AppInventor’s simplicity can yield striking complexity; much like the art of Piet Mondrian, it allows for the creation of aesthetically pleasing, grid-like layouts. Despite the simplicity of the humble rectangular and vertical arrangements, you can craft an app’s visual landscape with the precision and elegance reminiscent of the iconic artist’s work.
Event-driven programming in the context of Android app development is akin to the anticipation of a package delivery. Just as you can either vigilantly watch your doorstep or await the doorbell’s chime to signal your package’s arrival, programming offers two paradigms. The event-driven paradigm, symbolized by the doorbell scenario, is constructed as a “mad-lib” of “WHEN event occurs, DO action,” where the first part represents the event, and the second part is the callback or action triggered in response. Android apps rely on this paradigm, executing callback functions when specific events transpire, whether driven by user interactions like tapping or swiping, or the passage of time through clocks and timers. Consider the “Mole Mash” app, where a green event-handler block initiates a timer, and a purple callback block moves the mole to random coordinates, breathing life into the game. This dynamic approach empowers developers to craft responsive applications accommodating diverse user experiences.
We continue our quest to delve deeper into the realm of deep learning, refactoring the core functions of our simple neural network to engineer a deep learning model. By introducing the flexibility to customize the number of hidden layers and neurons in each layer, we elevate our model’s capacity for learning intricate patterns in the data. Our code refactoring maintains the simplicity of the six fundamental functions, preserving a user-friendly interface while embracing the power of deep learning. The journey doesn’t end here, as we’re poised to explore the innovations that have made deep learning models more practical and dependable, from techniques like batch normalization and residual network connections to the valuable concept of dropout. As the field of deep learning continues to advance, we eagerly anticipate diving even deeper into its depths.
In this post, we explore the process of building a simple neural network from scratch in PyTorch for binary classification. We discuss the network’s architecture, including the use of ReLU activation in the hidden layer and sigmoid activation in the output layer. We emphasize the importance of data partitioning for training, validation, and testing. The neural network is trained using gradient descent, and we provide insights into initializing weights and biases, implementing forward and backward passes, and updating coefficients. Furthermore, we explain the significance of non-linear activations in making the network a universal function approximator. We demonstrate an approach that is easily extensible to deep learning simply by refactoring code while keeping the same set of functions and interfaces implemented here. Thus, we offer a versatile and scalable neural network architecture.
Discover the incredible world of MIT AppInventor, where app development becomes a breeze, even for those without coding experience. Created by MIT and Google, this platform has empowered over 18 million users to craft a staggering 85 million apps, proving that anyone can bring their ideas to life with the right tool. From interactive show-and-tell experiences to animated games and AI-powered marvels, AppInventor simplifies app creation through its user-friendly drag-and-drop interface and event-driven architecture. Dive into this vibrant community, harness your creativity, and unlock your app potential with MIT AppInventor.
The repository opens a door to data storage in the Data layer of our Android app in MVVM architecture. The Domain layer houses our domain assets such as data models and the business logic. Adhering to the principle of programming to an interface, the Domain layer is where we define an interface containing the abstract functionality we need for data management, using only domain assets. The implementation requires we use the data models and CRUD functions in the Data layer. Adhering to the principle that the Domain layer does not know about other layers but other layers know about Domain layer. we flesh out the implementation of the repository interface in the Data layer’s package “repository”. By using the repository pattern, we put all the overhead of data management – error handling, asynchronous I/O, etc., in one place.
The different layers of MVVM architecture each require that we structure data differently, so we end up with multiple sets of data models representing the same information. In this article, we examine the drivers behind the need for distinct sets of data models. We introduce the “mapper” package within the Data layer which houses the code for mapping between these different sets of data models. We demonstrate a pattern for writing a mapper as an extension function of the source model, illustrating with an example. We conclude by setting the stage for the repository pattern which opens the door to the data in the app’s storage.
The Domain layer serves as a vital bridge between the Data and Presentation layers. This intermediary layer decouples the logic of other layers from each other, promoting separation of concerns while facilitating a seamless data flow. The need for this bridge arises from the distinct design considerations when it comes to storing and working with data in the app. We explore the data models in Domain layer, mirroring their counterparts in the Data layer’s “remote” package. These models serve as the foundation for shaping data for processing and presentation in our app. We delve into the model attributes that enable delivery of a modular quiz with multimedia content. We take this opportunity to examine the trade-offs in choice of FIrebase storage and present an alternative.
We delve into Firestore CRUD operations, where CRUD stands for Create, Retrieve, Update, and Delete, essential for data management. We explore how to integrate Firestore storage into our Android quiz app, following the Model-View-View Model (MVVM) architecture. Our journey includes defining an interface for data operations, creating data models, and implementing Firestore queries, while highlighting the crucial role of the Domain layer in shaping data into information for consumption.
In this series, we explore the development of a scalable Android quiz app, embracing the Model-View-ViewModel (MVVM) architecture. Our app, “Android Ally,” utilizes Jetpack Compose for the user interface and Firebase Firestore for data storage, with Dagger-Hilt for Dependency Injection. At this stage, we assume the Android project is set up with Firebase dependencies, and Firestore contains sample data for modules and quizzes. The app’s primary focus is to facilitate native Android development learning through quizzes organized by modules, each containing questions, answers, and ranks. The MVVM architecture empowers the app with three layers: Data for storage, Presentation for the user interface, and Domain as the bridge between them. We examine code organization within these layers to comprehend the app’s structure, starting with Firebase Firestore client-side code for data retrieval.
