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Advances in Causal Inference SURD
What it is, why it’s important, and an overview of the existing state of the art A recent paper on a new, even more refined approach to Causal Inference SURD came to our attention, and we wanted to share a primer on Causal Inference. This is also up on the Prism14 website with links to…
Where Does Money Really Come From? Prism14 on Rethinking Money, Debt, and Growth
Where Does Money Really Come From? Most discussions about money, taxes, and national debt start off with a fundamental misconception. Money isn’t just something governments collect to spend, nor do taxes merely cover public services. And the national debt? It’s not a burden in the way most imagine. In fact, these views miss the real…
Why You Should Care about Circuit Courts Composition
Circuit courts, also known as U.S. Courts of Appeals, are the intermediate appellate courts in the federal judicial system. They review decisions from lower district courts, and their rulings often set important legal precedents. There are 13 circuit courts, each covering different regions of the U.S. The composition of circuit courts matters because the political…
Deep Learning Resources – A Super Syllabus (2024 October)
Phase 1: Foundational Deep Learning Knowledge 1. Introduction to Machine Learning – Topics: 2. Mathematics for Machine Learning – Resources: – Topics: 3. Programming Skills – Resources: – Topics: Phase 2: Intermediate Concepts and Hands-on Practice 1. Supervised Learning 2. Unsupervised Learning – Resources: – Topics: – Projects: 3. Natural Language Processing (NLP) – Resources:…
Supercharge Your Collaborations: Tips for Internships, Projects, and Mentors
Unlock your professional potential with expert strategies for building powerful connections and seizing career-expanding opportunities Today’s competitive professional landscape is wild! Proactive career development is more crucial than ever. In this article, we explore four key areas to significantly boost your career trajectory: collaborations, internships, projects, and mentorships. By navigating these elements, you’ll be well-equipped…
Sequential / Recurrent Neural Network (RNNs) Models in Deep Learning
We’ve all heard about transformers in deep learning architectures, these days. What about other machine learning approaches dealing with sequential data but don’t have the same inherent performance limitations as decoder-only transformers? Several machine learning approaches are designed to handle sequential data without the limitations of decoder-only transformers (e.g., unidirectional processing and the inability to…
The Saga of Recurrent Sequential Models, RNNs vs Transformers: The Final Showdown?
In the context of machine learning, a showdown has emerged between two architectural giants—Recurrent Sequential Models and Transformers. These approaches represent two fundamentally different philosophies for processing sequential data, with each excelling in different aspects of learning from sequences. On one side, Recurrent Sequential Models (RNNs, LSTMs, GRUs) have long been the go-to for tasks…
How to Get Started with Decoder-Only Transformers
Get started with decoder-only transformers, like OpenAI’s GPT models! Decoder-only transformers have gained massive popularity due to their success in tasks like text generation, summarization, dialogue systems, and code generation. These models utilize only the decoder portion of the original transformer architecture, focusing on generating sequences autoregressively—meaning they predict the next token in a sequence…
What’s a Transformer in 3 Steps?
A transformer in code and process – in 3 steps Here’s a simplified 3-step explanation: Step 1: Input Process (Tokenization and Embedding) Step 2: Attention Mechanism (Self-Attention) Step 3: Output Generation (Decoding) This 3-step process represents the core mechanics of a transformer, combining tokenization, attention, and decoding to achieve powerful results in natural language processing,…
Neo4J vs MongoDB for GraphRAG – what you need to know
Neo4j vs. MongoDB for GraphRAG: Navigating the Right Choice for Complex Graph Queries What are GraphRAGs Comprehensive Comparison of Neo4j, MongoDB, Apache, and Other Tools for GraphRAG Systems Top 3 Applications of GraphRAG Systems Across Healthcare, E-Commerce, and Legal Fields From native graph queries in Neo4j to document-centric scalability in MongoDB, we break down how…
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