In this beginner-friendly crash course, I’ll show you how to use real-world data with Python to create something cool from scratch.

Timeline:

00:00 - Getting Started
0:54 - What is Machine Learning
3:19 - Machine Learning Pipeline
7:18 - Popular ML Libraries & Tools
12:42 - Importing Data
20:17 - Jupyter Shortcuts
25:16 - A Real Machine Learning Problem
29:13 - Preparing the Data
32:34 - Learning & Predicting
37:28 - Calculating Model Accuracy
44:10 - Persisting Models
48:32 - Visualizing Decision Trees

Playlist Link: https://www.youtube.com/watch?v=rE9XJGakAUM&list=PLNIQLFWpQMRVKC_zohdgl3pjDk4rlN3jc

🚩 Subscribe ➜ https://bit.ly/45IwoxJ

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Was this video on Python using Machine Learning helpful? Let me know your thoughts in the comments.

#python #artificialintelligence #machinelearning

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

🔥Intellipaat AI Deep Learning course: https://intellipaat.com/artifi....cial-intelligence-ma
In this deep learning interview questions and answers you will learn the latest and top questions asked by companies for deep learning interview. This deep learning interview questions & answers video covers all kinds of questions starting from basic to advanced questions so that you can get benefited.
#DeepLearningInterviewQuestionsAndAnswers #AIandDeepLearningInterviewQuestions #DeepLearningInterviewQuestions #DeepLearningInterview #DeepLearning #MachineLearning #AIInterviewQuestions

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📝Following questions are covered in this deep learning video:
00:00 - Deep Learning Interview Questions And Answers
00:59 - What is the Difference between Machine Learning and Deep Learning?
02:16 - What is Perceptron?
03:26 - How is Deep Learning better than Machine Learning?
04:29 - What are some of the most used applications of Deep Learning?
05:27 - What is the meaning of Over fitting?
06:47 - What are Activation functions?
08:00 - Why is Fourier transform used in Deep Learning?
08:55 - What are the steps involved in training a perceptron in Deep learning?
09:47 - What is the use of the loss function?
10:30 - What are some of the Deep Learning Frameworks or tools that you have used?
11:49 - What is the use of the swish function?
12:38 - What are auto encoders?
13:41 - What are the steps to be followed to use the gradient descent algorithm?
14:57 - Differentiate between a single layer perceptron and a multi-layer perceptron
16:00 - What is data normalization in Deep Learning?
16:54 - What is forward propagation?
17:44 - What is back propagation?
18:40 - What are Hyper parameters in Deep Learning?
19:19 - How can hyper parameters be trained in neural networks?
21:38 - What is the meaning of dropout in Deep Learning?
22:42 - What are Tensors?
23:44 - What is the meaning of model capacity in Deep Learning?
24:33 - What is Boltzmann Machine?
25:25 - What are some of the advantages of using TensorFlow?
26:27 - What is the computational graph in Deep Learning?
27:40 - What is a CNN?
28:25 - What are the various layers present in a CNN?
30:19 - What is an RNN in Deep Learning?
31:15 - What is a Vanishing gradient when using RNNs?
32:11 - What is exploding gradient descent in Deep Learning?
33:10 - What is the use of LSTM?
34:04 - Where are autoencoders used?
35:05 - What are the types of auto encoders?
35:35 - What is a restricted Boltzmann Machine?
36:30 - What are some of the limitations of Deep Learning?
38:04 - What are the variants of gradient descent?
39:33 - Why is mini-batch gradient descent so popular?
40:35 - What are deep autoencoders?
41:47 - Why is the leaky ReLu function used in Deep Learning?
42:35 - What are some of the examples if the supervised learning algorithms in Deep Learning?
43:25 - What are some of the examples of unsupervised learning algorithms in Deep Learning?
43:56 - Can we initialize the weights of a network to start from zero?
45:00 - What is the meaning of valid padding and same padding in CNN?
46:16 - What are some of the applications of transfer learning in Deep Learning?
47:25 - How is the transformer architecture better than RNNs in Deep Learning?
48:41 - What are the steps involved in the working of an LSTM network?
50:07 - What are the elements in TensorFlow that are programmable?
50:43 - What is the meaning of bagging and boosting in Deep Learning?
51:52 - What are generative adversarial networks (GANs)?
53:00 - Have you earned any sort of Certification to improve your learning and implementation process?
----------------------------
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Emil Björnson explains the basics of supervised deep learning and two useful applications of it in the physical layer of communication systems.

