Understanding and Implementing Artificial Neural Network Theory and DNN, CNN Architectures

Explanation of web data collection techniques using Python. Explanation of web data scraping using the latest technologies.

This article explains how to develop an AI web service using the API from OpenAI.

Explaining how to connect to a Python-based AI web server using FastAPI from a Spring Boot project.

Learn Oracle Database SQL syntax.

We will cover the major Python libraries that you must learn to become a data analysis expert.

A star trainer in IT development, artificial intelligence, and IT strategic planning provides an easy, detailed, and accurate explanation of the fundamentals of the Java programming language.

A star instructor in IT development, artificial intelligence, and IT strategic planning provides an easy, detailed, and accurate explanation of the Python programming language.

This course is a step-by-step learning process that covers the principles of image and object recognition using deep learning, from fundamentals to the latest models. - Building Foundations: Understanding tensors and basic neural network structures with PyTorch - Understanding Images: Learning computer vision concepts, image data structures, and augmentation techniques - CNN Model Training: Hands-on image classification practice with Convolutional Neural Networks (CNN) using datasets like CIFAR-10 - Transfer Learning: Leveraging pre-trained models to achieve fast learning with limited data - Object Detection: Understanding and practicing with the latest object detection models including R-CNN, YOLO, SSD, DETR - Segmentation: Experiencing pixel-level object segmentation through U-Net and Mask R-CNN

Learn MLP, CNN, and RNN, the basic framework of deep learning, quickly and easily.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

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

This is a comprehensive machine learning project course that learns various useful machine learning regression and classification projects along with theory using Python and TensorFlow 2 based on Boston Marathon big data.

You will create an Android/iOS app based on a simple machine learning model, learn the basics of machine learning, model conversion using TFLite, and the process of implementing an Android app/iOS app.