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The AI Guy
เข้าร่วมเมื่อ 7 ก.พ. 2023
Welcome to CognitoBytes, your go-to destination for insightful and engaging content on machine learning, neural networks, artificial intelligence, and programming! Dive into the fascinating world of technology with clear, concise tutorials that break down complex concepts into easy-to-understand lessons. Whether you're a beginner looking to grasp the basics or an experienced coder seeking in-depth insights, our videos cover a wide range of topics, from programming tips to hands-on AI projects.
Stay tuned for regular updates, tutorials, and tech explainers that will fuel your curiosity and help you stay at the forefront of the ever-evolving world of AI and programming. Subscribe now, and let's embark on this exciting journey together! 🚀💻 #cognitobytes #MachineLearning #AI #Programming
Stay tuned for regular updates, tutorials, and tech explainers that will fuel your curiosity and help you stay at the forefront of the ever-evolving world of AI and programming. Subscribe now, and let's embark on this exciting journey together! 🚀💻 #cognitobytes #MachineLearning #AI #Programming
Understanding why we use Neural Networks: When Linear and Logistic Regression Fall Short!
Why choose neural networks over linear or logistic regression? In this video, we dive into the strengths and limitations of these models and explain why neural networks excel at handling complex, non-linear patterns. We'll break down key concepts like polynomial features, decision boundaries, and activation functions, using clear examples, including a deep dive into image recognition. Whether you're new to machine learning or looking to deepen your understanding, this video will provide valuable insights into why neural networks are a powerful choice for advanced tasks.
Don't forget to like, subscribe, and hit the bell icon for more machine learning content!
Don't forget to like, subscribe, and hit the bell icon for more machine learning content!
มุมมอง: 1 020
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Neural Networks Explained: From 1943 Origins to Deep Learning Revolution 🚀 | AI History & Evolution
มุมมอง 1K3 หลายเดือนก่อน
Discover the fascinating history of neural networks, from their origins in 1943 to the groundbreaking deep learning advancements of today. Learn how pioneering scientists like Warren McCulloch, Walter Pitts, Frank Rosenblatt, John Hopfield, Geoffrey Hinton, and others contributed to this revolutionary field. Understand key developments like the perceptron, backpropagation, and the role of GPUs ...
Unlock YouTube Success with ChatGPT: Boost Views with AI-Optimization! Tips & Tricks!
มุมมอง 214 หลายเดือนก่อน
Unlock the secrets to TH-cam success with ChatGPT, your AI-powered assistant! In this video, we dive into how ChatGPT can revolutionize your channel by optimizing titles, descriptions, and tags to boost views and discoverability. Discover how ChatGPT helps you craft catchy titles and compelling descriptions, brainstorm content ideas, and enhance your video scripts for maximum engagement. Whethe...
This FREE AI tool creates VIRAL videos in 1 MINUTE | WATCH before everyone knows this!
มุมมอง 2426 หลายเดือนก่อน
Learn how to create viral TH-cam shorts effortlessly with Opus Clip, the free AI tool that streamlines the process. Discover how to extract the best parts of a video, add subtitles, emojis, and resize-all in under a minute! Join us as we explore the powerful features of Opus Clip and unlock the secret to crafting engaging short videos. Don't miss out on this game-changing tool-watch now and sta...
How to Make $27,000 a Month with AI-Generated YouTube Shorts in just 5 MINUTES
มุมมอง 2.6K6 หลายเดือนก่อน
Discover the incredible world of AI-powered content creation! This faceless TH-cam channel is raking in up to $27,000 per month with super simple TH-cam Shorts. In this video, we reveal the secrets behind their success, using AI and automation to generate engaging content effortlessly. Best of all, you can do it too, with just a computer and an internet connection. Learn how to create 50 shorts...
Understanding the Cost Function in Logistic Regression
มุมมอง 1466 หลายเดือนก่อน
Unravel the intricacies of logistic regression training in this enlightening tutorial. Learn how to optimize your model's parameters for precise predictions using the binary cross-entropy loss function. Join us as we delve into the fundamentals of cost functions and explore the convexity of logistic regression. Subscribe now for more insightful machine learning content!
Logistic Regression is EASY Once you’ve seen This - Classification problems in Machine learning
มุมมอง 467 หลายเดือนก่อน
Unlock the secrets of logistic regression in this classification masterpiece! 🚀 Dive into predicting spam emails or patients' health status with discrete outcomes. Unlike linear regression, classification deals with yes/no scenarios. 🤔 Let's explore logistic regression's power in handling discrete values through the sigmoid function. Unravel the decision boundary that helps us decide if an inpu...
