Channel Title: Computer Science with Dr. RCB Channel Description: Hey there, fellow tech enthusiasts! I'm Dr. Ram Charan Baishya. If you have a passion for the world of Computer Science and Programming, you're in the right place. I'm on a mission to simplify the complexities of the digital realm and make the exciting fields of Computer Science- Programming, Web Design and Machine Learning accessible to everyone. With a focus on languages like JS, PHP, Python, HTML, CSS, and C as well as diving into the fascinating realms of Web Design and Machine Learning, we'll explore the ever-evolving landscape of technology together. Expect a mix of hands-on coding tutorials and core computer science concepts to enhance your skills. Let's embark on this learning journey together, where the language of programming becomes not just a set of instructions but a means to create, innovate, and transform ideas into reality. Don't forget to hit that subscribe button and join our growing community of tech enthusiasts. Let's code, create, and learn together! 🚀💻 <video> Student's Attendance Management System with email notification-Complete project with source code Description: Student's Attendance Management System with email notification Complete project with source code #btechprojects #sourcecode #mcaproject #studentsattendacesystem #attendancemanagement Project Source Code: https://github.com/DrRCB/AttendanceAppWithEmail.git Code a Student's Attendance Management System from scratch playlist: https://www.youtube.com/playlist?list=PLJ4-ETiGBrdOZ4kvbzNGidD26M24BLImM Blech CSE Project with source code MCA Project with source code BCA project with source code Batchelor Of Computer Science, BSC computer Science Project with source code "🚀 Explore the world of full-stack web development with our innovative Students Attendance Management System! 📚 Perfect for BTECH, MCA, and Computer Science enthusiasts, this comprehensive project utilizes HTML, CSS, JavaScript, PHP, and MySQL to create a dynamic solution for tracking attendance effortlessly. 💡 With intuitive features like automatic email notifications for low attendance, our system streamlines communication between faculty and students, ensuring no one falls behind. Learn how to configure your local machine to enable SMTP email sending via Gmail, and even generate an app password for seamless integration. 🔍 Dive deep into the source code with our step-by-step walkthrough, where we navigate through the project structure and explain key concepts. Plus, access our curated playlist for in-depth tutorials on web development, programming, and project development, perfect for beginners and seasoned developers alike. 🎓 Whether you're a student looking to understand the intricacies of web development or a curious mind eager to embark on a programming journey, this project serves as an excellent starting point. Don't miss out on unlocking the secrets of full-stack development and database management — join us on this exciting journey today! 🔗 Ready to get started? Watch our demo, download the project, and begin your adventure into the world of software engineering! #WebDevelopment #FullStack #ProjectTutorial" Timecodes 0:00 What is this video about? 2:17 Live demonstration of the main features 6:23 What is the E-mail notification feature? 10:21 What are the reports? 13:31 How to download the source code? 18:19 How to create the tables automatically? 27:31 SMTP configuration to enable email sending feature. 36:02 How to get gmail app passwords for sending emails from programs? 44:02 A brief explanation of the source code 49:53 How to navigate through the source code? 1:01:41 Thank you for watching. </video> <video> Mathematics of neural network Description: In this video, I will guide you through the entire process of deriving a mathematical representation of an artificial neural network. You can use the following timestamps to browse through the content. Timecodes 0:00 Introduction 2:20 What does a neuron do? 10:17 Labeling the weights and biases for the math. 29:40 How to represent weights and biases in matrix form? 01:03:17 Mathematical representation of the forward pass 01:10:29 Derive the math for Backward Pass. 01:11:04 Bringing cost function into the picture with an example 01:32:50 Cost function optimization. Gradient descent Start 01:39:15 Computation of gradients. Chain Rule starts. 04:24:40 Summarization of the Final Expressions 04:38:09 What's next? Please like and subscribe. Feel free to like and follow my Facebook Page https://www.facebook.com/contactDrRCB/ Link to the playlist https://studio.youtube.com/playlist/PLJ4-ETiGBrdOZuDqcuEkGH6_MqGoxy4HW/edit Link to the e-book "Neural Networks and Deep Learning by Michael Nielson" https://static.latexstudio.net/article/2018/0912/neuralnetworksanddeeplearning.pdf The mathematics behind neural networks, particularly feedforward networks, involves concepts from linear algebra, calculus, and optimization. Here is a step-by-step overview of the mathematics involved in the operation of a neural network: Representation of Neurons: In a feedforward neural network, neurons are represented as mathematical functions. Each neuron takes inputs, performs a weighted sum of those inputs, applies an activation function to the sum, and produces an output. Weighted Sum: The weighted sum of inputs for a neuron is calculated by multiplying each input by its corresponding weight and summing them up. Mathematically, for a neuron with n inputs, the weighted sum can be represented as: z = w₁x₁ + w₂x₂ + ... + wₙxₙ Here, w₁, w₂, ..., wₙ represent the weights, and x₁, x₂, ..., xₙ represent the inputs. Activation Function: The weighted sum is then passed through an activation function to introduce non-linearity into the neural network. Common activation functions include sigmoid, ReLU, tanh, and softmax. The choice of activation function depends on