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Generative AI
3,019 Views · 3 years ago

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This Data Science tutorial will help you in understanding what is Data Science, why we need Data Science, the prerequisites for learning Data Science, what a Data Scientist does, the Data Science lifecycle with an example, and career opportunities in the Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.

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This Data Science tutorial for beginners will cover the following topics:
Start (0:00)
1. Need for Data Science? ( 00:50 )
2. What is Data Science? ( 05:55 )
3. Data Science vs Business intelligence ( 11:44 )
4. Prerequisites for learning Data Science ( 16:36 )
5. What does a Data scientist do? ( 24:31 )
6. Data Science life cycle with use case ( 30:17 )
7. Demand for Data scientists ( 47:17 )

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📚Data Science Article: https://bit.ly/30cYbpI

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➡️ Advanced Certificate Program In Data Science
This Advanced Certificate Program in Data Science is designed for working professionals & covers job-critical topics like R, Python, Machine Learning techniques, NLP notions,& Data Visualization with Tableau, using an active learning model that includes live sessions from global professionals, practical labs, IBM Hackathons, corporate ready projects.

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Generative AI
13 Views · 3 months ago

🔥Agentic AI Training Course - Master AI Agents: https://www.edureka.co/agentic-ai-training-course
🔥Integrated MS+PGP Program in Data Science & AI:https://www.edureka.co/dual-ce....rtification-programs

Explore the future of artificial intelligence with Agentic AI! In this video, we dive into the exciting developments and advancements that are set to shape the industry in 2026. We will learn what Agentic AI is and how it goes beyond traditional AI by acting with purpose and autonomy. You’ll discover 5 powerful secrets behind Agentic AI—from multi-agent collaboration to real-world tool integration and ethical guardrails. By the end, you’ll understand how Agentic AI is transforming industries and why it’s the future of intelligent systems.

Join us as we discuss the latest trends, innovations, and predictions for Agentic AI in 2026. Whether you're an AI enthusiast, a business leader, or simply curious about the future of technology, this video is for you. Stay ahead of the curve and discover what's next for Agentic AI!

00:00:00 Introduction
00:01:28 What is Agentic AI?
00:12:20 Agentic AI vs Generative AI
00:33:33 Alexa+ Powered by Generative AI
00:44:47 Agentic AI Roadmap
00:51:15 Introduction to Artificial Intelligence
01:10:03 Introduction to Deep Learning
02:27:26 Artificial Neural Network
02:59:26 Transformers Explained Using Generative AI
03:07:05 Transformers Neural Networks Explained
03:14:29 What are Large Language Models?
03:35:28 What is Multimodal AI?
03:52:46 LLM vs SLM
03:58:14 What is LangChain?
04:15:29 Langchain Agents Explained
04:22:44 What is RAG?
04:45:32 LLMOps: The Future of AI Development
04:51:55 Prompt Engineering
05:06:09 Natural Language Processing (NLP) & Text Mining using NLTK
05:44:52 KNN Algorithm using Python
06:03:11 Alibaba’s Qwen 2.5-Max Just Beat GPT-4 & DeepSeek?
06:07:35 DeepSeek Training Cost: How China Built AI for Less
06:13:04 DeepSeek vs OpenAI: Who Wins the AI Race?
06:25:36 Deep Learning Interview Questions

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📝Feel free to share your comments below.📝

𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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

What is Agentic AI?
Agentic AI refers to artificial intelligence systems that can autonomously make decisions, take actions, and pursue goals with minimal human intervention. Unlike traditional AI, which adheres to predetermined rules, agentic AI may dynamically adapt to new situations. It is widely utilized in robotics, virtual assistants, self-driving cars, and sophisticated decision-making processes.

What are the prerequisites for this Agentic AI Training Course?
In order to complete this course successfully, participants need to have a basic understanding of the Python programming language, machine learning, deep learning, natural language processing, generative AI, and prompt engineering concepts.

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

#agenticai #aiagents #agenticaicourse #generativeai

Generative AI
1,942 Views · 3 years ago

🔥Post Graduate Program In Cyber Security: https://www.simplilearn.com/pgp-cyber-security-certification-training-course?utm_campaign=KaliLinuxTutorialFor2022&utm_medium=Descriptionff&utm_source=youtube

In this video on Kali Linux Tutorial for 2022, we cover the basics of Kali Linux as an OS for penetration testing and ethical hacking. We have made it a beginner's guide to Kali Linux with explanations on how to install Kali Linux, detailed demos about the different types of network security attacks, as well as the rival operating system that is parrot security OS.

