/explore

Click through on any links that interest you or select the planets on the right to continue exploring the Outer Web.
You are here

 explainextended.com
Happy New Year: GPT in 500 lines of SQL - EXPLAIN EXTENDED at EXPLAIN EXTENDED
| | justinhj.github.io
Optimizing training a GPT style Tokenizer with C++
2.0 parsecs away

Travel
| | [AI summary] The user has provided a detailed explanation of implementing the BPE (Byte Pair Encoding) algorithm for tokenization, focusing on the challenges and considerations involved in the process. They describe the use of different conflict resolution strategies, such as first occurrence and lexicographical ordering, and discuss the optimization techniques applied to improve performance, including incremental frequency counting and efficient data structures. The user also outlines future directions for the project, such as porting to Zig, exploring other tokenization algorithms, and optimizing encoding/decoding steps. The response highlights the complexity of working with C++ and the benefits of using modern C++ practices while emphasizing the importanc...
| | haifengl.wordpress.com
LLM | Haifeng's Random Walk
2.1 parsecs away

Travel
| | Generative artificial intelligence (GenAI), especially ChatGPT, captures everyone's attention. The transformerbased large language models (LLMs), trained on a vast quantity of unlabeled data at scale, demonstrate the ability to generalize to many different tasks. To understand why LLMs are so powerful, we will deep dive into how they work in this post. LLM Evolutionary Tree...
| | zserge.com
AI or ain't: LLMs
1.7 parsecs away

Travel
| | Finally, building a simple GPT model that would finish our sentences.
| | blog.keras.io
Building Autoencoders in Keras
14.6 parsecs away

Travel
| [AI summary] The text discusses various types of autoencoders and their applications. It starts with basic autoencoders, then moves to sparse autoencoders, deep autoencoders, and sequence-to-sequence autoencoders. The text also covers variational autoencoders (VAEs), explaining their structure and training process. It includes code examples for each type of autoencoder and mentions the use of tools like TensorBoard for visualization. The VAE section highlights how to generate new data samples and visualize the latent space. The text concludes with references and a note about the potential for further topics.