the specific problem and network architecture. Forward Propagation: Forward propagation refers to the process of passing inputs through the neural network layer by layer, starting from the input layer and moving towards the output layer. Each layer performs the weighted sum and activation function operations described above. Loss Function: A loss function is used to measure the difference between the predicted output of the neural network and the actual output (ground truth) for a given input. The choice of loss function depends on the specific problem being solved. For example, mean squared error (MSE) is commonly used for regression tasks, while cross-entropy loss is often used for classification tasks. Backpropagation: Backpropagation is a key algorithm for training neural networks. It involves computing the gradients of the loss function with respect to the weights and biases of the network. This process utilizes the chain rule of calculus to efficiently propagate the gradients backward through the layers of the network. Gradient Descent: Once the gradients are computed, the weights and biases are updated using an optimization algorithm such as gradient descent. Gradient descent adjusts the weights and biases in the direction opposite to the gradient to minimize the loss function. Training Iterations: The process of forward propagation, backpropagation, and weight updates is repeated iteratively until the network's performance reaches a satisfactory level. Each iteration is called an epoch, and the entire dataset is typically used for each epoch. By understanding these mathematical concepts and algorithms, you can implement a neural network from scratch by creating the necessary data structures and implementing the mathematical operations described above. However, it's important to note that implementing a neural network from scratch requires a solid understanding of linear algebra, calculus, and programming, as well as knowledge of efficient algorithms and numerical optimization techniques. Mathematics behind neural networks Neural network workings Implementing neural networks from scratch Artificial neural network mathematics Understanding neural networks step by step Mathematics of artificial neural networks Backpropagation explained step by step Chain rule in neural networks Chain rule of partial differentiation in backpropagation Gradient computation in neural networks Deep learning mathematics Mathematics of feedforward networks Exploring the math behind neural networks Introduction to deep learning mathematics Neural network implementation math Mathematics behind neural networks. How does neural network work? Implement a neural network from scratch. </video> <video> Student Attendance Management System-Complete project with source code Description: Student Attendance Management System-Complete project with source code. Student Attendance Management System-Part 7-Final Part #studentattendancesystem #attendancemanagement Source Code GitHub Link https://github.com/DrRCB/atteendnceappCompleteProjectSourceCode.git Attendance Management System With e-mail notifications https://youtu.be/9OOiJNO5i9s Student attendance management system PHP MYSQL HTML CSS JAVASCRIPT AJAX FULL STACK WEB DEVELOPMENT Using PHP, MYSQL, AJAX, HTML, CSS, and JAVASCRIPT Code from scratch-Student attendance management system-Complete Project Tutorial-Playlist Link: https://www.youtube.com/watch?v=I1ZZT_fw-xU&list=PLJ4-ETiGBrdOZ4kvbzNGidD26M24BLImM&pp=gAQBiAQB In this extensive series on FULL STACK WEB DEVELOPMENT, we are delving into the construction of a Student Attendance Management System. Using a combination of PHP, MYSQL, AJAX, HTML, CSS, and JAVASCRIPT, you will gain insights into the entire process. Whether you are a novice or a seasoned developer, this step-by-step guide equips you with the essential knowledge to successfully undertake a FULL STACK WEB DEVELOPMENT Project starting from the ground up. Here's what you'll discover in this video: Setting up the Development Environment: We'll start by preparing the tools and technologies you need for this project. Creating the Database: Learn how to design a MySQL database to store student and attendance information efficiently. Building the Front-End: We'll design a user-friendly web interface using HTML and CSS for managing student data and recording attendance. PHP for Backend Logic: Implement the server-side functionality using PHP to handle user requests, database interactions, and data processing. JavaScript & AJAX: Enhance the user experience by adding interactive features with JavaScript and AJAX, making your system more dynamic and responsive. Attendance Tracking: We'll demonstrate how to mark student attendance and update the database in real-time without page refresh. Reporting & Analytics: Create attendance reports and gain insights into attendance trends using PHP and MySQL. Security and User Authentication: Learn how to secure your system and implement user authentication to restrict access to authorized users only. By the end of this tutorial, you'll have a fully functional Student Attendance Management System that you can use for your school, college, or educational institution. Plus, you'll gain valuable insights into web development and database management. Don't forget to like, share, and subscribe for more in-depth tutorials on web development and programming. Let's get started on this exciting journey of building a Student Attendance Management System together! Timecodes 0:00 Introduction. What have been done till Part-06 4:19 What are we going to achieve? 9:27 Coding starts. 