Android and iOS app development using machine learning models

This course covers machine learning theory and practical examples based on <Machine Learning, Just the Essentials—Fast!> (Insight, 2025).

Learn how to build deep learning models yourself using intuitive and Pythonic PyTorch. This reflects the latest PyTorch version.

This course covers the theory and practical examples of LLMs, based on <Fundamentals of Large Language Models, Quickly!> (Insight, 2025).

Learn Android mobile programs step by step through practical projects. While creating a mobile app that measures brain waves and plays music using a Bluetooth brainwave measurement device, learn the basics of mobile app development as well as useful techniques such as SQLite, Bluetooth, and Sound that can be used in real life.

Kotlin grammar in a short 3 lectures! Learn the basics of Kotlin grammar and Android programming, and even create your own Android app!

Let's create a Tetris game in Swift.

Create an augmented reality AR app in the new SwiftUI environment. Create your own Snow app using ARKit, RealityKit, Core ML, etc.

Let's understand the major algorithms of machine learning and deep learning, and get to know artificial intelligence!

Learn about MAUI cross-platform development, which lets you develop and run Windows, macOS, iOS, and Android applications with a single code base.

Putting a neural network on an FPGA—the phrase is familiar, but few have actually done it. If you want to stay a step ahead in capstone projects, competitions, or graduation projects, you must be able to go beyond just training a model and actually build and prove how those computations run inside a real chip. This course bridges that exact gap between theory and physical hardware. You will complete a neural network accelerator (NPU) for MNIST digit recognition from start to finish—from reading specifications to Python golden models, Verilog RTL design, and FPGA verification. From Conv layers made with line buffers and parallel MACs, through ReLU, MaxPool, and Flatten, to fully connected layers and Argmax—you will integrate nine blocks into a single chip and prove for yourself that "the Python reference and the hardware output do not differ by even a single bit (Bit-True)." I have even included how to overcome those frustrating moments when the simulation runs fine but the results are subtly different. I will guide you through practical know-how that general deep learning textbooks don't cover, such as integer quantization, array axis reordering, and accumulator overflow prevention. I have distilled the trial and error I experienced over years of RISC-V CPU design and FPGA Bit-True verification into this course so that you don't have to repeat them. By the end of the class, you will have a completed hardware project that allows you to say, "I designed and verified it myself."

This course teaches you how to quickly create various native app features for mobile environments using Javascript, a web service development technology, and walks you through the process of releasing them to the store.

Let's implement a deep learning framework directly with Excel! Let's solve supervised learning/unsupervised learning/reinforcement learning with the implemented framework!

When you learn machine learning, you learn how to improve accuracy. But the moment you try to put that model onto an actual chip, questions no one has answered remain. How many multiplications does this model perform? How much does it need to remember, and for how long? Can the chip specifications you are targeting handle those requirements? Machine learning textbooks talk about accuracy, and semiconductor textbooks talk about circuits, but materials that bridge the gap between them are rare. I repeatedly encountered this gap while implementing RISC-V CPUs and NPUs on FPGAs. The model with the highest accuracy often turned out not to fit on the board. In this course, you will build six algorithms directly in Python, measuring the number of multiplications, comparisons, memory usage, and accuracy each time. Then you will convert those figures into the resources of an actual FPGA board. Saying that something requires a certain number of multiplications is not enough to gauge its size, but knowing that it uses a certain percentage of the board makes the decision clearer. After nine Labs, when the six models are brought together in a single table, an unexpected picture emerges. We spent nine lessons counting multiplications, but multiplications were not what held us back. After that, we reduce floating-point values to integers, manage bit widths, and even establish criteria for evaluating the circuit. You do not need an FPGA board. The course is conducted entirely in Python, and the board is used only as a reference for converting the measurements.

Learn Springboot + Android(Compose) RestAPI Board Creation Faster Than Anyone Develop a CRUD server with Springboot and create your own board with Android!!

Currently, Android development can be done using both Java and Kotlin. You can develop the same Android applications regardless of which language you choose. This course covers most aspects of Android application development, from basics to advanced topics, and all code is written using Kotlin starting from the fundamentals. Additionally, it utilizes Google's latest UI library, Material 3. This course involves completing three projects throughout the entire Android curriculum. By working on these projects, you can master advanced techniques actually used in the industry, such as Hilt, Retrofit, Room Database, and MVVM. Furthermore, you can increase productivity using AI by performing "vibe coding" with Gemini within the Android development tools. Soft Campus will always strive to provide the most up-to-date lectures.

Learn about the data-related property wrappers that appear early in SwiftUI: @State, @Binding, .constant, ObservableObject, @Published, @ObservedObject, @EnvironmentObject, @Environment

This course is for anyone who is curious about developing test automation on Android and iOS devices. This course will enable you to perform automated testing on mobile devices.

We will develop an iPhone-only RPG game using Xcode. We have prepared a lecture so that even those with no knowledge of app development can follow along.

Kotlin Programming Language, which you can learn by practicing with 76 examples. You have to develop in Kotlin starting tomorrow, but you don't have time to study the grammar? Let's quickly learn all the grammar of Kotlin with the examples provided in this lecture.

From perceptrons to backpropagation, systematically learn the core mathematical theories of neural networks and implement them directly in R. Build a solid foundation in deep learning—including activation functions, gradient descent, and multilayer neural networks—while developing the ability to solve classification problems using real-world datasets.