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Deep Learning & Machine Learning

[Enterprise Challenge] Machine Learning + Deep Learning for Self-Study

You can learn the foundational knowledge for those learning machine learning and deep learning for the first time. You will cover basic machine learning models such as K-Nearest Neighbors, Linear Regression, Logistic Regression, Decision Trees, and various ensemble algorithms, as well as core concepts for training machine learning models like Stochastic Gradient Descent, regularization, and overfitting/underfitting. It also covers unsupervised learning such as K-Means and PCA. In the deep learning section, you will learn about basic artificial neural networks, deep neural networks, convolutional neural networks, and recurrent neural networks through examples using Keras and PyTorch. The final chapter covers large-scale models based on Transformers. It introduces the structure and core principles of LLMs in detail and helps you acquire practical skills through examples such as summarizing product descriptions or generating answers to questions.

Machine Learning(ML)
Deep Learning(DL)
B2B Only Challenge
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haesunpark

"LLM & AI with PyTorch" Book Publication and Event Announcement

Hello. I'm Haesun Park.

It's been a while, but I'm happy to announce the publication of a new book.

<AI and Machine Learning for Coders> author Laurence Moroney's new book <Llama 2 and Beyond: A Guide to LLMs with PyTorch> has been published!

The previous book focused on deep learning applications using the TensorFlow ecosystem. This book uses PyTorch instead of TensorFlow and focuses on generative AI, such as LLMs and diffusion models. It is now available at online and offline bookstores! The e-book has also been released! 🙂

I have created a short video summarizing what's new compared to the previous edition. Share this video on your social media, and five winners will be selected to receive a copy of from Hanbit Media! You must register on the Google Form after sharing it on social media. Please refer to the instructions below!

  • Video URL to share: https://youtu.be/vEwY31kDtl4

  • Please register the SNS URL where your shared content can be viewed in the Google Form (https://forms.gle/9CMP37YMugpkA8G96).

  • If you cannot register the URL address, please upload a screenshot instead.

  • Event Period: 3/5 (Thu) ~ 3/13 (Fri)

  • Winner Announcement: 3/16 (Mon) (Winners will be contacted individually)

The books will be sent directly by Hanbit Media.

We look forward to your participation.

Thank you!

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