2:03:04 When will the source code be available? </video> <video> Student Attendance Management System. Page Loader. PART-5 Description: Student Attendance Management System. Page Loader in HTML and CSS. PHP CRUD Student Attendance Management System PART-5 In this extensive series on STEP BY STEP FULL STACK WEB DEVELOPMENT, we are delving into the construction of a Student Attendance Management System. Using a combination of PHP, MYSQL, AJAX, HTML, CSS, and JAVASCRIPT, you will gain insights into the entire process. Whether you are a novice or a seasoned developer, this step-by-step guide equips you with the essential knowledge to successfully undertake a FULL STACK WEB DEVELOPMENT Project starting from the ground up. In this video we will implement a SPINNING LOADER in HTML and CSS. Here are the main CSS features used in the code: Selectors: Class Selectors: Used to select elements with specific classes, such as .diverror, .applyerrordiv, .lockscreen, .applylockscreen, and .spinner. Properties and Values: background-color: Sets the background color of elements. color: Sets the text color of elements. height and width: Specifies the height and width of elements. display: Defines how an element should be displayed (e.g., flex for flexbox layout). justify-content and align-items: Control the alignment of items within a flex container. visibility: Determines whether an element is visible or hidden. opacity: Controls the opacity (transparency) of elements. transition: Specifies transition effects for CSS properties. letter-spacing: Adjusts the spacing between characters in text. Keyframes Animation: @keyframes: Defines a set of animation steps and their corresponding properties. animation: Applies animation to an element, referencing the defined keyframes. Positioning and Layout: position: Defines the positioning method for an element (e.g., fixed). flex-direction: Specifies the direction of the main axis for a flex container. Border and Border Radius: border-radius: Sets the radius of rounded corners for elements. border-top, border-right, border-bottom, border-left: Defines border styles for different sides of an element. Colors: RGB color values: Used to set colors with the rgb() function. Transformations: transform: Used to apply transformations to elements, such as translation and rotation. Animation Timing: linear and infinite: Control the timing and repetition of animations. Code from scratch-Student attendance management system-Complete Project Tutorial-Playlist Link: https://www.youtube.com/watch?v=I1ZZT_fw-xU&list=PLJ4-ETiGBrdOZ4kvbzNGidD26M24BLImM&pp=gAQBiAQB Timecodes 0:00 What are We going to do in this video 1:41 Why and when we might need a busy loader in a webpage 3:22 CODING - HTML and CSS coding for the FALLING DOWN animation 10:48 CODING - HTML and CSS coding for the LOCK SCREEN SPINNING LOADER 16:38 DONE! What have we built? 18:34 What's next? 19:14 Thank you for watching. Please LIKE, COMMENT, SHARE, and SUBSCRIBE. </video> <video> Student Attendance Management System With SOURCE CODE #attendancemanagement Description: Student Attendance Management System With SOURCE CODE. FULL STACK WEB DEVELOPMENT FROM SCRATCH #attendancemanagement Student attendance management system PHP MYSQL HTML CSS JAVASCRIPT AJAX FULL STACK WEB DEVELOPMENT Using PHP, MYSQL, AJAX, HTML, CSS, and JAVASCRIPT Attendance Management System With e-mail notifications https://youtu.be/9OOiJNO5i9s Code from scratch-Student attendance management system-Complete Project Tutorial-Playlist Link: https://www.youtube.com/watch?v=I1ZZT_fw-xU&list=PLJ4-ETiGBrdOZ4kvbzNGidD26M24BLImM&pp=gAQBiAQB In this extensive series on FULL STACK WEB DEVELOPMENT, we are delving into the construction of a Student Attendance Management System. Using a combination of PHP, MYSQL, AJAX, HTML, CSS, and JAVASCRIPT, you will gain insights into the entire process. Whether you are a novice or a seasoned developer, this step-by-step guide equips you with the essential knowledge to successfully undertake a FULL STACK WEB DEVELOPMENT Project starting from the ground up. Here's what you'll discover in this video: Setting up the Development Environment: We'll start by preparing the tools and technologies you need for this project. Creating the Database: Learn how to design a MySQL database to store student and attendance information efficiently. Building the Front-End: We'll design a user-friendly web interface using HTML and CSS for managing student data and recording attendance. PHP for Backend Logic: Implement the server-side functionality using PHP to handle user requests, database interactions, and data processing. JavaScript & AJAX: Enhance the user experience by adding interactive features with JavaScript and AJAX, making your system more dynamic and responsive. Attendance Tracking: We'll demonstrate how to mark student attendance and update the database in real-time without page refresh. Reporting & Analytics: Create attendance reports and gain insights into attendance trends using PHP and MySQL. Security and User Authentication: Learn how to secure your system and implement user authentication to restrict access to authorized users only. By the end of this tutorial, you'll have a fully functional Student Attendance Management System that you can use for your school, college, or educational institution. Plus, you'll gain valuable insights into web development and database management. Don't forget to like, share, and subscribe for more in-depth tutorials on web development and programming. Let's get started on this exciting journey of building a Student Attendance Management System together! Timecodes 0:00 Introduction 0:29 Demonstration of Student attendance management system 4:45 Chapter Structure of the Student Attendance Management System 7:58 Please like subscribe and share </video> <video> Student Attendance Management System. Responsive Front-End. PART-6 Description: Student Attendance Management System. Responsive website using CSS grid, flexbox, and Media Queries. Attendance System PART-6 Responsive Webpage Design with CSS Flex, Grid, and Media Queries. Student Attendance System PART-6 In this extensive