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Learn how to use Figma Variants to design a scalable mobile app. Variants can streamline your frontend design process by allowing you to group and organize similar components into a single container.

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🔗 Tutorial file link: https://www.figma.com/file/FrL....R3t9jZ9X8psPdmztdlh/
🔗 Homework file link: https://www.figma.com/file/8js....2feuv6LdhmvVtBXkC2L/

⌨️ (0:00:18) Intro
⌨️ (0:02:13) Accessing Tutorial and Homework Files
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In this video on Machine Learning with R, you will understand machine learning, applications of machine learning, and learn R programming. In this machine learning full course, you will also understand machine learning algorithms, such as linear regression, logistic regression, decision tree, and random forest to name a few.

Dataset Link - https://drive.google.com/drive..../folders/1DeoJO7NdoZ

The topics covered in this Machine Learning Tutorial video are:
00:00:00 Introduction
00:00:57 Machine Learning Basics
00:31:50 Machine Learning Applications
00:13:41 R programming
05:51:08 Linear Regression in R
06:20:14 Logistic Regression in R
06:38:20 Decision Tree in R
07:22:28 Random Forest in R
07:47:36 Support Vector Machine in R
08:24:41 Hierarchical Clustering in R
08:48:33 Time Series Analysis in R

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Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results.

R is a widely popular programming language in the field of machine learning. R is an open-source programming language used for statistical computing. It is one of the most popular programming languages today. R was inspired by S+, which is similar to the S programming language. R has various data structures and operators. It can be integrated with other programming languages like C, C++, Java, and Python.


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"🔥 Large Language Models (LLMs) Course with Generative AI : https://www.edureka.co/generative-ai-llms-course

In this video on ""What is LLM"" we'll dive into large language models (LLMs), powerful AI systems trained on massive text datasets to understand and generate human-like language. We'll introduce the concept of LLMs, explain how they leverage deep learning and self-attention mechanisms, and Discover examples of large language models like Google's LLaVA, OpenAI's GPT-3, and Microsoft's Turing-NLG, which power LLM's capabilities. We'll explore their impressive capabilities in tasks like text generation, summarization, question answering, and translation, as well as their real-world applications across industries.

In this informative video on What is LLM, we will cover the following Topics:

✅ 00:00 - Introduction to Large Language Model
✅ 01:03 - What is LLMs?
✅ 01:48 - Examples of LLMs
✅ 02:01 - Applications of LLMs
✅ 02:36 - How does LLM works?
✅ 03:57 - Understand Working of LLM With an Example

"
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𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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

About Large Language Models (LLMs) Course with Generative AI

Edureka's course on Large Language Models with Generative AI offers a transformative journey into the intricacies of LLMs. Delve deep into content generation and application development, gaining the expertise to create innovative solutions. Enroll now to become a trailblazer in this rapidly evolving domain.
- - - - - - - - - - - - - -

Who should go for this course?
This course on Large Language Models is suitable for data scientists, software developers, and AI researchers aiming to advance their expertise in natural language processing and utilize LLMs in their projects. Additionally, professionals in content creation, marketing, and customer service seeking to leverage LLMs for text generation and analysis can benefit from this course.

- - - - - - - - - - - - - -
What are the Prerequisites for this course?
The prerequisites for this course on Large Language Models include a basic understanding of machine learning concepts, proficiency in programming languages such as Python, and familiarity with natural language processing fundamentals. Additionally, knowledge of deep learning frameworks like TensorFlow or PyTorch would be beneficial for participants.

For more information, please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: +18338555775 (toll-free).
#Edureka #largelanguagemodel #llm #llmmachinelearning #generativeai #edurekacourse"

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An introduction to logistic regression and the perceptron algorithm that requires very little math (no calculus or linear algebra), only a visual mind.

0:00 Introduction
0:08 Series of 3 videos
0:41 E-mail spam classifier
7:19 Classification goal: split data
11:36 How to move a line
12:21 Rotating and translating
18:47 Perceptron Trick
23:20 Correctly and incorrectly classified points
24:20 Positive and negative regions
27:18 Perceptron Error
29:40 Gradient Descent
34:36 A friendly introduction to deep learning and neural networks
37:48 Activation function (sigmoid)
38:31 Log-Loss Error
41:37 Perceptron Algorithm
42:45 Logistic regression algorithm
44:48 Thank you!

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Learn everything about AI agents from scratch in this comprehensive tutorial. No prior knowledge required. We'll take you from zero to building production-ready AI systems with hands-on labs.

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• MCP (Model Context Protocol) - Connect AI to external tools

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05:56 - How LangChain works?
10:12 - Practice Labs - Your First AI API Call
14:57 - Practice Labs - LangChain
17:57 - Prompt Engineering Techniques
21:21 - Practice Labs - Master Prompt Engineering
24:46 - Vector Databases Deep Dive
31:27 - Practice Labs - Build Semantic Search Engine
35:15 - RAG (Retrieval Augmented Generation)
38:14 - Practice Labs - RAG Implementation
42:14 - LangGraph for AI Workflows
45:51 - Practice Labs - Build Stateful AI Workflow
48:51 - Model Context Protocol (MCP)
51:56 - Practice Labs - Advanced MCP Concepts
55:21 - Conclusion

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MIT 6.100L Introduction to CS and Programming using Python, Fall 2022
Instructor: Ana Bell

View the complete course: https://ocw.mit.edu/courses/6-....100l-introduction-to
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

This lecture discusses the core elements of programs: strings, input/output, f-strings, operators, branching, and indentation. Big idea: Debug early, debug often. Write a little and test a little. Don’t write a complete program at once. It introduces too many errors. Use the Python Tutor to step through code when you see something unexpected!

License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Support OCW at http://ow.ly/a1If50zVRlQ

We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.

This video introduces the syllabus and organization of the "Introduction to Deep Learning and Generative Modeling" course.

Link to the slides: https://sebastianraschka.com/p....df/lecture-notes/sta

-------

This video is part of my Introduction of Deep Learning course.

Next video: https://youtu.be/6VbtJ9nn5ng

The complete playlist: https://www.youtube.com/playli....st?list=PLTKMiZHVd_2

A handy overview page with links to the materials: https://sebastianraschka.com/b....log/2021/dl-course.h

-------

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Intro to Deep Learning - Part 1

In this video, I introduce the fundamentals of deep learning with a real-world scenario:
📌 Predicting how much it will rain tomorrow (precipitation).

Suitable for beginner to intermediate practitioners in machine learning and AI.

🔍 Topics Covered in Part 1:
1. Scenario: How can we predict precipitation?
2. Data-Driven Approach:
- Adding more variables (features)
- Nearest neighbor
- Mapping vectors to real numbers
3. Machine Learning:
- Random search

💡 Coming in Part 2:
3. Machine Learning:
- Derivative
- Gradient Descent
4. Conclusion and Summary