If you want to learn more, you can read "Two Applications of Deep Learning in the Physical Layer of Communication Systems" by Emil Björnson and Pontus Giselsson (https://arxiv.org/pdf/2001.03350)

In Application 1, the following paper is used as an example:

Trinh Van Chien, Emil Björnson, Erik G. Larsson, “Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning,” IEEE International Conference on Communications (ICC), 2019. https://arxiv.org/pdf/1903.08163.pdf

In Application 2, the following paper is used as an example:

Özlem Tugfe Demir, Emil Björnson, “Channel Estimation in Massive MIMO under Hardware Non-Linearities: Bayesian Methods versus Deep Learning,” IEEE Open Journal of the Communications Society, 2020. https://arxiv.org/pdf/1911.07316.pdf

The GPT3 model from OpenAI is a new AI system that is surprising the world by its ability. This is a gentle and visual look at how it works under the hood -- including how the model is trained, and how it calculates its predictions.


Introduction & GPT-3 Demos (0:00)
GPT-3 Inputs and Outputs (2:06)
Training the GPT-3 model (2:48)
The scale of GPT-3 and its 175 billion parameters (6:37)
The order of GPT-3 token processing (7:58)
"Deep" learning: looking inside a layer stack (9:00)
Input prompts and priming examples (11:00)
Fine-tuning: the best is yet to come (11:56)


Twitter: https://twitter.com/JayAlammar
Blog: https://jalammar.github.io/
Mailing List: https://jayalammar.substack.com/

More videos by Jay:
Jay's Visual Intro to AI
https://www.youtube.com/watch?v=mSTCzNgDJy4

Making Money from AI by Predicting Sales - Jay's Intro to AI Part 2
https://www.youtube.com/watch?v=V4-lXSs3jrk

🔥Explore more about the Course here: https://www.simplilearn.com/general-management-certification-training-course?utm_campaign=26July2023-Webinar-IIM-Indore-General-Management&utm_medium=Descriptionff&utm_source=youtube
About the Webinar
IIM Indore invites you to join us for a career information session about general management. You’ll see how the Executive Certificate Program in General Management prepares you to advance in your management career in just six months. And you’ll receive an exclusive bonus just for attending the webinar!

What You’ll Find Out
Prof. Manoj Motiani from IIM Indore will share the following:

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About the Speaker
Seema Sahay

✅ Career Enhancement Coach and Author

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⏩ Check out the Business management training videos: https://www.youtube.com/watch?v=NjOa6l4GFJA&list=PLEiEAq2VkUUIk2HnEsN-xgruMOtVgJK5g

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About Executive Certificate Program In General Management:
With IIM Indore's General Management program, you will gain an understanding of domains including Operations, HR, Business Strategy, and digital disruption. Understand the fundamentals of how business works and leverage it to support your organization’s strategy. Move from a functional strategy role to a business strategy role with thorough knowledge of the business environment and the new fundamentals of business strategy with this general management program from IIM Indore.

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✅ Induction Session for IIM Indore General Management Program
✅ EC GM - Foundations of Management
✅ EC GM - Economics for Business
✅ EC GM - Accounting and Financial Management
✅ EC GM - Ops & SCM
✅ EC GM - Marketing Management
✅ EC GM - HR
✅ EC GM - Business Analytics
✅ EC GM - Management & Leadership
✅ EC GM - Business Strategy
✅ EC GM - Design Thinking
✅ EC GM - Digital Disruption & Innovation
✅ EC GM - Capstone Project

Skills Covered
✅ General Management
✅ Economics
✅ Accounting
✅ Financial Management
✅ Operations
✅ Supply Chain Management
✅ Marketing
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✅ Leadership
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✅ Design Thinking
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In this course, you will learn how to use Rust to create an authentication server. The course provides a comprehensive guide on user data management, structure, and token-based authentication. Special attention is given to error handling, JWT issues, and practical solutions for common challenges. Concluding with a hands-on demo, viewers will gain a thorough understanding of token creation and user management in modern applications

✏️ Course created by @AkhilSharmaTech

0:00:00 Introduction and setup
0:06:42 Admin and user authentication
0:15:53 User data handling and roles
0:28:54 Introduction to user structures and attributes
0:45:41 Token management and claims analysis
1:01:48 Deep dive into roles and tokens
1:05:03 Error handling and permissions
1:14:58 Advanced error insights and JWT issues
1:29:19 Code analysis and issue resolution
1:31:24 Final touches and RS file exploration
1:34:50 Demo review and token creation


🎉 Thanks to our Champion and Sponsor supporters:
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🔥Enroll for the UI / UX Design Course: https://intellipaat.com/ui-ux-design-course/

#UIUXDesignFullCourse #UIUXCourse #UIUXTraining #Intellipaat

This UI UX Design Full Course covers all the important skill sets required to get you a UI UX job!