Alice, our intrepid explorer of Android Wonderland, had a grand vision for her Vaxin app. She dreamed of allowing users to personalize their experience by choosing an image from their phone’s gallery. Little did she know the adventures that awaited her. Part 1: Alice’s Journey in the Android Wonderland With unwavering determination, Alice declared a […]
Material 3 is Google’s latest design system that brings a refreshed look and feel to Android applications. One of its key components is theming, which includes defining a cohesive color scheme for your app. This guide will focus on using Material 3 for theming, with a particular emphasis on colors, composition, and light and dark […]
We can be incredibly creative with CNNs. They might be designed for images, but with a little ingenuity, we can make them our allies in understanding non-image data like heart sounds. We don’t need to reinvent the wheel and build entirely new models. Instead, we can convert our data into an image format and let CNNs do what they do best. As we keep exploring the possibilities of deep learning, this approach opens exciting doors for improving medical diagnosis and treatment. So, remember, when dealing with non-image data and CNNs, sometimes all you need is a little creative twist.
Learning Like a Little Leaguer: Applying David Perkins’ Seven Principles of Teaching in Craft With Code
David Perkins’ seven principles of teaching, rooted in his childhood baseball experiences, provide valuable insights for hands-on learning environments like Craft With Code. By encouraging students to “play the whole game” by creating projects from the start, making learning relevant and engaging, and emphasizing targeted practice, educators can enhance the effectiveness of hands-on courses. These principles remind us that, much like little leaguers learning baseball, students in Craft With Code courses benefit from grasping the broader context before fine-tuning their skills – a philosophy that transforms hands-on learning into a transformative educational experience.
Ensuring your child receives timely vaccinations is crucial for their health and well-being. However, managing the schedule for 24 vaccinations within the first 18 months of your child’s life can be a daunting task for parents. That’s where the Vaxin app comes to the rescue. With Vaxin, you can effortlessly keep track of your child’s vaccination schedule, leaving you free from the stress of keeping everything in mind. Simply enter your child’s birthday, and you’re all set to ensure they receive the vaccinations they need on time, promoting their health and peace of mind for parents.
In the world of Android app development, effective event-handling is crucial for providing users with a seamless and engaging experience. To tackle the diverse range of user actions, from button clicks to text input and more, the Model-View-ViewModel (MVVM) architecture offers an elegant solution. The `onEvent` function within the View Model acts as a central hub for managing these events. However, the challenge lies in passing these diverse events to `onEvent` while preserving their unique flavors. This challenge is met with the introduction of a sealed class which serves as a versatile wrapper for various events, providing a common interface. This class not only simplifies event management but also ensures that the distinctive characteristics of each user action are retained. Through use of the mechanism described here, Android app developers can streamline event-handling and create exceptional applications that respond seamlessly to user interactions.
In the world of programming, the event-driven paradigm, much like patiently waiting for a doorbell to ring when expecting a package, is a cornerstone of creating dynamic and responsive mobile applications. It involves crafting code that responds to specific events or triggers, bringing interactivity to your apps. Through our exploration and mastery of this event-driven magic using App Inventor’s powerful tools and techniques, you’ll discover how to design and build engaging mobile apps that captivate users, transforming your ideas into interactive digital experiences.
In the evolving landscape of remote work, the issue of monitoring employee productivity while respecting ethical boundaries has taken center stage. The cat-and-mouse game between surveillance measures and tech-savvy workers adept at finding creative solutions has illuminated the need for a delicate balance. This article delves into the intricacies of remote work monitoring, shedding light on the ethical considerations that arise as managers and employees navigate this complex terrain, where trust and transparency are pivotal in maintaining a harmonious and productive remote work environment.
In this rhythmic ode to coding tools, we explore the prowess of Gradio, FastAPI, and Streamlit in the world of technology. Gradio is celebrated for its speed and simplicity, making it an inspiring muse for developers. FastAPI’s power to shape endpoints and its flexibility are likened to a sage’s wisdom, constantly amazing with its versatility. Streamlit’s charm lies in its compromise, making it an ideal companion for day and night app development. The message encourages the reader, referred to as “tech bro,” to embrace these tools, and not be shy but boldly utilize them to architect their tech journey.
Two words, Rest API, unveil the digital alchemy that forges connections in the vast tapestry of the World Wide Web. “Rest,” a guiding acronym for Representational State Transfer, offers a blueprint for harmonious data exchange among machines, encapsulating the ever-changing states of systems like the ebb and flow of stock market data. “API,” or Application Programming Interface, serves as the digital handshake uniting denizens of the internet, be they human or machine. In a virtual universe rife with chaos, protocols emerge as the steadfast guardians of order, and the HTTP protocol acts as the conductor orchestrating seamless data exchanges. Yet, the Rest API emerges as a beacon of efficiency, offering answers to questions posed through a simple URL, unlocking the magic of automation. As the digital sentinel facilitating data’s journey, Rest API wields the power to transform ordinary applications into extraordinary AI marvels, and the FastAI coaching program stands ready to equip Python enthusiasts with the tools to navigate this exhilarating realm.
Imagine this: With just a few lines of code, you can awaken a deep learning marvel, a digital creature that can recognize pets in the blink of an eye. The beauty of it all lies in the fact that you don’t need to be a mathematical wizard, drown in a data deluge, or mortgage your future for a supercomputer. It’s a digital adventure that levels the playing field, opening doors to anyone with the curiosity and a modest amount of programming experience.
At the age of three, I discovered my love for creation through LEGO, which later evolved into a passion for technology and coding. This journey inspired the founding of Craft With Code, a platform dedicated to experiential learning. In an era of rapid technological advancement, our approach empowers students to actively create, fostering problem-solving skills and a digital mindset. We bring Confucius’ wisdom to life: “I hear and I forget. I see and I remember. I do and I understand.” Our students don’t just write code; they make it come alive on devices, equipping them with a versatile toolbox of modern programming skills.