series on FULL STACK WEB DEVELOPMENT, we are delving into the construction of a Student Attendance Management System. Using a combination of PHP, MYSQL, AJAX, HTML, CSS, and JAVASCRIPT, you will gain insights into the entire process. Whether you are a novice or a seasoned developer, this step-by-step guide equips you with the essential knowledge to successfully undertake a FULL STACK WEB DEVELOPMENT Project starting from the ground up. In this Video we will learn how to Create Responsive Web Layouts with CSS: Grid, Flexbox, and Media Queries. We will use several important CSS concepts to create a responsive and visually appealing web page layout. Here's an overview of these concepts: Universal Selector (*): The universal selector targets all elements on the page and is used to set default styles for all elements. Box Model Properties: margin: 0; and padding: 0; reset the margin and padding of all elements to zero. box-sizing: border-box; ensures that padding and border values are included in the element's total width and height. Custom Properties (CSS Variables): Custom properties, such as --fontsize, --gap, and --padding, are declared and used throughout the code to make it more maintainable and adaptable to different screen sizes. Layout and Display Properties: display: flex; is applied to the body element, creating a flex container and centering content. display: grid; is used to create grid layouts for various sections of the page. Grid Layout (Grid): Grid properties, including grid-template-columns, grid-template-rows, and grid-template-areas, define the grid structure for specific sections like .page, .header-area, and more. gap: var(--gap); sets the spacing between grid items and elements within sections. Flexbox (Flex): Flexbox properties, such as justify-content and align-items, are used to control the alignment and positioning of elements within flex containers. Areas like .logo-area, .logout-area, and others within the grid layout use Flexbox for alignment. Media Queries (Media Queries): Media queries are employed to apply different styles based on screen width: @media only screen and (min-width: 600px) and (min-width: 767px) adjust custom properties like --fontsize to make the design responsive to different screen sizes. Animations: The animation property and @keyframes are used for animations. For instance, the fadein and tilt animations are applied to elements for dynamic effects. Typography and Font Styling: Font-related properties like font-size, font-weight, and font-family are used to define font styles for text within various sections. Sizing and Spacing: width, height, and padding properties are utilized to control the size and spacing of elements. Colors: Properties like background-color, color, and border-color set the colors of various elements and backgrounds. By combining these CSS concepts, the code achieves a responsive and visually appealing web page layout. It maintains a consistent design while adapting to different screen sizes through the use of Grid, Flexbox, and Media Queries, ensuring a dynamic and flexible user experience. Code from scratch-Student attendance management system-Complete Project Tutorial-Playlist Link: https://www.youtube.com/watch?v=I1ZZT_fw-xU&list=PLJ4-ETiGBrdOZ4kvbzNGidD26M24BLImM&pp=gAQBiAQB Timecodes 0:00 Introduction. Let's get into the flow. 4:10 Responsive Design: Mobile vs Desktop Views. 7:39 Where CSS GRID will be used? 11:38 CODING- Responsive Design with CSS: Grid, Flexbox, and Media Queries 57:42 CODING- Change color on mouse hover CSS 59:26 CODING- The CSS TILT effect 1:02:42 What have we built? 1:07:19 What's NEXT? 1:09:27 Thank you for watching. Please LIKE, COMMENT, SHARE, and SUBSCRIBE. </video> <video> Student Attendance Management System. LOGIN PAGE using HTML, CSS, and JavaScript. PART-3 Description: Student Attendance Management System Code a LOGIN PAGE using HTML, CSS and JavaScript from scratch. Student Attendance Management System. In this extensive series on FULL STACK WEB DEVELOPMENT, we are delving into the construction of a Student Attendance Management System. Using a combination of PHP, MYSQL, AJAX, HTML, CSS, and JAVASCRIPT, you will gain insights into the entire process. Whether you are a novice or a seasoned developer, this step-by-step guide equips you with the essential knowledge to successfully undertake a FULL STACK WEB DEVELOPMENT Project starting from the ground up. This video is a step-by-step tutorial on building a secure user authentication login form for your Student Attendance Management System. In this video, we'll guide you through the process of creating a user-friendly login interface with HTML, CSS, and a touch of JavaScript. 🔒 Key CSS features used: Universal Selector (*): Resets margin, padding, and sets the box-sizing property to border-box for all elements. Flex Display: Utilized for centering content both horizontally and vertically within the body. Pseudo-Class :hover: Used to apply styles when an element is hovered over, e.g., for the .loginform. @keyframes Animation: Defines a custom animation named blink with keyframes for creating a blinking effect. Adjacent Sibling Selector (+): Used to select an element that is immediately preceded by a specific element, such as changing label styles when input fields are hovered, focused, or contain valid input. Transitions: Employed to create smooth animations when properties change, such as the transition of properties like color, border-bottom, and box-shadow. Pseudo-Classes (:valid, :focus): Applied to style input elements based on their validation state and focus, respectively. Background Color: Set to achieve different visual states, e.g., the .activecolor and .inactivecolor. Box-Shadow Property: The box-shadow property is used to create a glowing effect when the .loginform is hovered over. This adds a shadow to the element, creating a visually appealing glow effect. The box-shadow property is animated as part of the hover state to enhance the effect. Code from scratch-Student attendance management system-Complete Project Tutorial-Playlist Link: https://www.youtube.com/watch?v=I1ZZT_fw-xU&list=PLJ4-ETiGBrdOZ4kvbzNGidD26M24BLImM&pp=gAQBiAQB Timecodes 0:00 Understanding the basics 5:19 CODING begins-HTML, CSS and JAVASCRIPT 26:26 What we have built? 