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

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Six Sigma gives you the tools and techniques to determine what’s making the manufacturing process slow down, how you can eliminate the delays, improve the process, and fix further issues along the way. It is a methodology that has seen worldwide adoption. This video also provides detailed examples on how each of the two Six Sigma methodologies work. The example we'll be considering is how a car manufacturing company is able to achieve its goals with the help of the Six Sigma methodologies, DMAIC and DMADV.

Don't forget to take the quiz at 07:25!
We'll be covering the below topics in this Six Sigma video:
1) What is Six Sigma? 00:33
2) History of Six Sigma 00:47
3) Six Sigma methodologies and examples 01:26
4) Quiz 07:23

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About Simplilearn SIx Sigma green belt course:
This Lean Six Sigma Green Belt course provides an overview of Six Sigma and the DMAIC methodology and is aligned to the leading Green Belt certifications at ASQ and IASSC. In this Lean Six Sigma Green Belt course, you will learn how to measure current performance to identify process issues and how to formulate solutions.

Six Sigma Green Belt Training Key Features:
- 56 hours of high-quality blended learning
- 33 PDUs offered
- 4 simulation test papers, 4 real-life projects
- Aligned to ASQ and IASSC

Eligibility:
Lean Six Sigma professionals are in high demand due to their ability to use problem-solving techniques to reach business solutions and assuring quality control throughout the process. The Lean Six Sigma Green Belt certification is ideal for Quality system managers, Quality engineers, Quality supervisors, Quality analysts and managers, Quality auditors, and any individual wishing to improve quality and process within an organization.

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This Edureka session on Python OOPS concepts (Python OOPs Blog: http://bit.ly/2JlVhFJ​) will help you understand Python Classes and Objects with examples. It will also explain the concept of OOPS in Python.
Topics Covered
00:00:00 Introduction
00:00:27 Why do we need functions?
00:01:53 What are Functions?
00:02:42 Hands-on
00:39:50 Object Oriented concepts
00:40:37 Python OOP is different than other OOP's
00:42:47 Use Case

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

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

Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang

Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department & Electrical Engineering Department
https://profiles.stanford.edu/dorsa-sadigh

To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.....io/autumn2021/#sched

0:00 Introduction
0:06 Machine learning backpropagation
0:18 Motivation: regression with four-layer neural networks
2:09 Computation graphs
3:26 Functions as boxes
6:11 Basic building blocks
8:40 Function composition
10:02 Linear classification with hinge loss
13:45 Two-layer neural networks
24:17 A note on optimization
25:55 How to train neural networks
29:21 Summary

I'm going to show you GPT3 playground and how I built my very first GPT3 based application. GPT3 is amazing and this tutorial will take you into the magic and give you the basics of prompt design and examples of how the tech can be applied in python.

I've made the Google Colab and Github repos available here:

Google Colab notebook example:
https://colab.research.google.....com/drive/1TyuTvjs0h

Github Cbot command line example
https://github.com/graiz/cbot

Blog post with additional details:
https://gregraiz.com/building-....a-command-line-appli

How do you get an API key?
You have to apply for a key on OpenAI: https://beta.openai.com/
If you're waiting you can play around with GPT2. I have a video on how that works here: https://www.youtube.com/watch?v=R6KoIp1ETpM

Follow me on twitter http://www.twitter.com/graiz
I also have a blog and discord channel if you want to connect with me:
https://www.gregraiz.com

- GPT 3 demo of how to use the Open AI playground
- GPT 3 demo of using Google Colab with OpenAI
- GPT 3 demo of exporting an app to Python
- GPT 3 demo some of the problems with Open AI
- GPT3 demo of command line auto-complete from English to shell commands

The entire experience of building a GPT3 application is incredibly easy. That being said there are a lot of limits on what GPT3 currently does.

The core of the GPT3 experience is a single screen called the playground, this is where you can send commands, adjust the sliders and settings and see responses. The rest of the interface is example prompts that fill-in data from the playground and documentation.