The Basics of Machine Learning: Linear regression Explained - EASY Step By Step Guide for beginners
มุมมอง 807 หลายเดือนก่อน
Welcome to the first course in our machine learning series! In this video, we'll dive into the basics of machine learning, starting with an explanation of linear regression. If you've ever wanted to understand how machine learning can predict almost anything with enough data, this is the perfect starting point for you. By the end of this video, you'll be ready to take on your first real machine...
STOP Searching for the Perfect Video - How To Start with AI and Machine learning in 47s
มุมมอง 957 หลายเดือนก่อน
Join us on an amazing journey into the world of machine learning and AI. From the basics of linear regression to self-driving cars and smartphone recognition, we'll cover it all. Whether you're a beginner or an expert, this channel is the perfect starting point. Let's take that first step together and dive into the endless possibilities of machine learning and AI.
Machine Learning's SECRET Weapon: The Normal Equation Method for Optimal Parameters in 1 Step! 🚀🔍
มุมมอง 237 หลายเดือนก่อน
Machine Learning's SECRET Weapon: The Normal Equation Method for Optimal Parameters in 1 Step! 🚀🔍
Mastering Polynomial Regression in Machine Learning: Unleashing Curves, Features, and Square Roots!
มุมมอง 438 หลายเดือนก่อน
Embark on a captivating journey into Polynomial Regression with this latest installment! We're breaking free from the constraints of straight lines and diving into the realm of curves and sophistication. Discover the power of higher-order features, square roots, and creating your own unique features to mold models tailored to your data. But wait, there's a twist - feature scaling becomes crucia...
How to Initialize Parameters? Optimizing Machine Learning: The Key to Faster Convergence | With CODE
มุมมอง 778 หลายเดือนก่อน
Unlock the secrets of optimal parameter initialization in machine learning with this tutorial! Learn how the choice of initial theta values impacts the convergence speed during gradient descent. Discover why starting with all zeros can hinder performance and explore a more effective strategy: random initialization. Find out how breaking symmetry by giving each parameter a unique starting value ...
Why You MUST Apply FEATURE SCALING in Machine learning! Improve gradient descent
มุมมอง 3868 หลายเดือนก่อน
In this insightful tutorial, we delve into the crucial concept of feature scaling, exploring how it optimizes gradient descent for faster convergence, as vividly illustrated through contour plots. Contour plots depict the cost function's value in relation to parameters theta 1 and theta 2, providing valuable insights into the convergence speed. The key takeaway? The more elliptical the contour ...
Optimizing Gradient Descent: How to Choose the Best Learning Rate (Step-by-Step Guide) 🚀
มุมมอง 3388 หลายเดือนก่อน
Welcome to our channel! 🚀 In this video, we dive into the crucial aspect of selecting the perfect learning rate for your specific application in gradient descent. Before we delve into the details, let's quickly refresh our memory on what the learning rate, or alpha, entails. Take a moment to reflect on it before we proceed. The learning rate (alpha) essentially dictates the size of the steps ta...
How to check if gradient descent is working correctly? | Linear regression | Machine learning
มุมมอง 1088 หลายเดือนก่อน
How to check if gradient descent is working correctly? | Linear regression | Machine learning
Linear Regression with Multiple Features: Unveiling the Generic Gradient Descent Algorithm
มุมมอง 2189 หลายเดือนก่อน
Linear Regression with Multiple Features: Unveiling the Generic Gradient Descent Algorithm
Mastering Matrices and Vectors in Machine Learning | Essential Concepts Explained with Examples
มุมมอง 1619 หลายเดือนก่อน
Mastering Matrices and Vectors in Machine Learning | Essential Concepts Explained with Examples
Mastering Multivariate Linear Regression: From Hypothesis to Gradient Descent | Complete Guide!
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Mastering Multivariate Linear Regression: From Hypothesis to Gradient Descent | Complete Guide!
Understanding Gradient Descent for Linear Regression | Machine learning
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Understanding Gradient Descent for Linear Regression | Machine learning
GRADIENT DESCENT Explained | Essential Guide for Machine Learning | Linear Regression Explained
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GRADIENT DESCENT Explained | Essential Guide for Machine Learning | Linear Regression Explained
Understanding the Cost Function | Machine Learning | Optimize Parameters for Accurate Predictions
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Understanding the Cost Function | Machine Learning | Optimize Parameters for Accurate Predictions
Machine Learning | LINEAR REGRESSION with One Variable | Explained in under 5 minutes!
