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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…
Neo4j, MongoDB, Apache, and Other GraphRAG Systems
GraphRAG systems, Neo4j vs MongoDB, distributed graph processing, graph analytics, cloud-native graph database.
What Are GraphRAGs?
What Are GraphRAGs? 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 GraphRAG (Graph Retrieval Augmented Generation) systems are AI-driven tools that integrate graph databases with retrieval augmented generation (RAG) models to enhance data retrieval, knowledge representation,…
Top 3 Applications of GraphRAG Systems Across Different Fields
Top 3 Applications of GraphRAG Systems Across Healthcare, E-Commerce, and Legal Fields From personalized medicine to dynamic shopping recommendations and automated legal research, GraphRAG systems are transforming how industries leverage data-driven decision-making. What are GraphRAGs Comprehensive Comparison of Neo4j, MongoDB, Apache, and Other Tools for GraphRAG Systems Top 3 Applications of GraphRAG Systems Across Healthcare,…
Global Geopolitics, Fall 2024
As we watch the world grapple with intensifying geopolitical conflicts, here at Prism14, we’ve found ourselves reflecting on a question that feels more urgent with each passing week: Is Pax Americana—the era of U.S. leadership in global affairs—in jeopardy? In this, our latest article on the Prism14 website, we dive into what feels more and…
Causal machine learning for predicting treatment outcomes
Causal machine learning (ML) provides flexible, data-driven methods to predict how well treatments will work and if they might have harmful side effects. This helps researchers and doctors evaluate drugs for safety and effectiveness. One of the main advantages of causal ML is that it can estimate how each treatment will affect individual patients. This…
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