The core of GPT3 is that it's a very sophisticated auto-complete. So when designing a prompt or playing with the playground, you want to give it as much context to complete your text in a meaningful way.

After playing around in the playground, I wanted to build an actual app that I could use on a regular basis. I've been trying to use the command line more to keep my hands on the keyboard and keep from dragging/dropping files around but I would often forget the more obscure commands.

I decided to build a bot that would help me find those commands based on simple english querries like.
- How do you install a program
- How do you change permissions on a file
- How do you copy files or pretty much anything else.

I started in the playground and got a very basic version working. OpenAi gives you tools to export the code and I did that into Google Collab. This is an interactive way to run/test and edit python. This was my first python program I've written so the interactive nature was helpful in playing around with basic concepts and getting the bare bones of the program running.

From there I was able to copy the code to my computer and get it working even better. I made this utility that I call cbot, I've made it open source and available to try. Unfortunately you do need an OpenAI API key to use it. This is unfortunate because it makes it difficult for developers to create and share open source tools that leverage OpenAI.

So far cbot has been really useful for looking up obscure commands and allows me to stay in the flow rather than jumping around the web trying to look up commands. My big takeaways from building my first GPT3 application:

1 - Getting started is incredibly easy and a lot of fun. I played around with how AI could be applied to ordering systems, explaining medicines in plain english to patients, grammar correction, and authoring Seinfeld fan-fiction scripts. I'm excited because there's a lot of interesting applications and the technology is very accessible.
2 - It's not magic and it's not perfect. It would often go off on a tangent and occasionally get stuck in a loop. There are controls to adjust how it will perform, but tweeking these controls seems to be an art, not a science.
3 - Developers should think of OpenAI as infrastructure. Similar to AWS, or Azure, it's providing pieces of functionality for your product/application. It's also a dependency and a point of failure. If you're starting a business using GPT3 you need to consider your long-term advantage and moat for your company because application of this technology will become common place.
4 - After playing with GPT3, I'm less concerned about the tech becoming self-aware and taking over the world. It's really impressive but it still has a long way to go. As I said before, the PT in GPT stands for pre-training and in the current version you're not yet able to upload large sets of data to fine tune the model. This is something that OpenAI is working on but it's got a way to go.
5 - Lastly - We are at the very start of something really big. If someone with no prior python experience can whip together an app that harnesses the collective knowledge of the web, just imagine where the technology can go.

Recently, I have trained GPT-2 on the Lakh MIDI dataset. It is a collection of roundabout 175K MIDI files. Training takes quite a long time! But using NVIDIA DGX-2 really made a difference!

The unprocessed Lakh MIDI dataset:
https://colinraffel.com/projects/lmd/

Checkout NVIDIA DGX-2:
https://www.nvidia.com/en-us/data-center/dgx-2/

Checkout the NVIDIA DGX Systems family:
https://www.nvidia.com/en-us/d....ata-center/dgx-syste


Hexagon Machine - New Order MMXXI:
https://bfan.link/new-order-mmxxi

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Say Hello on LinkedIn:
https://www.linkedin.com/in/dr....-tristan-behrens-734

More on my Patreon account:
https://www.patreon.com/computational_creativity

I am excited to bring you a comprehensive step-by-step guide on how to fine-tune the newly announced MPT-7B parameters model using just a single GPU. This remarkable model is not only open source but also commercializable, making it a valuable tool for a wide range of natural language processing (NLP) tasks. MPT token size beat GPT4 and also it outperformed many available language models like GPT-J, LLAMA and etc.

Don't forget to subscribe to our channel and hit the notification bell to stay updated with the latest tutorials and developments in the field of AI. Let's dive in and empower your AI projects with the limitless potential of MPT-7B!

