Core Neural Network Theory and Practice

Learn the core functions of SPSS in a short period to pass the Social Survey Analyst Level 2 practical exam. Master SPSS know-how along with problem-solving techniques—ranging from data entry to descriptive statistics, cross-tabulation, t-tests, ANOVA, and correlation/regression analysis—to perfectly prepare for the practical exam.

We are rooting for your success in your exam preparation. The lecture is based on the content included in the PDF materials. PDF download link: https://www.inflearn.com/clip/1364/%EC%82%AC%ED%9A%8C%EC%A1%B0%EC%82%AC%EB%B6%84%EC%82%AC-2%EA%B8%89-%ED%95%84%EA%B8%B0

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.

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 is a lecture that studies the physical information neural network, one of the next-generation innovative technologies of deep learning, and implements it directly using Pytorch. Let's learn the next-generation innovative technology of artificial intelligence with me!

To directly design AI semiconductors such as NPUs, Transformer accelerators, and mini LLMs, you must understand the theoretical reasons behind the operations occurring within them. This course distills the essential theories required for hardware design—from a single neuron to CNNs, Transformers, and LLMs—into a compact three-part series. Rather than memorizing formulas, you will gain an intuition for "why things work the way they do," as a current professor with 30 years of experience in the semiconductor industry bridges the gap between software theory and hardware implementation.

DNN, CNN, RNN? A deep learning practical course where you can perfectly organize core deep learning algorithms and actually put them to use, even with professional terminology that may seem difficult!

Have you been getting stuck on linear algebra formulas every time you try to study AI? Instead of memorizing formulas, this lecture shows you visually how vectors and matrices actually move in space. By the end of the course, you will be able to interpret the mathematics behind neural networks, PCA, and SVD on your own.

You will build Android/iOS apps based on simple machine learning models and learn the fundamentals of machine learning, model conversion using TFLite, and the process of implementing Android and iOS applications.

This is a lecture covering LLM theory and practical examples based on <Large Language Models, Just the Essentials!> (Insight, 2025).

This is a replay of our seventh webinar on how to use the free, no-coding data visualization tool, 'HEARTCOUNT'. Instead of spending time learning data or learning tools, spend it finding the insights you need from your data.