Principal component analysis (PCA) is a complex technique used for dimensionality reduction, with two main techniques: classical and non-classical. The article discusses the challenges of implementing PCA using the classical technique and demonstrates a C# implementation on a subset of the Iris Dataset.
The article demonstrates how to implement an ArgSort() function using the C# language, providing code examples for both arrays and lists. It highlights the availability of a C# Array.Sort(a,b) overload that allows sorting based on values in an array.
The article discusses the author's student project on forecasting crop yield and crop price using various statistical methods, emphasizing the importance of choosing a topic of interest. The project received a high score and the author provides tips for starting a successful project, including conducting a literature review.
The article discusses the author's implementation of matrix inverse using QR decomposition and highlights the different algorithms and variations involved in computing the inverse of a matrix. The demo showcases the computation of a 4x4 matrix's inverse and verifies the result by multiplying it with the original matrix to obtain the identity matrix.
The article discusses the importance of project prioritization in the analytics world and suggests using a mental model to make better decisions. It emphasizes the risks associated with projects and the need to consider impact and time constraints when prioritizing.
Gaussian splatting is a fast and interpretable method for representing 3D scenes without neural networks, gaining popularity in a world obsessed with AI models. It uses 3D points with unique parameters to closely match renders to known dataset images, offering a refreshing alternative to complex and opaque methods like NeRF.
This article explores the mechanics of prompt engineering in GPT-2, a large language model. It delves into how the model learns about the world through human text projection and generates text based on probability distributions.
In this article, the focus is on building an LLM-powered analyst and teaching it to interact with SQL databases. The author also introduces ClickHouse as an open-source database option for big data and analytical tasks.
LoRA is a parameter efficient method for fine-tuning large models, reducing computational resources and time. By decomposing the update matrix, LoRA offers benefits such as reduced memory footprint, faster training, feasibility for smaller hardware, and scalability to larger models.
Large language model (LLM) training has surged in popularity with the release of popular models like Llama 2, Falcon, and Mistral, but training at this scale can be challenging. Amazon SageMaker's model parallel (SMP) library simplifies the process with new features, including a simplified user experience, expanded tensor parallel functionality, and performance optimizations that reduce trainin...
Mistral AI's Mixtral-8x7B large language model is now available on Amazon SageMaker JumpStart for easy deployment. With its multilingual support and superior performance, Mixtral-8x7B is an appealing choice for NLP applications, offering faster inference speeds and lower computational costs.
Amazon SageMaker JumpStart offers pretrained foundation models like Llama-2 and Mistal 7B for generative tasks, but fine-tuning is often necessary. TruLens, integrated with Amazon Bedrock, provides an extensible evaluation framework to improve and iterate on large language model (LLM) apps.
Pandera, a powerful Python library, promotes data quality and reliability through advanced validation techniques, including schema enforcement, customizable validation rules, and seamless integration with Pandas. It ensures data integrity and consistency, making it an indispensable tool for data scientists.
Great customer experience is crucial for brand differentiation and revenue growth, with 80% of companies planning to invest more in CX. SageMaker Canvas and generative AI can revolutionize call scripts in contact centers, improving efficiency, reducing errors, and enhancing customer support.
This article provides an introduction to developing non-English RAG systems, including tips on data loading, text segmentation, and embedding models. RAG is transforming how organizations utilize data for intelligent ChatBots, but there is a gap for smaller languages.