Other commercializable LLM: https://github.com/eugeneyan/open-llms
Notebook: https://github.com/antecessor/mpt_7b_fine_tuning

People have witnessed supernovae for millennia, but what threat do they pose to life on Earth? This video is sponsored by Brilliant. You can get started for free, or the first 200 people to sign up via https://brilliant.org/veritasium get 20% off a yearly subscription.

▀▀▀
A massive thanks to Prof. Hans-Thomas Janka for helping us with the physics of supernovae and GRBs. A massive thanks to Prof. Brian Thomas for all of his help with the terrestrial effects of supernovae and GRBs. This video would not have been possible without them. Also thanks to Dr. Luke Barnes for his initial help with the literature search.

Hydrogen bomb vs Supernova fact was taken from this great article by xkcd/Randall Munroe – https://what-if.xkcd.com/73/ (based on the calculation by Andrew Karam, 2002)

Cosmic bubble footage from
https://www.cfa.harvard.edu/ne....ws/1000-light-year-w

Neutrino driven SN explosion simulations from https://iopscience.iop.org/art....icle/10.1088/2041-82

▀▀▀
References:

Melott, A. et al. (2019). Hypothesis: Muon radiation dose and marine megafaunal extinction at the End-Pliocene supernova. Astrobiology, 19(6), 825-830. – https://ve42.co/Melott1

Thomas, B. C. et al. (2016). Terrestrial effects of nearby supernovae in the early Pleistocene. The Astrophysical Journal Letters, 826(1), L3 – https://ve42.co/Thomas1

Melott, A. L., & Thomas, B. C. (2019). From cosmic explosions to terrestrial fires?. The Journal of Geology, 127(4), 475-481. – https://ve42.co/Melott2

Fields, B. et al. (2019). Near-Earth supernova explosions: Evidence, implications, and opportunities. arXiv preprint arXiv:1903.04589. – https://ve42.co/Fields1

Thomas, B. C., Atri, D., & Melott, A. L. (2021). Gamma-ray bursts: not so much deadlier than we thought. Monthly Notices of the Royal Astronomical Society, 500(2), 1970-1973. – https://ve42.co/Thomas2

Melott, A. et al. (2004). Did a gamma-ray burst initiate the late Ordovician mass extinction?. International Journal of Astrobiology, 3(1), 55-61. – https://ve42.co/Melott3

Firestone, R. B. (2014). Observation of 23 supernovae that exploded less than 300 pc from Earth during the past 300 kyr. The Astrophysical Journal, 789(1), 29. – https://ve42.co/firestone1

Janka, H. T. (2017). Neutrino emission from supernovae. arXiv preprint arXiv:1702.08713. – https://ve42.co/Janka1

Janka, H. T., & Hillebrandt, W. (1989). Neutrino emission from type II supernovae-an analysis of the spectra. Astronomy and astrophysics, 224, 49-56. – https://ve42.co/Janka2

Janka, H. T. (2017). Neutrino-driven explosions. arXiv preprint arXiv:1702.08825. – https://ve42.co/Janka3

Karam, P. A. (2002). Gamma and neutrino radiation dose from gamma ray bursts and nearby supernovae. Health physics, 82(4), 491-499. – https://ve42.co/Karam1

Melott, A. L., Thomas, et al.. (2017). A supernova at 50 pc: effects on the Earth's atmosphere and biota. The Astrophysical Journal, 840(2), 105. – https://ve42.co/Melott4

Ludwig, P., et al. (2016). Time-resolved 2-million-year-old supernova activity discovered in Earth’s microfossil record. Proceedings of the National Academy of Sciences, 113(33), 9232-9237. – https://ve42.co/Ludwig1

Gritschneder, et al. (2011). The supernova triggered formation and enrichment of our solar system. The Astrophysical Journal, 745(1), 22. – https://ve42.co/Gritschneder1

Motizuki, Y., Takahashi, et al. (2009). An Antarctic ice core recording both supernovae and solar cycles. arXiv preprint arXiv:0902.3446. – https://ve42.co/Motizuki

