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Are you overwhelmed by complex data and unsure of how to interpret it? Do you want to enhance your data analysis skills? You've come to the right place! Our course is designed to teach you the essentials of Business Intelligence using Power BI.

Whether you're a student, a young professional, or a manager, this course is tailored for you. Led by expert instructor, you'll learn everything from connecting to data sources to creating interactive dashboards and visualizations. You'll discover how to transform and clean data, and how to use advanced features like tooltips, animations, and bookmarks to make your data more engaging. Additionally, you'll learn to design effective dashboards.

By the end of this course, you'll be proficient in analyzing and visualizing data, empowering you to make informed, data-driven decisions. You'll be equipped to elevate your data analysis skills and advance your career.

Don't miss this chance to unlock the full potential of your data. Start learning today! This educational YouTube series is a full course on Power BI, covering all essential features and functions. It's perfect for business analysts, data analysts, and anyone interested in Business Intelligence and data analytics. Led by Mr. Pavan Lalwani, this tutorial will guide you from the basics to advanced usage, helping you effectively utilize Power BI for data analysis and visualization.

You'll learn how to enhance your visualizations with Power BI tooltips, animations, and bookmarks, and design impactful dashboards. This course will provide you with the knowledge and skills needed to confidently analyze and present data using Power BI.

➡️ About PL-300 Microsoft Power BI Certification Training
* Aligned with PL-300: Microsoft Power BI Data Analyst certification
* Topics include Power BI Desktop layouts, BI reports, dashboards, DAX commands, and functions
* Learn to experiment, refine, prepare, and present data with ease
* Explore comprehensive Power BI training for hands-on applied learning
* Learn through a practical approach to help you gain expertise


✅ Key Features
30+ hours of blended learning
Industry-based projects for experiential learning
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✅ Skills Covered

- Data Modelling
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- Data Visualization
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For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

November 18, 2025
This lecture covers career advice and a guest speaker.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

Please follow along with the course schedule and syllabus: https://cs230.stanford.edu/syllabus/

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

Guest Speaker
Laurence Moroney
Best-selling AI author and award-winning researcher

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

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This video on "What is Artificial Intelligence" will give you a brief overview of artificial intelligence as a technology in just 5 minutes. We will start with a minor introduction to artificial intelligence in which we will know what is artificial intelligence with the help of examples. Moving ahead we will see what are the uses of AI, what is strong AI and what is weak AI. We will warp up this video by making you understand the major difference between AI ML and Deep Learning which will be followed by the future scope of artificial intelligence.

By the end of this video, you will understand:
00:00 What is Artificial Intelligence?
00:37 Uses of AI
01:48 What is AI
02:02 Weak AI
02:18 Strong AI
03:13 Difference between AI ML and Deep learning
05:00 Future of Artificial Intelligence

Don't forget to take the quiz at 04:10!

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What is Artificial Intelligence?
Artificial Intelligence or AI is the combination of algorithms used for the purpose of creating intelligent machines that have the same skills as a human being. It uses machine learning and deep learning techniques to build complex systems.

✅ 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.

✅ Key Features
- 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
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✅ Skills Covered
- Statistics
- Python
- Supervised Learning
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- Recommendation Systems
- NLP
- Neural Networks
- GANs
- Deep Learning
- Reinforcement Learning
- Speech Recognition
- Ensemble Learning
- Computer Vision

✅ Tools Covered
- Python
- Keras
- TensorFlow
- Matplotlib
- Scikit Learn
- Django
- Flask
- OpenCV

✅ Eligibility Criteria

For admission to this Post Graduate Program in AI and ML, candidates should have:
- 2+ years of work experience preferred
- A bachelor's degree with an average of 50% or higher marks
- Basic understanding of programming concepts and mathematics

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🔥Machine Learning Training with Python: https://www.edureka.co/machine....-learning-certificat
This Edureka video on 'Mathematics for Machine Learning' teaches you all the math needed to get started with mastering Machine Learning. It teaches you all the necessary topics and concepts of Linear Algebra, Multivariate Calculus, Statistics, and Probability and also dives into the actual implementation of these topics.