We will begin this UI UX Course with an Introduction to UI UX Design. Then we will get introduced to Figma, how to do Brainstorming and Empathization in Figma, as well as how to use predefined templates and features from Figjam.

Next, we will cover How to use inspiration from the internet to create design, how to recognize UI patterns, Atomic Design principles, and much more about UI and UX technicalities as highlighted on the screen. Finally, we will help you understand how you can create UI Components in Figma as well as some of the important Figma PlugIns and their Use Cases.

This UI UX Course suits those who aspire to become UI UX Designers

🔵 Following topics are covered in this UI UX Design Full Course:
00:00:00 - Introduction to UI UX Course
00:03:15 - Introduction to Figma Products
00:37:46 - What is Affinity Map And How to Create?
00:49:46 - Collaborative Power
00:50:50 - Brainstorming & Empathization in Figma
01:09:23 - Using Predefined Templates and Features in FigJam
01:49:47 - Drawing Inspiration from the Internet
02:10:56 - Atomic Design Principle
02:28:38 - Principles of Good UI Design
02:47:20 - UI Design Styles
03:00:52 - What is a Wireframe?
03:07:30 - Jakob's Law
03:25:37 - Aesthetic Usability Effect
03:33:10 - Hick's Law
03:50:50 - Goal - Gradient Effect
03:57:45 - Miller's Law
04:08:26 - Serial Position Effect
04:11:25 - Von Restroff Effect
04:15:35 - Zeigarnik Effect
04:22:52 - Law of Common Region
04:24:58 - Gestalt Law of UX Designing
04:38:14 - UI Designing Principles
05:02:00 - Drawing Inspiration from the Internet
05:09:09 - Hands-on Session
07:30:03 - Components in Figma
07:56:42 - Figma Plugins & It’s Use Cases
10:31:46 - UI UX Designing Interview Questions And Answers

✅ What is a UI UX course?
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design and offers practical, skill-based instruction centered around a visual communications perspective, rather than one focused on marketing or programming alone.

✅What is the UI UX design course for?
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design and offers practical, skill-based instruction centered around a visual communications perspective, rather than one focused on marketing or programming alone.

➡ About the Course
Become a UI/UX professional by building a strong UX portfolio with our UI UX Course in just 6 months!

UI UX Design Course by iHUB, IIT Roorkee (An Innovation Hub of IIT Roorkee)
Learn UI/UX from eminent IIT Faculty & Industry Experts
Gain Generative AI skills in the UI UX Industry
2 Days Campus Immersion at IIT Roorkee

➡Who should take this course?
Web designers
Web developers
Product designers
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👉🏼50+ Live sessions across 6 months
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👉🏼1:1 Mock Interview

➡ What’s Covered in This Program? -
What’s Covered in This Program? -
✅ PART - 1 Introduction to UI/UX Design, design process, and user research
✅ PART - 2 Fundamentals of UI and UX design and how to Apply them
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✅ PART - 4 Getting Industry Ready

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This Introduction to Artificial Intelligence Full Course by Simplilearn provides a complete roadmap to mastering AI. It begins with an introduction to artificial intelligence, its future, and essential concepts like machine learning, deep learning, and generative AI. You’ll explore AI careers, the AI engineer roadmap, and how to land a job in this field. Dive into practical tutorials on using AI for coding, creating digital products, and building websites. Advanced topics include transformers, reinforcement learning, Q-learning, and LangChain. You’ll learn about AI bias, multimodal AI, and concepts like LLMs, benchmarking, and running models privately. The course also covers prompt engineering, data analytics with AI, and AI-powered tools like Cursor AI. Additionally, you’ll get hands-on with projects for beginners, including machine learning and AI, and explore top hackathons in India. Finally, you’ll gain insights into interview preparation and how to earn money using tools like ChatGPT.