Zucker, C. et al. (2022). Star formation near the Sun is driven by expansion of the Local Bubble. Nature, 601(7893), 334-337. – https://ve42.co/Zucker1

Hirata, K. et al.(1987). Observation of a neutrino burst from the supernova SN1987A. – https://ve42.co/Hirata1

Hayes, L. A., & Gallagher, P. T. (2022). A Significant Sudden Ionospheric Disturbance Associated with Gamma-Ray Burst GRB 221009A. Research Notes of the AAS, 6(10), 222.

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James Sanger, Louis Lebbos, Elliot Miller, Brian Busbee, Jerome Barakos M.D., Amadeo Bee, TTST, Balkrishna Heroor, Chris LaClair, John H. Austin, Jr., OnlineBookClub.org, Matthew Gonzalez, Eric Sexton, John Kiehl, Diffbot, Gnare, Dave Kircher, Burt Humburg, Blake Byers, Evgeny Skvortsov, Meekay, Bill Linder, Paul Peijzel, Josh Hibschman, Mac Malkawi, Mike Schneider, John Bauer, jim buckmaster, Juan Benet, Sunil Nagaraj, Richard Sundvall, Lee Redden, Stephen Wilcox, Marinus Kuivenhoven, Michael Krugman, Cy 'kkm' K'Nelson, Sam Lutfi

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Written by Petr Lebedev & Derek Muller
Edited by Fabio Albertelli
Animation by Fabio Albertelli, Jakub Misiek, Alex Drakoulis, Ivy Tello, Mike Radjabov, and Charlie Davies
Filmed by Derek Muller
Additional Research by Kovi Rose & Katie Barnshaw
Video/photos supplied by NASA, ESA, Pond5, and Getty Images
Music from Epidemic Sound & Jonny Hyman
Produced by Derek Muller, Petr Lebedev, and Emily Zhang

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In this video on large language models, we’ll take a look at Large Language Models (LLMs) and understand what they are all about. First, we’ll explain what LLMs are and why they’re becoming so important in the world of technology. You’ll learn what these models do and how they’re used in areas like healthcare, finance, and customer service. We’ll also talk about how large language models are trained. These models use huge amounts of data to learn and create human-like text. We’ll break down how they work, from understanding language to predicting what comes next in a sentence.
Lastly, we’ll explore where LLMs are used today, such as in chatbots, content creation, and translations. These models are helping businesses work smarter and faster.

00:00 Introduction to LLMs
02:17 What are large language models?
03:22 How LLMs are trained?
05:05 How LLMsWork?
05:48 Applications Of LLMs

What is a large language model?
A large language model (LLM) is a type of artificial intelligence (AI) program that can recognize and generate text, among other tasks. LLMs are trained on huge sets of data — hence the name "large." LLMs are built on machine learning: specifically, a type of neural network called a transformer model.

Is ChatGPT a large language model?
The reason is that Large Language Models like ChatGPT are actually trained in phases. Phases of LLM training: (1) Pre-Training, (2) Instruction Fine-Tuning, (3) Reinforcement from Human Feedback (RLHF).

What are LLM examples?
For example, LLMs could be used to create personalized education or healthcare plans, leading to better patient and student outcomes. LLMs can be used to help businesses and governments make better decisions by analyzing large amounts of data and generating insights.

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➡️ About Post Graduate Program In AI And Machine Learning

This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

✅ Key Features

- Post Graduate Program certificate and Alumni Association membership
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➡️ About Caltech Post Graduate Program In AI And Machine Learning

Designed to boost your career as an AI and ML professional, this program showcases Caltech CTME's excellence and IBM's industry prowess. The artificial intelligence course covers key concepts like Statistics, Data Science with Python, Machine Learning, Deep Learning, NLP, and Reinforcement Learning through an interactive learning model with live sessions.