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About the Course :

Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
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Why Learn Machine Learning with Python?

Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
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Who should go for this Course?

Edureka’s Python Machine Learning Certification Course is a good fit for the below professionals:
Developers aspiring to be a ‘Machine Learning Engineer'
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Machine Learning (ML) Techniques
Information Architects who want to gain expertise in Predictive Analytics
'Python' professionals who want to design automatic predictive models
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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.
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).

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

October 28, 2025
This lecture provides walkthroughs of examples of AI projects and making day-to-day decisions in building AI systems.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

To follow along with the course schedule and syllabus, visit: https://cs230.stanford.edu/syllabus/

More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

NOTE: There was no class on November 4, 2025 (Lecture 7). The next lecture is Lecture 8.

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

MIT Introduction to Deep Learning 6.S191: Lecture 3
Convolutional Neural Networks for Computer Vision
Lecturer: Alexander Amini
** New 2025 Edition **

For all lectures, slides, and lab materials: http://introtodeeplearning.com​

Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

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In this video, we guide you through a comprehensive Generative AI roadmap for beginners, breaking down the essential steps to kickstart your journey. From learning Python and basic machine learning concepts to diving into deep learning and building generative models like GANs and VAEs, this step-by-step guide covers everything you need. Whether you're just starting out or looking to build a solid foundation in AI, this roadmap will give you the skills and confidence to explore the exciting world of Generative AI. Don't miss out on this must-watch for aspiring AI enthusiasts!

✅ How to learn gen AI for beginners?
To learn Generative AI as a beginner, start by mastering Python and basic machine learning concepts. Then, explore deep learning frameworks like TensorFlow or PyTorch. Study neural networks and dive into generative models like GANs and VAEs. Practice with hands-on projects to build real-world experience.

✅ Does generative AI require coding?
Yes, generative AI typically requires coding, especially in languages like Python. You’ll need to understand coding to work with frameworks such as TensorFlow or PyTorch, build models, and manipulate data. However, some tools and platforms now offer low-code or no-code solutions for those with limited programming skills.

✅ Is AI highly paid?
Yes, AI professionals are highly paid due to the increasing demand for specialized skills in machine learning, data science, and AI development. Roles like AI engineers, data scientists, and machine learning specialists command high salaries, reflecting the value of AI expertise in industries like tech, healthcare, finance, and more.

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

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#genairoadmapforbeginners #genairoadmapforbeginnersandexperts #genAIRoadmap #careerInGenAI #GenAi #simplilearn #2024

➡️ 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
- 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 JobAssist helps you get noticed by top hiring companies
- 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

- ChatGPT
- Generative AI
- Explainable AI
- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
- And Many More…

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** Data Science Master's Program: https://www.edureka.co/masters....-program/data-scient **
This Edureka "Data Science Training" video (Data Science Blog Series: https://goo.gl/1CKTyN) will help you understand all the concepts of Data Science. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:

1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
6. Introduction to Machine Learning using R
7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
10. Deep Learning

Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS

#Datasciencetraining #Datasciencetutorial #Datasciencecourse #Datascience #Edureka

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How it Works?

1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
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. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!

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

About the Course

Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.

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

Why Learn Data Science?

Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.

After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R

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

Who should go for this course?

The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies

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

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Customer Reviews:

Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

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

Avalanches are beautiful, majestic, and completely terrifying – this is a video all about the science of avalanches. Head to https://brilliant.org/veritasium to start your free 30-day trial, and get 20% off an annual premium subscription.

A massive thank you to everyone at Whistler Blackcomb for making this shoot happen – Dane Gergovich, David Iles, Alastair Collis, Erin Taylor, and everyone else on the ski patrol team. Thank you for keeping us safe!

A massive thank you to Bruce Tremper, Niko Schirmer, and Mark Smiley for their expertise and help with this video. We are truly grateful for your time and expertise.

Check out Niko's channel here -- it's easily some of the best skiing content on YouTube. https://www.youtube.com/@Nikolai_Schirmer

If you have any intention of going into the backcountry, you have to read Bruce’s book – Staying Alive in Avalanche Terrain. It’s the Bible for this stuff, and the book that inspired us to make this video.