00:00:00 Introduction To Artificial Intelligence Full Course
00:08:08 Introduction To Artificial Intelligence
00:17:14 Future Of Artificial Intelligence
00:24:13 Deep Learning Tutorial For Beginners
00:36:17 Best Artificial Intelligence Coding tools
00:45:55 Top 10 Technologies To Learn
00:52:37 Artificial Intelligence Careers Tutorial
01:04:31 How Create And Sell Digital Products Using AI
01:05:16 What Is Generative AI
01:15:48 Artificial Intelligence Engineer Roadmap
01:30:53 How To Get A Job In Artificial Intelligence
02:27:30 What Is Machine Learning
02:33:11 AI Bias
02:38:53 Open AI Strawberry
02:45:04 Difference between Artificial Intelligence, Machine Learning, And Deep Learning
02:46:11 Run Lama Privately
02:54:55 Introduction To LLM
03:08:24 LLM Benchmarkeing
03:14:01 Mutimodel Artificial Intelligence
03:33:52 Chatgpt Analyse Tutorial
03:34:53 Transformers In Artificial Intelligence
06:59:07 Reinforcement Learning
06:59:57 Q Learning
07:22:58 Langchain Explained
07:37:26 What Is Prompt Engineering
07:59:49 Data Analytics Using Artificial Intelligence
08:01:31 Cursor AI
10:17:41 Recurrent Neural Network
10:35:26 Machine Learning Projects For Beginners
11:01:07 Build Website With Artificial Intelligence
11:19:18 Confusion Matrix In Machine Learning
11:27:32 Top Hackathons In india
11:40:51 Artificial Intelligence Projects With Source Code For Beginers
11:46:00 Generative AI Tools For Job Interview
13:41:50 Artificial Intelligence and Deep learning Interview Questions
13:42:50 How To Earn Money Using ChatGPT

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#ArtificialIntelligenceFullCourse #AIFullCourse #AIAndMLFullCourse #MachineLearning #AIAndMachineLearning #ArtificialIntelligenceFullCourse #MachineLearningFullCourse #Simplilearn

➡️ About Post Graduate Program In AI And Machine Learning

✅ Key Features
- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's Job Assist helps you get noticed by top hiring companies

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🔥PGP in Generative AI and ML in collaboration with Illinois Tech: https://www.edureka.co/executi....ve-programs/pgp-gene
🔥Generative AI Course: Masters Program: https://www.edureka.co/masters....-program/generative- (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")

This *Artificial Intelligence Full Course* covers everything about AI, from basics like machine learning and neural networks to advanced topics in computer vision and natural language processing. It's designed for all levels, packed with explanations, coding demos, and real-world insights to make AI understandable and exciting for beginners and experienced enthusiasts alike.

00:00:00:00 Introduction
00:01:48:07 What is Artificial Intelligence?
00:08:29:09 Types Of Artificial Intelligence
00:18:43:02 AI vs Machine Learning vs Deep Learning
00:32:27:07 What is Deep Learning
00:43:39:18 What is LLM (Large Language Model)
01:00:09:22 What is Generative AI?
01:15:28:11 What is AI Ethics
01:24:04:29 What is Responsible AI
01:32:18:20 Artificial Intelligence with Python
03:11:14:03 Artificial Neural Network
03:43:02:21 Recurrent Neural Networks
04:11:54:12 Convolutional Neural Network
04:32:29:08 Introduction to TensorFlow
04:56:06:27 Prompt Engineering
05:15:31:10 Prompt Engineering For Code Generation
05:24:49:25 Building a Chatbot with Prompt Engineering
05:40:53:15 OpenAI o3-mini Model
05:48:04:16 What is Agentic AI?
05:59:09:11 Introduction to Midjourney
06:17:41:11 How to use Midjourney?
06:25:24:24 GitHub Copilot
06:42:43:18 What is Vibe Coding?
06:51:53:18 Top 5 AI Frameworks
07:06:38:25 How AI is Transforming Studio Ghibli-Style Animation?
07:12:05:17 AI in Web Development
07:37:16:23 AI in Healthcare
07:45:59:00 AI in Retail
07:52:07:10 AI in Automotive
08:02:02:10 AI for Marketing
08:14:38:04 AI for Business
08:20:06:03 AI in Finance
08:28:41:23 AI for HR
08:35:03:06 AI in Manufacturing
08:41:30:17 AI in Cybersecurity
08:51:19:09 AI for Startup
08:59:52:18 AI for Testing
09:05:18:12 AI for Ethical Hacking
09:10:12:11 AI on Microsoft Azure
09:22:37:15 AI and Industry 4.0
09:27:38:22 AI Engineer Learning Path
09:34:50:17 Top 15 AI Skills You Need to Know
09:43:10:28 Build AI Applicant Tracking System (ATS)
09:58:44:24 Artificial Intelligence Interview Questions & Answers