What Is Kali Linux?
Features of Kali Linux
Ways to Install Kali Linux
Introduction to Parrot Security OS
Features of Parrot Security and Kali Linux
How to Install Kali Linux on VMware?
Five Stages of Penetration Testing
Best Tools for Kali Linux
Linux Terminal Basics
Setting Up Proxy Chains
Running N-Map Scans
Wireshark Sniffing
Metasploit Hands-On
Gaining Root Access from Windows 10
What Is Kali NetHunter?
Versions and Features of NetHunter
Pros & Cons of Kali NetHunter

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➡️ About Post Graduate Program In Cyber Security

This Post Graduate Program in Cyber Security will help you learn comprehensive approaches to protecting your infrastructure and securing data, including risk analysis, mitigation, and compliance. You will get foundational to advanced skills through industry-leading cyber security certification courses that are part of the program.

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- Network Security
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Data Analytics
27 Views · 2 years ago

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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.

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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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Generative AI
13 Views · 3 months ago

📌Generative AI Course: Masters Program : https://www.edureka.co/masters....-program/generative-
🔥Integrated MS+PGP Program in Data Science & AI:https://www.edureka.co/dual-ce....rtification-programs

Learn the fundamentals of generative artificial intelligence in this beginner-friendly course (2025 edition). We’ll cover what generative AI is, how it differs from traditional AI, key architectures (GANs, VAEs, Transformers), hands-on examples, prompt engineering, and real-world applications you can build today. No prior AI or deep-learning experience required.
✅ What you’ll learn:
What is generative AI & why it matters in 2026
Core models: GAN (Generative Adversarial Network), VAE (Variational Autoencoder)
Transformer architecture and large language models (LLMs)
Text-, image-, and audio-generation use cases
Basics of prompt-engineering and building your own simple GenAI app
Ethical, bias, and safety considerations for generative systems

00:00:00 Introduction
00:01:59 What is Generative AI?
00:15:27 Generative AI Examples
00:34:06 Generative AI Tools
00:55:31 Generative AI in Marketing
01:05:20 Exploring the Ethics of Generative AI
01:12:49 What is Deep Learning
01:24:03 Generate Images Using DC-GAN
01:48:36 Midjourney
02:06:58 What are Large Language Models?
02:26:42 LLM vs SLM
02:31:42 What Is Multimodal AI?
02:48:07 What is LangChain?
03:05:22 What is RAG?
03:28:14 LLMOps: The Future of AI Development
03:34:13 Prompt Engineering For Code Generation
03:43:32 Building a Chatbot with Prompt Engineering
03:59:03 OpenAI API using Python
04:07:18 Agentic AI vs Generative AI
04:25:18 DeepSeek vs OpenAI: Who Wins the AI Race?
04:37:51 Alibaba’s Qwen 2.5-Max Just Beat GPT-4 & DeepSeek?
04:42:14 Nvidia's Latest Breakthrough in Generative AI
04:48:52 The Future of Generative AI and Job Opportunities
04:54:36 GenAI Roadmap
05:03:21 Generative AI Interview Questions


📌 By the end of this course you’ll be equipped to explore your own generative-AI experiments and skill up for deeper tools.

🔔 If you enjoyed this video, Subscribe & hit the bell icon so you won’t miss further AI-tutorials in 2026. Leave a comment if you have questions or want to see a project walkthrough.

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📝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.

Generative AI
3,385 Views · 3 years ago

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Generative AI
19 Views · 5 months ago

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

This lecture provides a concise overview of building a ChatGPT-like model, covering both pretraining (language modeling) and post-training (SFT/RLHF). For each component, it explores common practices in data collection, algorithms, and evaluation methods. This guest lecture was delivered by Yann Dubois in Stanford’s CS229: Machine Learning course, in Summer 2024.

Yann Dubois
PhD Student at Stanford
https://yanndubs.github.io/

About the speaker: Yann Dubois is a fourth-year CS PhD student advised by Percy Liang and Tatsu Hashimoto. His research focuses on improving the effectiveness of AI when resources are scarce. Most recently, he has been part of the Alpaca team, working on training and evaluating language models more efficiently using other LLMs.