27:55 Why and Where JavaScript is used? 31:41 Thanks for watching. Please like and subscribe. </video> <video> Student Attendance Management System. Design ER diagram.PART-1 Description: ER diagram for Student Attendance Management System. ER diagram to relational schema. ER diagram- In the context of Student Attendance Management System Using PHP, MYSQL, AJAX, HTML, CSS, and JAVASCRIPT. PHP CRUD AJAX. PHP Full stack web development project. FULL STACK WEB DEVELOPMENT Complete web development project using PHP and MySQL CRUD operation. Timecodes 0:00 Overall project requirement. 6:09 Functional Requirements of the Student attendance management system 12:08 ER diagram of the Student Attendance Management System 18:53 Designing the Tables from the ER diagram of the Student Attendance Management System 27:12 What we need to install to start developing the project Code from scratch-Student attendance management system-Complete Project Tutorial-Playlist Link: https://www.youtube.com/watch?v=I1ZZT_fw-xU&list=PLJ4-ETiGBrdOZ4kvbzNGidD26M24BLImM&pp=gAQBiAQB In this extensive series on FULL STACK WEB DEVELOPMENT, we are delving into the construction of a Student Attendance Management System. Using a combination of PHP, MYSQL, AJAX, HTML, CSS, and JAVASCRIPT, you will gain insights into the entire process. Whether you are a novice or a seasoned developer, this step-by-step guide equips you with the essential knowledge to successfully undertake a FULL STACK WEB DEVELOPMENT Project starting from the ground up. Here's what you'll discover in this video: Setting up the Development Environment: We'll start by preparing the tools and technologies you need for this project. Creating the Database: Learn how to design a MySQL database to store student and attendance information efficiently. Building the Front-End: We'll design a user-friendly web interface using HTML and CSS for managing student data and recording attendance. PHP for Backend Logic: Implement the server-side functionality using PHP to handle user requests, database interactions, and data processing. JavaScript & AJAX: Enhance the user experience by adding interactive features with JavaScript and AJAX, making your system more dynamic and responsive. Attendance Tracking: We'll demonstrate how to mark student attendance and update the database in real-time without page refresh. Reporting & Analytics: Create attendance reports and gain insights into attendance trends using PHP and MySQL. Security and User Authentication: Learn how to secure your system and implement user authentication to restrict access to authorized users only. By the end of this tutorial, you'll have a fully functional Student Attendance Management System that you can use for your school, college, or educational institution. Plus, you'll gain valuable insights into web development and database management. Don't forget to like, share, and subscribe for more in-depth tutorials on web development and programming. Let's get started on this exciting journey of building a Student Attendance Management System together! </video> <video> Student Attendance Management System. Execute SQL commands from PHP programs.PART-2 Description: Student Attendance Management System. Effortless Database Creation with PHP Scripts How to execute SQL commands from PHP programs-In the context of Student Attendance Management System Using PHP, MYSQL, AJAX, HTML, CSS, and JAVASCRIPT. Php MySQL project. Php MySQLStudent Attendance Management System. Source Code at github: https://github.com/DrRCB/attendanceapp.git Timecodes 0:00 XAMPP MySQL from PHP MYADMIN 5:33 How to create table from PHP MY ADMIN 9:40 PHP MYSQL connect to database. What is a connection object in PHP MySQL? 34:28 Create a PHP Database Class to connect to MySQL Database automatically. 50:04 CODING-With background music-PHP script to Create database tables 1:00:28 CODING-With background music-PHP script to Insert Records into database tables 1:20:31 Why and How to perform SQL JOIN operation? Code from scratch-Student attendance management system-Complete Project Tutorial-Playlist Link: https://www.youtube.com/watch?v=I1ZZT_fw-xU&list=PLJ4-ETiGBrdOZ4kvbzNGidD26M24BLImM&pp=gAQBiAQB In this extensive series on FULL STACK WEB DEVELOPMENT, we are delving into the construction of a Student Attendance Management System. Using a combination of PHP, MYSQL, AJAX, HTML, CSS, and JAVASCRIPT, you will gain insights into the entire process. Whether you are a novice or a seasoned developer, this step-by-step guide equips you with the essential knowledge to successfully undertake a FULL STACK WEB DEVELOPMENT Project starting from the ground up. Here's what you'll discover in this video: Setting up the Development Environment: We'll start by preparing the tools and technologies you need for this project. Creating the Database: Learn how to design a MySQL database to store student and attendance information efficiently. Building the Front-End: We'll design a user-friendly web interface using HTML and CSS for managing student data and recording attendance. PHP for Backend Logic: Implement the server-side functionality using PHP to handle user requests, database interactions, and data processing. JavaScript & AJAX: Enhance the user experience by adding interactive features with JavaScript and AJAX, making your system more dynamic and responsive. Attendance Tracking: We'll demonstrate how to mark student attendance and update the database in real-time