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Machine Learning | LINEAR REGRESSION with One Variable | Explained in under 5 minutes!
What is machine learning? | Where is machine learning used? | Introduction to machine learning
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Understanding Neural Networks: The Building Blocks of AI What Is A Neural Network?
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Understanding Neural Networks: The Building Blocks of AI What Is A Neural Network?
The Ultimate C++ Course (2023) - Part 2 Basic Syntax
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The Ultimate C Course (2023) - Part 2 Basic Syntax
Top 5 VS Code Themes for a Mesmerizing Coding Experience!
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Top 5 VS Code Themes for a Mesmerizing Coding Experience!
APIs Unveiled: A Culinary Analogy Explained | Introduction to Application Programming Interfaces
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APIs Unveiled: A Culinary Analogy Explained | Introduction to Application Programming Interfaces
Top 5 Programming Languages to Learn in 2023 | Stay Ahead of the Tech Scene!
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Top 5 Programming Languages to Learn in 2023 | Stay Ahead of the Tech Scene!
C vs. C++: Choosing the Right Programming Language for You! [2023]
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The Ultimate C++ Course for Beginners and Seasoned Programmers (2023) - Part1 Introduction
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The Ultimate C Course for Beginners and Seasoned Programmers (2023) - Part1 Introduction
Can you tell me what is the background musics name? Nice video by the way 💪!
Thanks alot, that helped me.
You're welcome
Thank you for the info 🤝🏻
You’re welcome!
great topic, thanks 👍
❤🎉
very useful and easy to follow instructions,. subscribed, pls make a video on how to turn a human video into ai generated character video using free tools. i have tried on many tools like lensgo, domo, reface etc. but not able to create yet.
Thank you! I’ll look into it.
And we get all 50 shorts with the same background…does not seem very appealing for a TH-cam channel, doesn’t it?
If you want, you can still change the background of each short to be different. It won’t take too long to manually remove and add a new background to each short, and you would still save a lot of time by automating the other processes.
can you tell me how ca we add sounds in all of these shorts
If you want to add sound to each video, you have to add sound to one video, align it and then manually duplicate it until all videos have sound. I have not yet found a more automated approach. If I find one, I’ll let you know!
Could someone tell me how my channel could get better?
Hey, I am not an expert but I just checked your channel. It looks great. People might not be clicking on the videos because they look like ads, they are all less than 5 minutes. That was my first impression. I dont know much though I just started a TH-cam channel.
Hi, prompts are not there in the description, can you put in description, Thank you.
Yes of course
Thank You@@TheAIGuyExplains
Ai create your video and Ai likes your video. Humans create videos and humans like human video.
Thank you
You're welcome
Thanks a lot.
You're welcome
👍
Pretty great video! thanks for explaining this... Sometimes it is hard to remember how hard things are before we understand them. It took me some time to understand why this method works, and to do the calculations that show that it works... But even if one does not go as deep, it is useful to know that this method exists for linear regression. As for the video, the only thing that could've made it smoother would've to give a reminder of what the matrix X is before giving the formula... something like "recall the we can organize the features on a matrix X like this..." and do it in the example, and then give the formula... but you quickly get into the example so it is not a major issue (though maybe people who are seeing this for the first time could comment whether that was confusing for a second or not). Thanks for the nice video!
Thank you so much for your thoughtful feedback! I really appreciate your positive comments and I'm glad the video was helpful. Your suggestion about providing a quick reminder about the matrix X is valuable, and I'll definitely keep that in mind for future videos. Thanks again for watching and taking the time to share your thoughts!
That’s awesome
Thank you
If you have any questions, feel free to drop them below 👇
great video
Thank you!
If you have any questions, feel free to leave them in the comment section below 👇
This was very helpful
Thank you!
Great video!
Thank you!
I am trying to get started with Neural networks/back propagation, but rn this all too much overwhelming for me. Can u help, from where should i start? I m a cs student and i am already really good with DSA, and now i want to study nueral networks on my own, but i m failing to get started with it
Hi there! I appreciate your enthusiasm for diving into the world of neural networks and backpropagation. I'm currently working on a comprehensive video series about machine learning, starting from the basics. I recommend checking out all the videos starting from 1.1, where I discuss the regression problem. Right now, I'm focusing on explaining linear regression, which forms the foundation of machine learning. Understanding linear regression and logistic regression is crucial before delving into neural networks. After covering these fundamentals, I'll be diving deep into the intricacies of neural networks in the upcoming videos. So I recommend that you start from 1.1 and work your way up to the most recent video! Then once you saw all these video's, you'll have a good understanding about linear- and logistic regression and in a little over a month, I will start on Neural networks.