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Chapters:
00:00 - Introduction
00:10 - Recap on LLMs
00:16 - Definition of LLMs
00:19 - Examples of LLMs
01:16 - Importance of Data
01:20 - Evaluation Metrics
01:33 - Systems Component
01:41 - Importance of Systems
01:47 - LLMs Based on Transformers
01:57 - Focus on Key Topics
02:00 - Transition to Pretraining
03:02 - Overview of Language Modeling
04:17 - Generative Models Explained
05:15 - Autoregressive Models Definition
06:36 - Autoregressive Task Explanation
07:49 - Training Overview
08:48 - Tokenization Importance
10:50 - Tokenization Process
13:30 - Example of Tokenization
16:00 - Evaluation with Perplexity
20:50 - Current Evaluation Methods
24:30 - Academic Benchmark: MMLU

Generative AI
12 Views · 3 months ago

Welcome to Intro to Deep Learning! This course is for anyone who wants to become a deep learning engineer. I'll take you from the very basics of deep learning to the bleeding edge over the course of 4 months. In this video, we’ll predict an animal’s body weight given it’s brain weight using linear regression via 10 lines of Python. I’ll have a live session every Wednesday at 10 AM PST that covers my weekly video topics in depth. You can click on the little notification bell next to the subscribe button to get an email notification whenever I’m live. And each session is recorded and uploaded to this channel in case you miss it. This Youtube content is 100% created by me (from the writing to the editing, etc.) , it’ll all be released on my channel, and it’s totally free.

I am also very proud and excited to announce my new, exclusive partnership with Udacity. Together, we’re offering the new Deep Learning Nanodegree Foundation program. If you want to take your game to the next level, this is for you! Especially since Udacity will be providing guaranteed admission to their groundbreaking Artificial Intelligence and Self-Driving Car Nanodegree programs to all graduates. They’re offering discounted limited-time pricing, so enroll now to enjoy the unique projects, program sets, and expert reviews. Plus, their community is amazing, so don’t forget to join the Slack channel after you enroll (I’ll be in there too!) And hey, I’m getting paid a small royalty from each enrollment, so let’s do this together!

Link to the Udacity nanodegree:
https://www.udacity.com/course..../deep-learning-nanod

Please Subscribe! And like. And comment. That’s what keeps me going.

This weeks coding challenge (these weekly challenges are not related to the Udacity nanodegree projects, those are additional):
https://github.com/llSourcell/....linear_regression_de

Mick’s winning code:
https://github.com/mickvanhulst/q_learning

Vishal’s runner-up code:
https://github.com/erilyth/Q-Learning-on-Mazes

More learning resources:
http://machinelearningmastery.....com/a-tour-of-machin
https://docs.microsoft.com/en-....us/azure/machine-lea
https://www.toptal.com/machine....-learning/machine-le
https://onlinecourses.science.....psu.edu/stat501/node
http://machinelearningmastery.....com/simple-linear-re

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

The vast majority of this course will use Tensorflow. It's just this first example that's using scikit-learn.

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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Hit the Join button above to sign up to become a member of my channel for access to exclusive content! Join my AI community: http://chatgptschool.io/ Sign up for my AI Sports betting Bot, WagerGPT! (500 spots available): https://www.wagergpt.xyz

Generative AI
1,402 Views · 3 years ago

🔥Advanced Certification In UI UX Design: https://www.simplilearn.com/ui-ux-certification-training-course?utm_campaign=DesignThinking-4nTh3AP6knM&utm_medium=DescriptionFirstFold&utm_source=youtube

In this design thinking tutorial, we will be looking at what is design thinking, why design thing is important, steps of design thinking, empathy mapping, five phases and four pillars of design thinking and agile methodology with design thinking. It is extremely helpful in solving problems that are ill-defined or unknown. Design Thinking is an iterative method that helps resolve user issues or redefines problems in alternative strategies and solutions.