without page refresh. Reporting & Analytics: Create attendance reports and gain insights into attendance trends using PHP and MySQL. Security and User Authentication: Learn how to secure your system and implement user authentication to restrict access to authorized users only. By the end of this tutorial, you'll have a fully functional Student Attendance Management System that you can use for your school, college, or educational institution. Plus, you'll gain valuable insights into web development and database management. Don't forget to like, share, and subscribe for more in-depth tutorials on web development and programming. Let's get started on this exciting journey of building a Student Attendance Management System together! </video> <video> Student Attendance Management System-FULL STACK WEB DEVELOPMENT FROM SCRATCH #attendancemanagement Description: Code from scratch-Student attendance management system-Complete Project Tutorial-Playlist Link: https://www.youtube.com/watch?v=I1ZZT_fw-xU&list=PLJ4-ETiGBrdOZ4kvbzNGidD26M24BLImM&pp=gAQBiAQB In this extensive series on FULL STACK WEB DEVELOPMENT, we are delving into the construction of a Student Attendance Management System. Using a combination of PHP, MYSQL, AJAX, HTML, CSS, and JAVASCRIPT, you will gain insights into the entire process. Whether you are a novice or a seasoned developer, this step-by-step guide equips you with the essential knowledge to successfully undertake a FULL STACK WEB DEVELOPMENT Project starting from the ground up. Here's what you'll discover in this video series: Setting up the Development Environment: We'll start by preparing the tools and technologies you need for this project. Creating the Database: Learn how to design a MySQL database to store student and attendance information efficiently. Building the Front-End: We'll design a user-friendly web interface using HTML and CSS for managing student data and recording attendance. PHP for Backend Logic: Implement the server-side functionality using PHP to handle user requests, database interactions, and data processing. JavaScript & AJAX: Enhance the user experience by adding interactive features with JavaScript and AJAX, making your system more dynamic and responsive. Attendance Tracking: We'll demonstrate how to mark student attendance and update the database in real-time without page refresh. Reporting & Analytics: Create attendance reports and gain insights into attendance trends using PHP and MySQL. Security and User Authentication: Learn how to secure your system and implement user authentication to restrict access to authorized users only. By the end of this tutorial, you'll have a fully functional Student Attendance Management System that you can use for your school, college, or educational institution. Plus, you'll gain valuable insights into web development and database management. Don't forget to like, share, and subscribe for more in-depth tutorials on web development and programming. Let's get started on this exciting journey of building a Student Attendance Management System together! </video> <video> Student Attendance Management System. Login Page Back-End coding -PART-4 Description: Student Attendance Management System. Login Page Back-End coding in PHP and MySQL. PHP AJAX CRUD PROJECT. STUDENT ATTENDANCE SYSTEM COMPLETE PROJECT Code from scratch-Student attendance management system-Complete Project Tutorial-Playlist Link: https://www.youtube.com/watch?v=I1ZZT_fw-xU&list=PLJ4-ETiGBrdOZ4kvbzNGidD26M24BLImM&pp=gAQBiAQB In this extensive series on FULL STACK WEB DEVELOPMENT, we are delving into the construction of a Student Attendance Management System. Using a combination of PHP, MYSQL, AJAX, HTML, CSS, and JAVASCRIPT, you will gain insights into the entire process. Whether you are a novice or a seasoned developer, this step-by-step guide equips you with the essential knowledge to successfully undertake a FULL STACK WEB DEVELOPMENT Project starting from the ground up. Here's what you'll learn in this video: Implementing AJAX to enable seamless and dynamic user interactions. Handling form submissions and validating user input. Managing sessions to keep users authenticated. Displaying error messages and feedback for a user-friendly experience. By the end of this tutorial, you'll have the skills to create a fully functional login page with robust security features, ensuring the confidentiality of user data. Don't forget to like, share, and subscribe for more web development tutorials to enhance your skills. Get ready to elevate your web development expertise and create secure login pages for your web projects. Let's get started! Timecodes 0:00 Introduction and the background story 6:31 CODING begins-Connecting to the Back-end. PHP,MYSQL,AJAX,JAVASCRIPT 26:31 What we have built? 30:26 Lets understand the code and Its FLOW. 48:59 What's in the Next Part-Two Front-End features to enhance user experience. 54:20 Thank You For Watching. Don't Forget o LIKE, COMMENT, SUBSCRIBE, and SHARE </video> <video> Neural Network from scratch in 60 seconds using Only NUMPY. No Tensorflow/PyTorch/Scikit-Learn Description: The long form of the short Code a Neural Network from scratch. Implement neural network neural network. Deep Learning https://youtu.be/YqFHs3gdyac Mathematics of neural networks How does neural network work. Implement neural network from scratch https://youtu.be/b7NnMZPNIXA This playlist https://www.youtube.com/watch?v=UDIIGYF5Xwc&list=PLJ4-ETiGBrdOZuDqcuEkGH6_MqGoxy4HW&pp=gAQBiAQB </video> <video> Backpropagation. How a neural network learns? Code backpropagation. single-neuron neural network Description: What is Backpropagation? Welcome to this enlightening video, where I will guide you through the process of implementing a single-neuron neural network from scratch. Throughout this tutorial, we will explore the core concepts that drive neural network