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The Rise of Generative AI: Pioneering the next era of creativity

The Rise of Generative AI: Pioneering the next era of creativity
organiser.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from organiser.org Daily Mail and Mail on Sunday newspapers.

Autoencoders-vaes , Financial-services , Recurrent-neural-networks-rnns , Deloitte , Hotel-room-layouts , Renewable-energy-modeling , G-networks , Generative-adversarial-networks-gans , Network-optimisation , Energy-efficiency , Generative-models , Variational-autoencoders

100x less compute with GPT-level LLM performance: How a little known open source project could help solve the GPU power conundrum — RWKV looks promising but challenges remain

New model requires significantly fewer resources for running and training LLMs

Linux-foundation , Google , Recurrent-neural-networks-rnns , Recurrent-neural-networks , Artificial-intelligence , Receptance-weighted-key-value ,

How is Machine Learning Leveraged in Photonic Design?

Photonic design involves developing optical components and systems that use light for computing, communications, and sensing. With the growing complexity of these systems, the adoption of machine learning has proven effective in modeling intricate structures and extracting valuable insights from extensive datasets.

Owais-alijan , Quardia-shutterstock , Owais-ali , Convolutional-neural-networks-cnns , Recurrent-neural-networks-rnns , Generative-adversarial-networks-gans , Learning-algorithms-used , Neural-networks , Adversarial-networks , Machine-learning-used , Advanced-materials , Science-advances

Understanding and Coding Self-Attention, Multi-Head Attention, Cross-Attention, and Causal-Attention in LLMs

This article codes the self-attention mechanisms used in transformer architectures and large language models (LLMs) such as GPT-4 and Llama from scratch in PyTorch.

Greece , Greek , Pytorch-multiheadattention , A-survey-on-efficient-training-of-transformers , Recurrent-neural-networks-rnns , Self-attention-mechanism , Large-language-models-from-scratch , Large-language-model , Attention-is-all-you-need , Natural-language-processing , Recurrent-neural-networks , All-you

Beyond Buzzwords: Glasswing AI palette guides startups navigating AI's diverse terrain

LLMs have dominated the news, but it won't be a defensible technology. Glasswing's AI Palette is a new framework for startup founders about AI and ML technologies.

Rudina-seseri , Convolutional-neural-networks-cnns , Recurrent-neural-networks-rnns , Glasswing-ventures , Convolutional-neural-networks , Recurrent-neural-networks ,

GitHub - mlabonne/llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. - GitHub - mlabonne/llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

Romano-roth , Andrej-karpathy , Rachael-tatman , Thomas-thelen , Jay-alammar , Lilian-weng , Google-colab , Fastest-library-to , Neural-networks , Recurrent-neural-networks-rnns , Youtube , A-survey-on-evaluation

What Would the Chatbot Say?

What Would the Chatbot Say?
acm.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from acm.org Daily Mail and Mail on Sunday newspapers.

Germany , Heidelberg , Baden-wüberg , James-hendler , Bill-gates , Sebastien-bubeck , Colin-raffel , Sandrine-ceurstemont , Roman-trukhin , University-of-north-carolina-at-chapel-hill , Machine-learning-foundations-group-at-microsoft-research , Interest-group-on-artificial-intelligence

Uncovering Hidden Patterns: Time Series Analysis Techniques

Time series data, which comprises observations collected over sequential time intervals, is ubiquitous in various fields like finance, economics, climate

Recurrent-neural-networks-rnns , Time-series , Defining-time-series , Time-series-data , Moving-averages , Exponential-smoothing , Decomposition-components , Integrated-moving-average , Parameter-selection , Frequency-domain-analysis , Spectral-analysis

Wind | Free Full-Text | Wind Power Forecasting in a Semi-Arid Region Based on Machine Learning Error Correction

Wind power forecasting is pivotal in promoting a stable and sustainable grid operation by estimating future power outputs from past meteorological and turbine data. The inherent unpredictability in wind patterns poses substantial challenges in synchronizing supply with demand, with inaccuracies potentially destabilizing the grid and potentially causing energy shortages or excesses. This study develops a data-driven approach to forecast wind power from 30 min to 12 h ahead using historical wind power data collected by the Supervisory Control and Data Acquisition (SCADA) system from one wind turbine, the Enercon/E92 2350 kW model, installed at Casa Nova, Bahia, Brazil. Those data were measured from January 2020 to April 2021. Time orientation was embedded using sine/cosine or cyclic encoding, deriving 16 normalized features that encapsulate crucial daily and seasonal trends. The research explores two distinct strategies: error prediction and error correction, both employing a sequential model where initial forecasts via k-Nearest Neighbors (KNN) are rectified by the Extra Trees Regressor. Their primary divergence is the second model’s target variable. Evaluations revealed both strategies outperforming the standalone KNN, with error correction excelling in short-term predictions and error prediction showing potential for extended forecasts. This exploration underscores the imperative importance of methodology selection in wind power forecasting.

Denmark , Brazil , Bahia , Caatinga , Alagoas , Turkey , Sobradinho , Cearár , Brazilian , Casa-nova , Sistema-el , Recurrent-neural-networks-rnns

Pioneering a Counterfeit-Free Future: Global ETS LLC's

TAMPA, Fla., Oct. 23, 2023 (GLOBE NEWSWIRE) -- In the sprawling field of electronics, the menace of counterfeit chips continually undermines...

Cong-xu , Ciliang-yang , Jianli-lu , Wensheng-chen , Jiahui-zou , Steven-chung , Ming-kuan-lin , Surface-mount-technology-association , Bay-area-manufacturers-association , Recurrent-neural-networks-rnns , Convolutional-neural-networks-cnns , Counterfeit-symposium