인프런 - 라이프타임 커리어 플랫폼

This is a lecture where you can learn Deep Learning using TensorFlow. Learn linear regression, logistic regression, and softmax regression models, and finally create an MLP model that can classify fashion images!

By understanding the working principles of various recommendation algorithms, you can create your own personalized recommendation algorithm!

All the recent amazing achievements in the field of artificial intelligence have been announced in the field of reinforcement learning. We have covered reinforcement learning technology, which is revolutionizing true artificial intelligence technology such as robots, autonomous driving technology, and human-like machines, from the basics to the advanced level, in an easy-to-understand way for beginners.

Artificial intelligence, don't you want to try making one too? Haven't you ever wondered how many artificial intelligence products in our reality, such as self-driving cars, chatbots, and AI speakers, work and how they can be made? If you follow along with no-code and low-code, you can easily make artificial intelligence.

In this course, you will learn how to use the Tensorflow library to make data predictions. You will learn data analysis and processing methods that can be applied in practice by learning the library and analyzing sample data in parallel.

This lecture guides readers to deep learning with the most appropriate pace and straight direction, one step from concept to formula and one step from coding. In addition, there are over 100 graphs, illustrations, and diagrams, so you can easily and quickly accept abstract concepts. Another unique feature of this lecture is that you can start practicing right away by simply accessing a web browser without installing a program. After comfortably understanding the theory, you can directly code and conquer four representative deep learning problems with your eyes, so it is not lacking as a textbook for deep learning. The concepts or terms that you must go over are reviewed twice in the ‘Wait! Let’s move on to the next’ corner in the middle of the text and the ‘Memory Card’ corner at the end of the chapter to increase the learning effect. Let’s quickly overcome deep learning head-on with ‘Do it! Introduction to Deep Learning.’

This course will teach you the core theories of deep learning and how to implement deep learning code using the latest TensorFlow 2.0.

This lecture covers the contents of the Amazon bestseller <Hands-on Machine Learning 2nd Edition>. Using the representative machine learning library, scikit-learn, you will learn various machine learning algorithms and evaluation methods. Also, using the most famous deep learning libraries, TensorFlow and Keras, you will learn theories and practices from artificial neural networks to reinforcement learning. This lecture is not yet complete. One to two lectures will continue to be added every week.

Learn how to use deep learning with Python and Keras.

In the modern business world, data security and fraud prevention have become more important than ever. This lecture focuses on how to effectively detect and analyze fraudulent transactions using artificial intelligence (AI). Using Python and machine learning, we will build an AI model that can detect and detect anomalies in various industries, such as fraudulent transactions, credit card fraud, and production line abnormalities, early.

Understand the principles of deep learning and use Keras to simplify the complexity of building and training neural networks using models, layers, and optimization techniques.

To keep up with recent deep learning trends, we examine the context of deep learning's groundbreaking advancements.

From the basics of machine learning & deep learning! Creating and utilizing classification/regression/clustering/artificial neural networks with Python

This course teaches the “core concepts” of deep learning required to work in the deep learning field and the practical skills required to perform actual deep learning projects through hands-on training using PyTorch.

This course is designed to help those who want to deeply organize deep learning and grasp the big picture by focusing on theory and context rather than hands-on practice. You can easily understand the mathematical and statistical background that forms the foundation of deep learning, and gain an intuitive interpretation from a developer's perspective of the principles behind major modern deep learning models such as AutoEncoder, GAN, Transformer, and AlphaGo.

The automotive and parts manufacturing industry is a field where innovation is essential amid fierce global competition and complex supply chains. This course focuses on how AI technology can be applied in the field, explaining actual cases including production efficiency improvement, quality inspection automation, equipment predictive maintenance, and supply chain optimization. From the foundational understanding needed for AI implementation to strategies and risk management considerations for field application, this course covers topics step by step, providing knowledge and insights that practitioners and managers can immediately utilize.

I want to classify pictures of pizza and spaghetti! This is a deep learning practical project course that follows the A to Z of a practical project to classify images using CNN, one of the deep learning algorithms.

What is deep learning? A course to introduce deep learning and how to use it in practice by completely organizing the concept of deep learning!

Understand the basic concepts and latest trends of big data, and through technical understanding of big data analysis techniques, extract data patterns to present methods for business application. Through big data, you can create new businesses and develop insights into future business environments.

"LoRA-based lightweight fine-tuning: Your first step to creating a customized LLM!" This course is an introductory hands-on lecture designed so that even those encountering LLMs for the first time can easily follow along. We minimize complex theory and guide you step-by-step through the entire process: loading models → applying data → training → comparing results. In a short time, you'll directly experience the flow of cutting-edge lightweight fine-tuning techniques like LoRA and QLoRA, gaining an intuitive understanding of "how LLM fine-tuning works." Even without extensive resources, experience the satisfaction of creating an LLM specialized for your domain!