training, including the forward pass, cost function, gradient descent, and the pivotal backpropagation algorithm. By mastering these foundational ideas, you will be well-equipped to tackle more complex neural network architectures. To facilitate your understanding, we will delve into the mathematics behind backpropagation, specifically focusing on the calculus principles involved. In particular, we will explore the chain rule and its role in backpropagation, unraveling how it enables neural networks to optimize their performance. Furthermore, we will embark on a hands-on journey by writing Python code to construct a simple yet powerful neural network. This practical implementation will enable you to witness the translation of concepts into executable code, solidifying your understanding and empowering you to explore and experiment further. For your convenience, the code will be made available on GitHub, ensuring easy access and the ability to refer back to it whenever needed. This comprehensive resource will serve as a valuable reference for your ongoing neural network endeavors. Prepare yourself for an engaging and enlightening experience as we unravel the intricacies of a single-neuron neural network. By mastering the mathematics behind backpropagation, specifically the chain rule, and gaining practical coding skills, you will be well-prepared to tackle more complex neural network architectures and unleash the full potential of this powerful technology. Join me on this educational journey, and together, let's build and expand upon our understanding of neural networks. Backpropagation is an algorithm commonly used in training artificial neural networks, including multi-layer perceptrons (MLPs), to adjust the weights and biases of the network based on the error between the predicted output and the desired output. It is a key component of the learning process in neural networks. The backpropagation algorithm works by propagating the error from the output layer of the neural network back to the input layer, adjusting the weights and biases of each neuron along the way. The goal is to minimize the error or loss function between the predicted output and the actual output. To illustrate the backpropagation algorithm, let's consider an example with a single neuron in a neural network. Assume we have a neuron with two inputs, x1 and x2, and corresponding weights w1 and w2, with a bias term b. The output of the neuron is calculated as follows: Output = sigmoid(w1x1 + w2x2 + b) The sigmoid function is a common activation function used in neural networks to introduce non-linearity. Now, let's say we have a desired output, y, and we want to adjust the weights and bias of the neuron to minimize the error between the predicted output and the desired output. Forward Propagation: First, we feed the inputs x1 and x2 through the neuron and calculate the predicted output using the current weights and bias. Calculate the Error: Next, we calculate the error between the predicted output and the desired output using a suitable loss function, such as the mean squared error (MSE). Error = 0.5 * (y - Output)^2 Backpropagation: To update the weights and bias, we need to calculate how the error changes with respect to each weight and bias in the neuron. This is done through backpropagation. a. Calculate the gradient of the error with respect to the output: delta = (Output - y) * sigmoid_derivative(Output) The sigmoid_derivative() function computes the derivative of the sigmoid function with respect to its input. b. Calculate the partial derivatives of the error with respect to the weights and bias: dE/dw1 = delta * x1 dE/dw2 = delta * x2 dE/db = delta Update the Weights and Bias: Finally, we update the weights and bias using a learning rate (alpha) to control the size of the weight update. w1 = w1 - alpha * dE/dw1 w2 = w2 - alpha * dE/dw2 b = b - alpha * dE/db This process is repeated iteratively for a number of training examples, adjusting the weights and bias in the direction that reduces the error. By updating the weights and biases using the gradients calculated through backpropagation, the neural network can learn to make better predictions over time. Note that this example demonstrates the backpropagation algorithm for a single neuron, but in practice, backpropagation is used to train multi-layer neural networks with multiple neurons in each layer. </video> <video> Deep learning for beginners-Code a Neural Network from scratch. Chain rule and Gradient descent Description: code for the video is avaiable in Github on the following link https://github.com/DrRCB/deepPearning.git Welcome to this informative video, Build a Neural Network from scratch. Deep learning for beginners. Chain rule. Gradient descent, where I will guide you through the process of implementing a neuron neural network from scratch. We shall see how to build a neural network from scratch. First we shall derive the math in neural network and then we will implement neural network from scratch. Throughout this tutorial, we will explore the core concepts that drive neural network training, including the forward pass, cost function, gradient descent, and backpropagation. By delving into the mathematical foundations, specifically the calculus behind backpropagation, you will gain a deeper understanding of how neural networks operate. An essential aspect we will focus on is the chain rule and its significance in backpropagation. Understanding how the chain rule is used in this process will enable you to effectively optimize your neural network's performance. In addition to building the neural network from scratch without relying on any external libraries, we will also explore the implementation of different activation functions, such as ReLU and Sigmoid. Not only will we learn how to implement these activation functions, but we will also understand how to