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00:00:00 What is design thinking
00:07:42 Dt process
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Generative AI
620,051 Views · 3 years ago

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐉𝐚𝐯𝐚 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: https://www.edureka.co/java-j2ee-training-course (Use code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
This Edureka Java Full Course will help you in understanding the various fundamentals of Java programming in detail with examples. Below are the topics covered in this Java Full Course video:
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00:01:08 Agenda
00:02:23 What is Java
00:03:52 History of java
00:04:25 Features of Java
00:07:44 Installation of Java
00:15:57 First program in Java
00:16:51 Variables in Java
00:18:21 Data types in Java
00:19:11 Operators in Java
00:31:16 Control Statement in Java
00:39:40 Object oriented programming in Java
00:58:02 Method Overriding
01:00:10 Why you should learn Java
01:27:40 Loops in Java
01:40:40 OOPS Concept in Java
02:43:21 Inheritence in Java
03:02:31 Polymorphism in Java
03:35:41 Arrays in Java
04:42:53 ArrayList in Java
04:58:50 Java Strings
05:34:04 Java Interface
05:48:10 Data Structure in Java
06:06:40 LinkedList in Java
06:25:46 HashMap in Java
06:37:21 Generics in java
07:01:57 Object serialization in Java
07:16:48 Java Threads
07:43:23 Multithreading in Java
07:58:17 Regular Expression in Java
08:24:30 Socket programming in Java
08:36:15 Design patterns in Java
08:59:10 Java Projects
09:2407 Top Java Developer skills
09:34:40 Top 10 Java Framework
09:53:17 Top 10 Books to learn Java
10:04:11 Java Interview Question and Answers

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

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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.

Generative AI
54 Views · 3 years ago

Learn Calculus 1 in this full college course.

This course was created by Dr. Linda Green, a lecturer at the University of North Carolina at Chapel Hill. Check out her YouTube channel: https://www.youtube.com/channe....l/UCkyLJh6hQS1TlhUZx

This course combines two courses taught by Dr. Green. She teaches both Calculus 1 and a Calculus 1 Corequisite course, designed to be taken at the same time. In this video, the lectures from the Corquisite course, which review important Algebra and Trigonometry concepts, have been interspersed with the Calculus 1 lectures at the places suggested by Dr. Green.

⭐️ Prerequisites ⭐️
🎥 Algebra: https://www.youtube.com/watch?v=LwCRRUa8yTU
🎥 Precalculus: https://www.youtube.com/watch?v=eI4an8aSsgw

⭐️ Lecture Notes ⭐️
🔗 Calculus 1 Corequisite Notes: http://lindagreen.web.unc.edu/....files/2020/08/course
🔗 Calculus 1 Notes: http://lindagreen.web.unc.edu/....files/2019/12/course