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Special thanks to our Patreon supporters! Join the community to help us keep our videos free, forever: https://ve42.co/PatreonDE

Adam Foreman, Anton Ragin, Balkrishna Heroor, Bertrand Serlet, Bill Linder, Blake Byers, Burt Humburg, Chris Harper, Dave Kircher, David Johnston, Evgeny Skvortsov, Garrett Mueller, Gnare, gpoly, I. H., John H. Austin, Jr., john kiehl, Josh Hibschman, Juan Benet, KeyWestr, Kyi, Lee Redden, Marinus Kuivenhoven, Martin, Matthias Wrobel, Max Paladino, Meekay, meg noah, Michael Krugman, Orlando Bassotto, Paul Peijzel, Richard Sundvall, Sam Lutfi, Stephen Wilcox, Tj Steyn, Toni , TTST, Ubiquity Ventures, wolfee

If you’re looking for a molecular modeling kit, try Snatoms, a kit I invented where the atoms snap together magnetically - https://ve42.co/SnatomsV

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References:
Tremper, B. (2001). Staying Alive in Avalanche Terrain. Mountaineers Books. - https://ve42.co/Tremper2001
McClung, D. & Schaerer, P. (2006). The Avalanche Handbook. Mountaineers Books. - https://ve42.co/McClung2006
Hopfinger, E. J. (1983). Snow Avalanche Motion and Related Phenomena. Annual Review of Fluid Mechanics. - https://ve42.co/Hopfinger1983
Schweizer, J. et al. (2003). Snow avalanche formation. Reviews of Geophysics. - https://ve42.co/Schweizer2003
Schweizer, J. et al. (2008). Review of spatial variability of snowpack properties and its importance for avalanche formation. Cold Regions Science and Technology. - https://ve42.co/Schweizer2008
Jamieson, B. (2006). Formation of refrozen snowpack layers and their role in slab avalanche release. Reviews of Geophysics. - https://ve42.co/Jamieson2006
Schweizer, J. et al. (2016). Avalance release 101. International Snow Science Workshop Proceeding. - https://ve42.co/Schweizer2016
Schweizer, J. (2008). Snow avalanche formation and dynamics. Cold Regions Science and Technology. - https://ve42.co/Schweizer2008-2

Images & Video:
Skiing and snowboarding footage by Niko Schirmer - https://ve42.co/NikoYT
World's Biggest Avalanche (c)Thom Goddard/All Star Films Ltd 2015. Thank you to the Thom Goddard Channel for use - https://ve42.co/ThomGoddard
Newspaper clippings via Newspapers.com – https://ve42.co/Newspapers
Video of a snow slab avalanche via Trottet, B. et al. (2022). Transition from sub-Rayleigh anticrack to supershear crack propagation in snow avalanches. Nature Physics. - https://ve42.co/Trottet2022
Remote Triggered Avalanche via Henry’s Avalanche Talk - Remote Triggering of an Avalanche
WW1 images via Brugnara, Y. et al. (2016). December 1916: Deadly Wartime Weather. Geographica Bernensia - https://ve42.co/Brugnara2016
Major Avalanches via Geomorphological Hazards - https://ve42.co/MajorAvi
White Friday avalanche illustration via Avalanches WIllow - https://ve42.co/WhiteFriday
White Friday aftermath via Lost In History - https://ve42.co/AviGraves
Field mass on Marmolada via Austrian National Library - https://ve42.co/AviMass
Yungay disaster via NOAA - https://ve42.co/Yungay
Yungay aftermath via AMC Museum - https://ve42.co/YungayAMC
Bruce Tremper Image via utavy - https://ve42.co/BruceTPic
Snowpack via SnowStudies.org - https://ve42.co/SnowStudies
Snowpack via Avalanche.org - https://ve42.co/SnowPackAvi
Snow crystals via Reiweger, I. (2011). Failure of weak snow layers. ETH. - https://ve42.co/Reiweger2011

