This blog post serves as the conclusion to a series on training BERT from scratch. It discusses the significance of BERT in Natural Language Processing, reviews the previous parts of the series, and outlines the process of building and training a BERT model. The post emphasizes understanding the model’s inner workings and shares insights on…

The year 2023 saw significant developments in the Generative AI landscape, marked by the release of multiple LLMs and the emergence of LLMOps. While there were challenges in production, it was a year of experimentation and getting to know Generative AI. Looking ahead to 2024, the focus will likely be on successfully deploying Generative AI…

The text is a comprehensive explanation of computer simulations and their applications in understanding and predicting astronomical events. It covers various scenarios of transit phenomena, including exoplanet transits, asteroid belts’ influence, and hypothetical scenarios like simulating an exoplanet with an exomoon and detecting alien megastructures. It also highlights the advantages of simulations in scientific research.…

The paper explores training End-to-End Automatic Speech Recognition (ASR) models using Federated Learning (FL) and its impact on minimizing the performance gap with centralized models. It examines adaptive optimizers, loss characteristics, model initialization, and carrying over modeling setup from centralized training to FL.

The paper “Bootstrap Your Own Variance: Understanding Model Uncertainty with SSL and Bayesian Methods” was accepted at the Self-Supervised Learning workshop at NeurIPS 2023. It proposes BYOV, combining BYOL SSL algorithm with BBB Bayesian method to estimate model posteriors, showing that BYOV’s predictive standard deviation aligns well with a Gaussian distribution.

Multimodal datasets play a crucial role in recent AI advancements like Stable Diffusion and GPT-4. However, their design is not as researched as model architectures or training algorithms. To tackle this, DataComp introduces a testbed for dataset experiments using 12.8 billion image-text pairs from Common Crawl, allowing participants to create and evaluate new datasets.

A Multi-Strategy AI with Deep Reinforcement Learning has achieved victory over GPT3.5 in a Chess Match. For more details, please visit Towards Data Science.

The text describes the concept and process of building stacked ensembles in machine learning using H2O.ai and Optuna. The author outlines the steps involved in training a stacked ensemble, including the training of base models such as Deep Neural Networks, XGBoost, and LightGBM, and subsequently training the meta-model using H2OStackedEnsembleEstimator. The summary provides an in-depth…

The text discusses the comparison between intuition and code implementation for ABC with Particle Swarm Optimization to identify its superior performance. For more information, please visit Towards Data Science.