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- Simplilearn's JobAssist helps you get noticed by top hiring companies
- PGP AI & ML completion certificate from Caltech CTME
- Masterclasses delivered by distinguished Caltech faculty and IBM experts
- Caltech CTME Circle Membership
- Earn up to 22 CEUs from Caltech CTME
- Online convocation by Caltech CTME Program Director
- IBM certificates for IBM courses
- Access to hackathons and Ask Me Anything sessions from IBM
- 25+ hands-on projects from the likes of Twitter, Mercedes Benz, Uber, and many more
- Seamless access to integrated labs
- Capstone projects in 3 domains
- 8X higher interaction in live online classes by industry experts

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- Statistics
- Python
- Supervised Learning
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- Speech Recognition
- Ensemble Learning
- Computer Vision

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The Generative AI Full Course 2025 by Simplilearn starts with an introduction to Generative AI, covering beginner tutorials and the AI career landscape in 2025. It delves into the Gen AI engineer roadmap, the top AI technologies, and Deepseek R1. Learners explore deep learning, GANs, transformers, and LLMs, along with Search GPT and Langchain. The course provides a strong foundation in machine learning, reinforcement learning, and benchmarking LLMs. Practical applications include ChatGPT analysis, OpenAI Sora, and Hugging Face tutorials. The course concludes with AI tools for job interviews, Agentic AI, Google Quantum AI, and deep learning interview questions.

Following are the topics covered in the Gen AI Full Course 2025:

00:00:00 Introduction to Gen AI Full Course 2025
00:04:37 What is Gen AI ?
00:05:22 Generative AI Tutorial For Beginners
00:16:24 AI Careers in 2025
00:24:33 Gen AI Engineer Roadmap
00:33:40 Top 10 AI Technologies in 2025
00:57:46 Introduction to Deepseek r1
01:04:45 Deep Learning Tutorial
01:13:02 What is Search GPT
01:15:17 Langchain Tutorial
02:11:52 Generative Adversarial Tutorial
02:21:14 What are GANS?
02:22:15 Transformers in AI
02:34:44 LSTM Tutorial
02:36:08 Introduction to LLM
03:37:21 What is Machine Learning?
04:33:53 Machine Learning Tutorial
06:39:16 Reinforcement Learning
06:59:07 ChatGPT Analyze
07:10:03 Openai sora
07:18:56 LLM Benchmarking
08:09:39 Hugging Face Tutorial
08:25:03 OpenAI ChatGPT o1 model
08:30:12 Gen AI Tools for Job Interview
09:07:14 Agentic AI Tutorial
09:15:37 Google Quantum AI
11:15:58 Deep learning Interview questions and Answers

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#genai #generativeai #artificialintelligence #ai #machinelearning #llm #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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Dr. Raj Dandekar (MIT PhD) conducted a 7-hour small language model workshop. This is part 2 of that workshop.

In this, we cover the following four aspects:

1. 00:00 Recap of part 1: data pre-processing, tokenization, input-output pairs
2. 10:55 Building blocks of the SLM architecture
3 01:08:34 Attention Mechanism Explained

If you want to get access to the detailed lecture notes, register here: https://vizuara.ai/courses/bui....ld-slm-from-scratch-

In this video I show you examples of how to perform transfer learning in various ways, either having trained a model yourself, using keras.applications or the TensorFlow hub. I also show how you can then pick out specific chunks of the pretrained models and remove specific layers, and then how you could freeze those layers, add layers on top and perform fine tuning.

