LLM integration, custom Python tools, memory implementation, and natural language processing.

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