⭐️ Course Contents ⭐️
(0:00:00) [Corequisite] Rational Expressions
(0:09:40) [Corequisite] Difference Quotient
(0:18:20) Graphs and Limits
(0:25:51) When Limits Fail to Exist
(0:31:28) Limit Laws
(0:37:07) The Squeeze Theorem
(0:42:55) Limits using Algebraic Tricks
(0:56:04) When the Limit of the Denominator is 0
(1:08:40) [Corequisite] Lines: Graphs and Equations
(1:17:09) [Corequisite] Rational Functions and Graphs
(1:30:35) Limits at Infinity and Graphs
(1:37:31) Limits at Infinity and Algebraic Tricks
(1:45:34) Continuity at a Point
(1:53:21) Continuity on Intervals
(1:59:43) Intermediate Value Theorem
(2:03:37) [Corequisite] Right Angle Trigonometry
(2:11:13) [Corequisite] Sine and Cosine of Special Angles
(2:19:16) [Corequisite] Unit Circle Definition of Sine and Cosine
(2:24:46) [Corequisite] Properties of Trig Functions
(2:35:25) [Corequisite] Graphs of Sine and Cosine
(2:41:57) [Corequisite] Graphs of Sinusoidal Functions
(2:52:10) [Corequisite] Graphs of Tan, Sec, Cot, Csc
(3:01:03) [Corequisite] Solving Basic Trig Equations
(3:08:14) Derivatives and Tangent Lines
(3:22:55) Computing Derivatives from the Definition
(3:34:02) Interpreting Derivatives
(3:42:33) Derivatives as Functions and Graphs of Derivatives
(3:56:25) Proof that Differentiable Functions are Continuous
(4:01:09) Power Rule and Other Rules for Derivatives
(4:07:42) [Corequisite] Trig Identities
(4:15:14) [Corequisite] Pythagorean Identities
(4:20:35) [Corequisite] Angle Sum and Difference Formulas
(4:28:31) [Corequisite] Double Angle Formulas
(4:36:01) Higher Order Derivatives and Notation
(4:39:22) Derivative of e^x
(4:46:52) Proof of the Power Rule and Other Derivative Rules
(4:56:31) Product Rule and Quotient Rule
(5:02:09) Proof of Product Rule and Quotient Rule
(5:10:40) Special Trigonometric Limits
(5:17:31) [Corequisite] Composition of Functions
(5:29:54) [Corequisite] Solving Rational Equations
(5:40:02) Derivatives of Trig Functions
(5:46:23) Proof of Trigonometric Limits and Derivatives
(5:54:38) Rectilinear Motion
(6:11:41) Marginal Cost
(6:16:51) [Corequisite] Logarithms: Introduction
(6:25:32) [Corequisite] Log Functions and Their Graphs
(6:36:17) [Corequisite] Combining Logs and Exponents
(6:40:55) [Corequisite] Log Rules
(6:49:27) The Chain Rule
(6:58:44) More Chain Rule Examples and Justification
(7:07:43) Justification of the Chain Rule
(7:10:00) Implicit Differentiation
(7:20:28) Derivatives of Exponential Functions
(7:25:38) Derivatives of Log Functions
(7:29:38) Logarithmic Differentiation
(7:37:08) [Corequisite] Inverse Functions
(7:51:22) Inverse Trig Functions
(8:00:56) Derivatives of Inverse Trigonometric Functions
(8:12:11) Related Rates - Distances
(8:17:55) Related Rates - Volume and Flow
(8:22:21) Related Rates - Angle and Rotation
(8:28:20) [Corequisite] Solving Right Triangles
(8:34:54) Maximums and Minimums
(8:46:18) First Derivative Test and Second Derivative Test
(8:51:37) Extreme Value Examples
(9:01:33) Mean Value Theorem
(9:09:09) Proof of Mean Value Theorem
(0:14:59) [Corequisite] Solving Right Triangles
(9:25:20) Derivatives and the Shape of the Graph
(9:33:31) Linear Approximation
(9:48:28) The Differential
(9:59:11) L'Hospital's Rule
(10:06:27) L'Hospital's Rule on Other Indeterminate Forms
(10:16:13) Newtons Method
(10:27:45) Antiderivatives
(10:33:24) Finding Antiderivatives Using Initial Conditions
(10:41:59) Any Two Antiderivatives Differ by a Constant
(10:45:19) Summation Notation
(10:49:12) Approximating Area
(11:04:22) The Fundamental Theorem of Calculus, Part 1
(11:15:02) The Fundamental Theorem of Calculus, Part 2
(11:22:17) Proof of the Fundamental Theorem of Calculus
(11:29:18) The Substitution Method
(11:38:07) Why U-Substitution Works
(11:40:23) Average Value of a Function
(11:47:57) Proof of the Mean Value Theorem for Integrals

Generative AI
2,438,247 Views · 3 years ago

TOP 50 - In this video we will see the most beautiful animals in the world 8K.
You can see cute animals and endangered animals with nocturnal animals.
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We at 8K ULTRA HD videos have prepared a selection of 8K videos of the most beautiful and cute animals from all over the globe, and we just can’t take our eyes off them.

In this 8K video we wanted to discover the 50 most beautiful 8k animals wildlife in the world and we've found our best and cute animals. This doesn't mean there aren't other beautiful animals out there. We just think the following creatures stand out as the coolest to see them in 8K resolution HDR dolby atmos. They are birds, majestic and beautiful animals, some are pretty animals.
Beauty is a broad and subjective term. If there is an animal that you think is one of the most beautiful but does not feature on the list, leave us a comment and we will take it into account for future 8K ULTRA HD videos with the most beautiful places in the world 8K and nature sounds relaxing music birds.