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Directed by Petr Lebedev
Written by Petr Lebedev and Derek Muller
Edited by Peter Nelson (Additional editing by James Horsley)
Animated by Jakub Misiek, Fabio Albertelli, Alex Zepherin, and Alex Drakoulis
Additional Research by Gregor Čavloviċ
Filmed by Petr Lebedev and Ryan Regehr
FPV Drone by Mitch Winton
Produced by Petr Lebedev, Derek Muller, Han Evans, Giovanna Utichi, Rob Beasley Spence, Emily Taylor and Gregor Čavloviċ

Thumbnail by Ren Hurley and Peter Sheppard
Additional video/photos supplied by Getty Images and Pond5
Music from Epidemic Sound

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

In this video on ""Large Language Models Explained "", you'll gain a deep understanding of large language models (LLMs). We'll explore what LLMs are and how they function, providing clear examples along the way. You'll learn about the benefits of LLMs and discover the key differences between LLM development and traditional development methods. By the end of this video, you'll have a thorough grasp of LLM technology.

In this informative video of Large Language Models Explained, we will cover the essential topics such as:

✅ 00:00 - Introduction to Large Language Model
✅ 01:35 - What is LLMs?
✅ 02:20 - How LLMs works?
✅ 03:42 - Examples of LLMs
✅ 06:32 - Benefits of using LLMs
✅ 06:32 - Differentiate between LLM & Traditional development"
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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 [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll-free).
#Edureka #largelanguagemodel #llm #llmmachinelearning #generativeai #edurekacourse"

A complete guide to the mathematics behind neural networks and backpropagation.

In this lecture, I aim to explain the mathematical phenomena, a combination of linear algebra and optimization, that underlie the most important algorithm in data science today: the feed forward neural network.

Through a plethora of examples, geometrical intuitions, and not-too-tedious proofs, I will guide you from understanding how backpropagation works in single neurons to entire networks, and why we need backpropagation anyways.

It's a long lecture, so I encourage you to segment out your learning time - get a notebook and take some notes, and see if you can prove the theorems yourself.

As for me: I'm Adam Dhalla, a high school student from Vancouver, BC. I'm interested in how we can use algorithms from computer science to gain intuition about natural systems and environments.

My website: adamdhalla.com
I write here a lot: adamdhalla.medium.com
Contact me: [email protected]

Two good sources I recommend to supplement this lecture:

Terence Parr and Jeremy Howard's The Matrix Calculus You Need for Deep Learning: https://arxiv.org/abs/1802.01528

Michael Nielsen's Online Book Neural Networks and Deep Learning, specifically the chapter on backpropagation http://neuralnetworksanddeeple....arning.com/chap2.htm

ERRATA----
I'm pretty sure the Jacobians part plays twice - skip it when you feel like stuff is repeating, and stop when you get to the part about the "Scalar Chain Rule" (00:24:00).

And, here are the timestamps for each chapter mentioned in the syllabus present at the beginning of the course.

PART I - Introduction
--------------------------------------------------------------
00:00:52 1.1 Prerequisites
00:02:47 1.2 Agenda
00:04:59 1.3 Notation
00:07:00 1.4 Big Picture
00:10:34 1.5 Matrix Calculus Review
00:10:34 1.5.1 Gradients
00:14:10 1.5.2 Jacobians
00:24:00 1.5.3 New Way of Seeing the Scalar Chain Rule
00:27:12 1.5.4 Jacobian Chain Rule

PART II - Forward Propagation
--------------------------------------------------------------
00:37:21 2.1 The Neuron Function
00:44:36 2.2 Weight and Bias Indexing
00:50:57 2.3 A Layer of Neurons

PART III - Derivatives of Neural Networks and Gradient Descent
--------------------------------------------------------------
01:10:36 3.1 Motivation & Cost Function
01:15:17 3.2 Differentiating a Neuron's Operations
01:15:20 3.2.1 Derivative of a Binary Elementwise Function
01:31:50 3.2.2 Derivative of a Hadamard Product
01:37:20 3.2.3 Derivative of a Scalar Expansion
01:47:47 3.2.4 Derivative of a Sum
01:54:44 3.3 Derivative of a Neuron's Activation
02:10:37 3.4 Derivative of the Cost for a Simple Network (w.r.t weights)
02:33:14 3.5 Understanding the Derivative of the Cost (w.r.t weights)
02:45:38 3.6 Differentiating w.r.t the Bias
02:56:54 3.7 Gradient Descent Intuition
03:08:55 3.8 Gradient Descent Algorithm and SGD
03:25:02 3.9 Finding Derivatives of an Entire Layer (and why it doesn't work well)

