Data Science Techniques
Understanding the role of data and measurement in media planning, audience engagement, and advertising effectiveness.
Blogs
June 24, 2025
Part 8: From Blocks to Brilliance – How Transformers Became Large Language Models (LLMs) of the series - From Sequences to Sentience: Building Blocks of the Transformer Revolution
Explore how Transformers evolved into Large Language Models (LLMs) like GPT, Claude, and Gemini by integrating key innovations such as self-attention, parallel processing, and massive scaling. Learn about the role of data, architecture choices, and reinforcement learning in enhancing LLM capabilities for generating human-like text. Discover how these advancements enabled LLMs to excel in diverse tasks and consider the future directions of multimodal models and alignment research. Join Part 8 of our series to understand the comprehensive journey from foundational RNNs to state-of-the-art generative AI.
June 24, 2025
Part 7: The Power of Now – Parallel Processing in Transformers of the series - From Sequences to Sentience: Building Blocks of the Transformer Revolution
Discover how parallel processing revolutionized Transformers, enabling them to handle entire sequences simultaneously for unprecedented efficiency and scalability. Learn how this innovation freed models from the sequential constraints of RNNs, allowing for faster training, better GPU utilization, and the creation of large-scale models like GPT and BERT. Explore the impact on various domains, from language to vision and beyond. Join Part 7 of our series to understand how parallelism transformed the landscape of AI, making modern large language models possible.
June 24, 2025
Part 6: The Eyes of the Model – Self-Attention of the series - From Sequences to Sentience: Building Blocks of the Transformer Revolution
Explore the pivotal role of self-attention in Transformer models, the mechanism that allows for capturing relationships across entire sequences simultaneously. Learn how self-attention enables models like BERT and GPT to process text efficiently, focusing on relevant tokens regardless of their position. Discover its impact on various applications, from translation to text generation. Join Part 6 of our series to understand how self-attention underpins the capabilities and scalability of modern AI, revolutionizing the processing of language and beyond.
June 24, 2025
Part 5: The Generator – Transformer Decoders of the series - From Sequences to Sentience: Building Blocks of the Transformer Revolution
Explore the intricacies of Transformer decoders, the architecture that powers text generation in models like GPT. Learn about their structure, including masked self-attention, encoder-decoder cross attention, and feed-forward networks, and understand their transformative impact on language generation, translation, and more. Dive into how decoders generate text step-by-step and their pivotal role in modern AI applications. Join us in Part 5 of our series as we transition from understanding language to creating it.
June 24, 2025
Part 3: Giving Words Meaning – Word Embeddings of the series - From Sequences to Sentience: Building Blocks of the Transformer Revolution
Discover the power of word embeddings in natural language processing, a revolutionary technique that transformed words into meaningful numerical vectors. Explore how methods like Word2Vec and GloVe captured context and meaning, enabling applications from semantic search to sentiment analysis. Understand the limitations of static embeddings and their evolution towards contextual embeddings with transformers. Dive into Part 3 of our series to see how these innovations laid the groundwork for NLP advancements.
June 24, 2025
Part 2: The Gatekeeper – Long Short-Term Memory (LSTM) Networks of the series - From Sequences to Sentience: Building Blocks of the Transformer Revolution
Part 2: The Gatekeeper – Long Short-Term Memory (LSTM) Networks
June 24, 2025
Part 1: The Roots – Recurrent Neural Networks (RNNs) of the series - From Sequences to Sentience: Building Blocks of the Transformer Revolution
Part 1: The Roots – Recurrent Neural Networks (RNNs)
June 13, 2025
Demystifying SHAP: Making Machine Learning Models Explainable and Trustworthy
Demystifying SHAP: Making Machine Learning Models Explainable and Trustworthy