Download TensorFlow Hub (w. pip it's pip install tensorflow-hub):
https://anaconda.org/conda-forge/tensorflow-hub

I learned a lot and was inspired to make these TensorFlow videos by the TensorFlow Specialization on Coursera. Below you'll find both affiliate and non-affiliate links, the pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
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GitHub Repository:
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TensorFlow Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐒𝐞𝐥𝐞𝐧𝐢𝐮𝐦 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: https://www.edureka.co/seleniu....m-certification-trai code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
This video by Edureka helps you understand how to handle multiple windows when you are testing an application using Selenium. Following are the topics covered in this video:
00:00:00 Introduction
00:01:22 What is Selenium Webdriver?
00:03:29 Basic pre-requisites to run the test cases
00:04:05 Handling multiple windows in Selenium
00:05:29 Demo

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain
📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka
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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.

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

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In this What Are Transformers In Artificial Intelligence tutorial, we'll delve into the world of Transformers in AI. We'll unravel the intricate workings of these powerful models, from their foundational architecture to their applications in natural language processing, computer vision, and beyond. By the end, you'll grasp not only how Transformers operate but also why they've become a cornerstone in modern AI research and development. So, buckle up as we embark on this enlightening exploration through the realm of Transformers in AI.

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⏩ Check out More AI Videos By Simplilearn: https://youtube.com/playlist?l....ist=PLEiEAq2VkUULyr_

➡️ About Post Graduate Program In AI And Machine Learning

This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

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- Post Graduate Program certificate and Alumni Association membership
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- Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more
- Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools

✅ Skills Covered

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- Generative AI
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In this video we will cover the *Project management knowledge areas.* The 10 knowledge areas of project management includes - Project integration management, Project scope management, Project time management, Project cost Management, Project quality management, Project human resources management, Project communication management, Project risk management, Project stakeholder management and more.
✅ Why get PMP certified - Obtaining a PMP certification expands job opportunities in project management. Roles like project manager, program manager, and PMO manager become accessible across industries such as IT, construction, healthcare, finance, and manufacturing. PMP-certified professionals earn competitive salaries, with a 20% higher pay compared to non-certified project managers. In India, PMP salaries range from INR 12 lakh to INR 17 lakh per annum, while in the US, it's between $52,561 and $132,121 per year. Boost your career prospects by enhancing your project management expertise with PMP certification.

00:00 Project Management Knowledge Areas
05:17 Project management process
13:58 Project integration management
50:25 Project scope management
01:20:01 Project time management
02:01:09 Project cost Management
02:25:26 Project quality management
02:48:16 Project human resources management
03:13:05 Project communication management
03:25:24 Project risk management
03:33:28 Project stakeholder management

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⏩ Check out the business analyst training videos: https://youtube.com/playlist?l....ist=PLEiEAq2VkUULPx4

#ProjectManagementKnowledgeAreas #KnowledgeAreasofProjectManagement #ProjectManagementKnowledgeAreasExplained #PMP #Simplilearn

➡️ Post Graduate Program In Project Management
This project management certificate program will cover strategization and management - project, program, risk, quality, complexity management, customer-centric digital transformation, PMO implementation, and agile and scrum skills; and allow you to earn 146 Professional Development Units (PDUs) for maintaining CCR for PMI certifications.

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🔥Edureka Tensorflow Training (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"):
https://www.edureka.co/ai-deep....-learning-with-tenso
This Edureka "𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝" video will help you in understanding why we need Transformers and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using Transformers.

🔹Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
🔹Check our complete Deep Learning With TensorFlow Blog Series: http://bit.ly/2sqmP4s
🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV

00:00 Introduction
00:41 Introduction to Deep Learning
04:48 NLP Using RNN
06:16 Scaling up NLP Task
08:13 Transformers Walk Through
10:16 Top Language Models


📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain
📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka
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📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/
00:00 Agenda
01:28 Introduction to Deep Learning
08:24 What is Deep Learning
13:02 NLP using RNN
15:59 Scaling up NLP task
16:49 Transformers WalkThrough
19:51 Top Language Models

#Edureka #EdurekaDeepLearning #Transformersxplained #TransformerNeuralNetworks #DeepLearningLanguageModels #DeepearningTutorial #EdurekaTraining

---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧---------

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How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

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About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

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Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

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