PART IV - Backpropagation
--------------------------------------------------------------
03:32:47 4.1 The Error of a Node
03:39:09 4.2 The Four Equations of Backpropagation
03:39:12 4.2.1 Equation 1: The Error of the last Layer
03:46:41 4.2.2 Equation 2: The Error of any layer
04:03:23 4.2.3 Equation 3: The Derivative of the Cost w.r.t any bias
04:10:55 4.2.4 Equation 4: The Derivative of the Cost w.r.t any weight
04:18:25 4.2.5 Vectorizing Equation 4
04:35:24 4.3 Tying Part III and Part IV together
04:44:18 4.4 The Backpropagation Algorithm
04:58:03 4.5 Looking Forward

Building a research multi agent system → https://goo.gle/41TYswp
Agent starter pack → https://goo.gle/4iZiz35
Human-in-the-Loop LangGraph app → https://goo.gle/4iyii7h

What is agentic AI and how can developers apply agentic AI in their applications? Welcome back to season 2 of Real Terms for AI with Googlers Aja Hammerly and Jason Davenport. In this video, Aja and Jason discuss agentic workflows, how agents are different from workflows, and when to use an agentic workflow. Follow along with code snippets in the Github link and help implement these concepts in many use cases.

Chapters:
0:00 - Intro
0:43 - What does agentic mean?
2:08 - Agentic workflow examples
3:28 - When to use agentic workflow or AI agent
4:33 - Wrap up

Watch more Real Terms for AI → https://goo.gle/AIwordsExplained
Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech

#GoogleCloud #GenerativeAI

Speakers: Aja Hammerly, Jason Davenport
Products Mentioned: Gemini, Gemma

📌Generative AI Course: Masters Program : https://www.edureka.co/masters....-program/generative-

This Generative AI full course is designed to help you understand and explore the power of AI in the most simple and practical way. Whether you're a beginner or just curious about how tools like ChatGPT, Midjourney, or other AI systems work, this course covers everything from the basics of what Generative AI is, how to communicate with AI using effective prompts, and real-life use cases in content creation, business, and productivity.
00:00:00:Introduction
00:02:06 What is Machine learning?
00:18:42 Types of Machine Learning Models
00:25:56 Mathematics for Machine Learning
02:08:43 Machine Learning Algo
02:30:38 How to select the correct predictive modeling techniques?
02:42:48 Linear Regression Algorithm
02:50:14 Logistic Regression Algorithm
03:37:27 Linear Regression Vs Logistic Regression
03:40:58 MLOps for Beginners
03:53:00 How to Become a Machine Learning Engineer?
04:02:31 Machine learning Engineer Skills
04:10:40 Machine Learning Roadmap
04:20:32 Machine Learning Tips
04:27:12 What is Generative AI?
04:40:42 Generative AI Examples
04:59:21 Generative AI Tools
05:20:46 What Are GANs?
05:33:08 Generate Images Using DC-GAN
05:57:41 Transformers In Gen AI
06:05:20 Generative AI Course - Part 1 - What is LLM?
06:25:04 Generative AI Course - Part 2 - What is LangChain?
06:42:19 Generative AI Course - Part 3 - What is RAG?
07:05:11 Prompt Engineering Explained
07:18:56 Prompt Engineering for Code Generation
07:28:16 Building a Chatbot with Prompt Engineering
07:44:10 GitHub Copilot
08:01:36 OpenAI API using Python
08:09:51 Midjourney
08:28:12 Generative AI in Marketing
08:38:42 Exploring the Ethics of Generative AI
08:46:12 Nvidia's Latest Breakthrough in Generative AI
08:52:49 DeepSeek vs OpenAI: Who Wins the AI Race?
09:05:22 The Future of Generative AI and Job Opportunities
09:11:06 GenAI Roadmap
09:19:51 Generative AI Interview Questions

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py

📢📢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

📝Feel free to share your comments below.📝

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Please write back to us at [email protected] or call us at IND: 9606058406 / US: +18885487823 (toll-free) for more information.