00:00 INTRO 8K ULTRA HD
00:05 The Most Beautiful Animals in the World
00:10 Tiger
00:57 Golden Macaw - Blue-and-Gold Macaw
01:30 Mandarin Fish - Dragonet
02:00 African Leopard
02:37 Chameleon
03:09 The resplendent Quetzal
03:44 Dolphin
04:21 Crowned Pigeon
04:53 Orangutan
05:26 African Lion
06:14 Panda
06:51 Sea Dragon
07:14 Flamingo
07:52 Pheasant
08:42 Parrots
09:53 African Elephant
10:51 Penguins
12:06 Gorilla
13:18 Lemur
14:37 Clown Fish
15:52 Bald Eagle
17:19 Scarlet Macaw
18:56 Cheetah
19:52 Red Panda
21:00 Bear
22:16 Giraffe
23:24 Weaver Bird
24:55 Kangaroo
26:19 Whale Shark
27:49 Red Colobus Monkey
29:11 Hummingbird
30:47 Hippopotamus
32:14 Shark
33:52 Swan
35:20 Crocodile
36:30 Lilac Breasted Roller Bird
37:55 Sea Lion
39:25 Otter
40:41 Snowy Owl
42:09 Lynx
43:21 Cormorant
44:48 Warthog
46:00 Gray Heron
47:51 African Kudu
48:53 Gray Tree Frog
49:24 Spotted Seal
50:39 Stork
51:54 Beaver
53:07 Seagull
54:44 Lapwing
55:49 Humpback Whale
57:14 Squid
58:20 Squirrel
01:00:12 Fallow Deer
01:01:37 Python
01:02:54 Sheep
01:04:12 Parrot Fish
01:05:12 OUTRO 8K ULTRA HD

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Amazing & Beautiful The Most Beautiful Animals in the world 8K HDR 60FPS (FUHD) Demo 8K ULTRA HD

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Generative AI
2,700,618 Views · 3 years ago

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If you want me to upload some courses please tell me in the comments. Don't Forget To Subscribe, Like & Share
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INTRODUCTION TO DEEP LEARNING
0:00:00 Welcome
0:05:31 What is a Neural Network
0:12:48 Supervised Learning with Neural Network
0:21:17 Why is Deep Learing taking off
0:31:38 About this course
0:34:06 Geoffrey Hinton Interview

NEURAL NETWORKS BASICS
1:14:28 Binary Classification
1:22:52 Logistic Regression
1:28:51 Logistic Regression Cost Function
1:37:03 Gradient Descent
1:48:26 Derivatives
1:55:37 More Derivative Example
2:06:04 Computation Graph
2:09:38 Derivatives With a Computation Graph
2:24:12 Logistic Regression Gradient Descent
2:30:54 Gradient Descent on m Examples
2:38:55 Vectorization
2:46:59 More Vectorization Examples
2:53:18 Vectorization Logistic Regression
3:00:50 Vectorization Logistic Regression Gradient Output
3:10:28 Broadcasting in PYthon
3:21:34 A Note on Python Numpy Vectors
3:28:23 Quick tour of jupyter iPython Notebooks
3:32:06 Explanation of Logistics Regression Cost Function (optional)
3:39:21 Pieter Abbeel Interview

SHALLOW NEURAL NETWORKS
3:55:25 Neural Network Overview
3:59:51 Neural Network Representation
4:05:05 computing a Neural Network's output
4:15:03 Vectorizing Across Multiple Examples
4:24:09 Explantion for Vectorized Implementation
4:31:46 Activation Functions
4:42:43 Why do you need Non-Linear Activation Functions
4:48:19 Derivatives of Activation Functions
4:56:16 Gradient Descent for Neural Networks
5:06:14 Backpropagation Intuition (Optional)
5:22:02 Random Initialization
5:30:00 Ian Goodfellow Interview

DEEP NEURAL NETWORKS
5:44:56 Deep L-layer Neural Network
5:50:47 Forward Propagation in a Deep Network
5:58:02 Getting your Matrix Dimentions Right
6:09:12 Why Deep Representations
6:19:46 Building Blocks of Deep Neural Networks
6:28:19 Forward and Backward propagation
6:38:49 Parameters vs Hyperparameters
6:46:06 What does this have to do with brain


By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.

⭐ Important Notes ⭐
⌨️ The creator of this course is Deeplearning.ai (Andrew Ng)

Note: If you have any copyright issue with the content used in our channel or you find something that belongs to you, before you claim it to Youtube, SEND US A MESSAGE and the respective content will be DELETED right away. Thanks for understanding.

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Data Analytics
23 Views · 2 years ago

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Advanced Excel Full Course 2023 by Simplilearn is complete Excel tutorial that helps aspiring data analysts with excel data analytics fundamentals. This Microsoft Excel Tutorial For Beginners will cover basic topics of excel like data validation, conditional formatting, spreadsheet fundamentals to advanced excel topics like time series analysis, filters, slicers, excel dashboarding, data visualization, and much more.