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📢📢Check out the latest 2025 video on Top 10 Technologies for the most up-to-date insights!
📌 Top 10 Technologies to Learn in 2025 → https://youtu.be/5kjWh8lBxC4

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain
📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka
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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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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/
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POST Method API Endpoint: https://api.apify.com/v2/acts/....faVsWy9VTSNVIhWpR/ru

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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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Join MarkitUpX Discord Server: https://discord.gg/fwSpTje4rh

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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✨ Hashtags ✨
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This demo shows how to run large AI models from #huggingface on a Single GPU without Out of Memory error. Take a OPT-175B or BLOOM-176B parameter model .These are large language models and often require very high processing machine or multi-GPU, but thanks to bitsandbytes, in just a few tweaks to your code, you can run these large models on single node.

In this tutorial, we'll see 3 Billion parameter BLOOM AI model (loaded from Hugging Face) and #LLM inference on Google Colab (Tesla T4) without OOM.

This is brilliant! Kudos to the team.

bitsandbytes - https://github.com/TimDettmers/bitsandbytes
Google Colab Notebook - https://colab.research.google.....com/drive/1qOjXfQIAU

In 2018 the kg will be defined by Planck's constant, not a hunk of metal.
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Special thanks to the staff at NIST who made this possible: Darine Haddad, Jon Pratt, Stephan Schlamminger, and Ben Stein.

Additional footage and animations by Sean Kelley, Jennifer Lauren Lee, and Frank Seifert.

I have been obsessed with measurement for a long time and I'm not sure quite how it happened. The world's roundest object played a role in this. I guess I'm just fascinated by how difficult it is to pin down a quantity like a kilogram. A physical object seemed like a good idea until the mass of the international prototype kilogram wasn't as constant as expected. These methods of the Kibble balance and silicon sphere have shown better precision than 20 parts per billion, making them superior to the old method. The agreement between Avogadro approaches

Special thanks to Patreon Supporters:
Tony Fadell, Donal Botkin, Jeff Straathof, Zach Mueller, Ron Neal, Nathan Hansen

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Interferometer video by TSG Physics: https://www.youtube.com/watch?v=j-u3IEgcTiQ

Music from http://epidemicsound.com "ExperiMental1" by Gunnar Johnsén

Studio filming by Raquel Nuno

Learn how to fine tuning LLM models. This course will teach you fine tuning using using QLORA and LORA, as well as Quantization using LLama2, Gradient and the Google Gemma model. This crash course includes both theoretical and practical instruction to help you understand how to perform fine tuning.

💻 Code: https://github.com/krishnaik06/Finetuning-LLM

✏️ Course developed by @krishnaik06

⌨️ (0:00:00) Introduction
⌨️ (0:01:39) Quantization Intuition
⌨️ (0:34:03) Lora And QLORA Indepth Intuition
⌨️ (0:56:26) Finetuning With LLama2
⌨️ (1:20:35) 1 bit LLM Indepth Intuition
⌨️ (1:37:33) Finetuning with Google Gemma Models
⌨️ (1:59:45) Building LLm Pipelines With No code
⌨️ (2:20:33) Fine tuning With Own Custom Data

🎉 Thanks to our Champion and Sponsor supporters:
👾 davthecoder
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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.

🔥Purdue - Applied Generative AI Specialization - https://www.simplilearn.com/applied-ai-course?utm_campaign=NRmAXDWJVnU&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Professional Certificate Program in Generative AI and Machine Learning - IITG (India Only) - https://www.simplilearn.com/iitg-generative-ai-machine-learning-program?utm_campaign=NRmAXDWJVnU&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Advanced Executive Program In Applied Generative AI - https://www.simplilearn.com/applied-generative-ai-course?utm_campaign=NRmAXDWJVnU&utm_medium=DescriptionFirstFold&utm_source=Youtube

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

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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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