🔥Introduction to Robotic Process Automation Course - https://www.simplilearn.com/introduction-to-robotic-process-automation-course?utm_campaign=Skillup-RPA&utm_medium=DescriptionFirstFold&utm_source=youtube

This video, titled "What is RPA?" gives a basic overview of RPA and its ideas. We learn about what RPA is, why it is being widely used across industries, how it works, the results of using RPA, and finally, its growth estimates over the last few years. So, let's jump right in.
0:00 - Start
0:50 - What is RPA?
1:51 - Working of RPA
2:46 - Advantages of RPA
3:29 - Applications of RPA
3:42 - Future of RPA
4:23 - Quiz


Don't forget to take the quiz at 04:24! 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 May 27th, 2020.

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

⏩ Check out the RPA training videos: https://bit.ly/2Sy2zeO

#RPA #RoboticProcessAutomation #RPADeveloper #WhatIsRPA? #RPAExplained #RPATutorialForBeginners #Simplilearn #WorkingofRPA #RPAtools #RPAApplications

What is RPA?
Robotic Process Automation (RPA) is the use of software with Artificial Intelligence and Machine Learning capabilities to handle high-volume, repetitive tasks that previously required humans to perform. Some of these tasks include addressing queries, making calculations, maintenance of records, and performing transactions.

If you want to expand your expertise in advanced intelligent applications, then this Introduction to Robotic Process Automation (RPA) course will put you on the fast track. RPA is one of the hottest and fastest-growing technologies for improving real-time business operations and processes. This course will give you an overview of RPA concepts, the value-add it brings with relevant business use cases and tools understanding.

What are the benefits of this course?
Robotic Process Automation (RPA) is an advanced technology that automates huge quantities of redundant tasks by applying artificial intelligence (AI). RPA can be used for processing transactions, manipulating data, triggering responses and communicating with other digital systems. RPA has applications in a plethora of industries including insurance claims processing, invoice processing, customer feedback analysis, onboarding of employees, HR operations, and much more. More on the RPA market:
- The global market for RPA software and services is expected to grow to $1.2 billion by 2021 at a compound annual growth rate of 36 per cent.
- One wine producer increased its order accuracy from 98% to 99.7% while reducing costs There are 3000+ RPA job postings in India and the US.

What are the course objectives?
The primary objective of this Introduction to RPA training course is to give you a glimpse into the bright and exciting future of Robotic Process Automation and to give you an overview of the fundamental methodologies and tools that will help you employ RPA on the job. You’ll learn the key RPA tools and workflows used in intelligent automation, steps for implementing RPA in your enterprise and case studies that will provide innovative best practices.

Upon successful completion of our Introduction to Robotics Process Automation online training course, you will gain a fair understanding of RPA and related business applications. By the end of the course, you will be able to understand-
1. RPA and it's business applications
2. The impact that RPA creates with significant ROI
3. Various tools available in the market and their right fit
4. Organization and management of a real-life workflow automation project

Who should take this course?
Simplilearn’s Introduction to Robotic Process Automation course is best suited for:
1. CXOs
2. Team Leads
3. Business Analysts
4. Solution Architects

👉Learn more at: https://www.simplilearn.com/robotic-process-automation-using-uipath-training-course?utm_campaign=RPA&utm_medium=Description&utm_source=youtube

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Military robotics technology is not far behind as our world becomes more advanced. If you have seen Corridor Digital’s parody video, you may know what the future will look like. Don't worry; the realism of that video is a testament to the advancements in visual effects at the Los Angeles production studio, and not necessarily robotics.
But to be honest, we are not far behind, and in this video, we will explore a company and its line of robots that are leading the charge to make soldiers obsolete.