calculate their derivatives during backpropagation. Furthermore, we will compare Sigmoid and ReLU, examining their characteristics and exploring their pros and cons. To facilitate your learning experience, we will write Python code to construct a simple neural network using only the powerful NumPy library. This practical implementation will provide you with the necessary tools to translate mathematical concepts into functional code. For your convenience, the code will be made available on GitHub. This comprehensive resource will serve as a valuable reference for your continued exploration of neural networks. Get ready for an engaging and enlightening experience as we dive into from understanding the mathematics behind them to implementing code that brings them to life. Join me on this educational journey as we bridge the gap between theory and code, equipping you with the knowledge and skills to build artificial neural networks from scratch. The fascinating world of neural networks awaits you! Neural networks are a fundamental component of deep learning, which is a subfield of machine learning. They are mathematical models inspired by the structure and function of biological brains. Neural networks consist of interconnected nodes called neurons, which perform computations on input data to produce output predictions. Deep learning refers to neural networks with multiple hidden layers, allowing them to learn and represent complex patterns and relationships in the data. By stacking multiple layers, deep neural networks can extract increasingly abstract features from the input data. Backpropagation is a crucial algorithm used to train neural networks. It computes the gradients of the network's parameters with respect to a loss function, enabling the network to update its weights and biases in the direction that minimizes the loss. The backpropagation algorithm consists of two main steps: forward propagation and backward propagation. In forward propagation, the input data is passed through the network, and the activations of each layer are calculated sequentially. The output prediction is then compared to the desired output, and a loss function is used to measure the discrepancy between them. In backward propagation, the gradients of the loss function with respect to the network's parameters are calculated. This is done by iteratively applying the chain rule of calculus from the output layer to the input layer. The gradients indicate the sensitivity of the loss function to changes in each parameter. Activation functions play a crucial role in neural networks by introducing non-linearity. The rectified linear unit (ReLU) and sigmoid functions are common activation functions. ReLU returns the input if it is positive and zero otherwise, while sigmoid squashes the input into the range [0, 1]. The derivatives of activation functions are necessary for the backpropagation algorithm. The derivative of the ReLU function is 1 for positive inputs and 0 for negative inputs. The derivative of the sigmoid function is calculated as the sigmoid function multiplied by (1 - sigmoid function). The simplest neural network typically refers to a single-layer perceptron, which is the basic building block of neural networks. It has no hidden layers and only consists of input and output layers. </video> <video> Neural Network from scratch using Only NUMPY Description: source code: GitHub link https://github.com/DrRCB/code_a_neural_network.git Mathematics of neural networks How does a neural network work. Implement neural network from scratch https://youtu.be/b7NnMZPNIXA This playlist https://www.youtube.com/watch?v=UDIIGYF5Xwc&list=PLJ4-ETiGBrdOZuDqcuEkGH6_MqGoxy4HW&pp=gAQBiAQB Neural Network from scratch using Only NUMPY. No Tensorflow/PyTorch/Scikit-Learn Libraries 🚀 Dive into the fundamentals of deep learning with our latest tutorial! In this video, we'll guide you through the process of building a Neural Network from scratch using only NumPy, the powerful numerical computing library in Python. Whether you're a beginner looking to understand the inner workings of neural networks or an experienced developer aiming to strengthen your foundational knowledge, this tutorial is tailored for you. 🔍 What You'll Learn: Introduction to Neural Networks: Uncover the basic concepts and architecture of neural networks. Forward Propagation: Learn how to implement the forward pass to make predictions using your neural network. Loss Function: Understand the importance of loss functions and how they guide the network towards better performance. Backpropagation: Dive into the intricacies of backpropagation, the key algorithm for training neural networks. Gradient Descent: Implement gradient descent to optimize the network's parameters and improve its accuracy. Code Walkthrough: Follow along with our step-by-step coding demonstration using Python and NumPy. 🛠️ Prerequisites: Basic knowledge of Python programming. Familiarity with machine learning concepts is a plus but not required. 👨‍💻 Who Is This For: Beginners and intermediate-level developers interested in understanding the fundamentals of neural networks. Python enthusiasts looking to expand their skills in deep learning. 🎓 By the end of this tutorial, you'll have a solid understanding of the core components of a neural network and be equipped to build your own models from scratch using NumPy. 📚 Resources: Code snippets and resources will be available on our GitHub repository. Further readings and references for those who want to delve deeper into neural network concepts. 🚨 Don't forget to subscribe, like, and hit the notification bell to stay updated with our latest tutorials! Let's embark on this coding journey together and demystify the world of neural networks. Happy coding! 🚀🤖 #NeuralNetworks #DeepLearning #NumPyTutorial </video>

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