This Excel Full Course will cover the following topics:
00:00:00 introduction to Microsoft Excel Full Course
00:01:52 Excel Basic Knowledge
00:18:45 How To Change Lower Case To Upper Case In Excel?
00:19:58 How To Add Rows In Excel?
00:21:32 How To Add Columns In Excel?
00:24:29 How To Select Entire Column In Excel?
00:27:16 How to Compare Two Columns In Excel?
00:34:36 How To Convert Rows To Columns In Excel?
00:36:13 How to Group Rows in Excel?
00:37:55 How To Remove Blank Rows In Excel?
00:41:44 How To Freeze Row In Excel Tutorial
00:44:12 How to Convert Numbers to Words in Excel?
00:46:59 Combining Data From Multiple Cells In Excel
00:49:27 How To Merge Cells In Excel
00:54:12 How to Add Date in Excel?
00:55:48 How To Change Date Format In Excel (dd/mm/yyyy) To (mm/dd/yyyy)
00:59:55 How To Calculate Age In Excel From A Date Of Birth?
01:01:37 How To Calculate Time Difference in Excel?
01:03:15 DAX In Excel Explained
01:08:20 Checkboxes In Excel
01:12:31 How to Insert Excel in PPT?
01:14:40 How To Insert Image In Excel?
01:18:05 How To Insert PDF in Excel Sheet?
01:21:40 How To Convert PDF To Excel
01:26:05 How to Insert Tick Mark in Excel?
01:29:05 How to add Watermark in Excel?
01:32:00 How to Increase Cell Size in Excel?
01:34:50 How to create Barcode In Excel
01:38:08 Excel Flash Fill
01:43:00 How to Add Hyperlink In Excel
01:55:44 Excel Round-Off Formula
01:58:18 How To Calculate Standard Deviation In Excel?
02:04:50 Index Match In Excel
02:09:10 How to Sort by Date in Excel?
02:10:40 How to Use AutoSum In Excel
02:13:47 How To Sort Data In Excel?
02:23:56 Slicer and Filter in Excel
02:27:49 How to Add Filters in Excel
02:29:20 Goal Seek In Excel
How to Create Drop-Down List In Excel?
02:44:46 Data Validation In Excel
02:44:50 How To Lock(Protect) Cells In Excel
03:17:55 How To Calculate Average In Excel?
03:18:43 Excel Print Page Setup
03:36:47 Page Break In Excel For Beginners
03:08:53 Conditional Formatting In Excel
03:41:56 How to Count Colored Cells in Excel?
03:47:54 DateDif In Excel Explained
03:51:08 Remove Duplicates In Excel
03:55:05 SUMIFS Formula in Excel
04:02:40 How To Create Pivot Tables In Excel
04:07:00 Autofill In Excel Explained
04:10:22 How to Calculate Percentage In Excel
04:12:08 How To Highlight Duplicates In Excel?
04:16:04 Charts In Excel
04:21:56 Progress Tracker In Excel
04:25:50 Excel Gantt Chart Tutorial
04:28:48 Pivot Tables In Excel
04:57 Pivot Charts in Excel and How to Create a Pivot Table Using Multiple Sheets in Excel
06:45:02 Time Series Analysis in Excel
07:37:00 Excel Dashboard Design
08:47:00 Excel Power Query Tutorial For Beginners
09:44:44 Excel Budget Template
09:51:37 Excel Interview Questions And Answers

⏩ Check out the Excel tutorial videos: https://www.youtube.com/watch?v=nPkmWE4JCfE&list=PLEiEAq2VkUUKf8aLrspLg3zuyJ5S-5K5S

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Generative AI
14 Views · 5 months ago

A video about reinforcement learning, Q-networks, and policy gradients, explained in a friendly tone with examples and figures.

Introduction to neural networks: https://www.youtube.com/watch?v=BR9h47Jtqyw

Introduction: (0:00)
Markov decision processes (MDP): (1:09)
Rewards: (5:39)
Discount factor: (8:51)
Bellman equation: (10:48)
Solving the Bellman equation: (12:43)
Deterministic vs stochastic processes: (16:29)
Neural networks: (19:15)
Value neural networks: (21:44)
Policy neural networks: (25:44)
Training the policy neural network: (30:46)
Conclusion: (34:53)

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