Awesome... Enjoyed
Thank you
Background music distracting from watching the lesson apart from that I find the lessons good.
Thank you! I totally agree with you, that’s why the video’s after that are without background music
Great video
Thank you!
Great video, thank you for explaining this
You're Welcome!
Great video!
Thank you
awsom
Indeed, Neural networks are amazing
Great video!
Thank you
Feel free to ask any questions in the comment section below 👇
If you get rid of distracting background music, the lesson will be a lot better.
Thank you for sharing your feedback with us. We genuinely appreciate your input. We have ceased the use of background music in the linear regression series starting from 1.3 gradient descent onwards. We hope this adjustment enhances your learning experience.
Great video!
Thank you!
If you have any questions, feel free to drop them in the comment section below 👇
Great video!
Thank you!
Great video! Was explained very well, thank you!
You’re welcome!
This was really helpful. Thank you!
You're welcome!
Great video!
Thank you!
If you have any questions, feel free to ask them in the comment section below 👇
Pretty great and concise... Perhaps it is worth to mention that for linear regression you could find the minimum through a closed formula. This is more interesting somehow for the multidimensional case... but amusingly, finding the minimum involves inverting a matrix, and for very large dimensions, it is faster to just use gradient descent instead of inverting the matrix. Another important part of all those machine learning algorithms is that finding an "exact" minimum is not relevant because whatever minimum you find is a random variable anyways (you will find different minimums for different set of data points), so finding a close enough to the minimum is good enough
Great observation! You're absolutely right. In the context of linear regression, especially in the multidimensional case, closed-form solutions like the normal equation involving matrix inversion can indeed be used to find the exact minimum. However, as you rightly pointed out, the computational cost of inverting matrices, especially for large datasets, can make gradient descent a more efficient choice and this is something I will explain after I made a video about linear regression with multiple features. Additionally, your insight about the variability of the minimum due to different sets of data points is indeed something I can explain in one of the next videos. Thanks for adding this valuable perspective to the discussion and I shall explain both in the upcoming videos!
Looking forward to the next ones!
I just uploaded the video explaining the normal equation method. I would appreciate your feedback on the clarity of the explanation. Are there any areas that you think need further clarification or improvement?
@@TheAIGuyExplains Thanks for keeping me posted... i'll comment on that video!
thank you!
Thank you!
You're welcome
Great video!
Thank you!
Great video!
Thank you!
Feel free to drop any questions in the comments below!
cool explanation... a random reflection: Statistics use data for decision making. The rise of computational power allowed for more sophisticated statistical methods, so can we think of machine learning as statistics on steroids?
Absolutely, that comparison is quite on point! At its core, machine learning does heavily rely on statistical methods, leveraging data for decision-making. The analogy of machine learning being "statistics on steroids" is quite fitting. With the surge in computational power, machine learning takes statistical analysis to new heights by not just analyzing data but also allowing systems to learn, adapt, and make predictions or decisions without explicit programming. It's like statistics empowered by technological advancements, enabling machines to handle more complex tasks and vast amounts of data efficiently. In many ways, machine learning amplifies the capabilities of statistics, providing solutions and insights beyond what traditional statistical methods could achieve. It's a fascinating evolution in the realm of data analysis and decision-making.
@@TheAIGuyExplains Statistics started studying parametric models in part due to a necessity of reducing calculations (for example, modelling the height of a population using a gaussian distribution allows us to say things about the number of people with certain height, or even to distinguish between populations. Such model only require us to calculate two parameters). But as we increased our computational power, we could use more advanced models to the current point in which we can use billions of parameters... In a way, machine learning is the natural evolution of statistics. We are still exploring the limits of the machine learning approach... will we be able to get an Artificial General Intelligence (AGI) by just creating bigger models? will we be able to define what an AGI is? It seems like each time we achieve a milestone in artificial intelligence, we move the goal of AGI even further (In the 60s a machine able to beat humans at chess or go would have been considered intelligent... we thought that a machine able to create art would be intelligent... we also thought that a machine able to hold a coherent conversation would be intelligent...)... It is an exciting time to be a live! (though, to be fair, it is always an exciting time to be alive)
Great video
thank you
Great video!
thank you
neural networks are magic!
I agree
Feel free to drop any questions or requests in the comment section below! I'd love to hear from you and help in any way I can. 👇
Great video!!!
Thank you!
Great video! Thank you!
You're welcome!