Holisticon Poland’s Post

Time series analysis is constantly evolving with new methodologies and tools. Here are some next-level approaches to explore: ✅ Temporal Fusion Transformers (TFT): Combining the power of transformers with recurrent neural networks, TFTs provide interpretable, multi-horizon forecasting for complex datasets. ✅ Graph Neural Networks (GNN) for Temporal Data: GNNs can capture spatial and temporal relationships in time series data, making them ideal for networked data like IoT sensor readings and social media analytics. ✅ Autoregressive Integrated Moving Average (ARIMA) with Exogenous Variables (ARIMAX): Integrating external variables with ARIMA models adds context to time series forecasting, enhancing predictions in fields like finance and energy. ✅ Prophet for Seasonality and Trend Analysis: Developed by Facebook, Prophet excels in capturing seasonality, holidays, and other temporal patterns, especially in noisy data. ✅ Reinforcement Learning for Time Series Decisions: Reinforcement learning can optimize decisions in time-dependent processes, such as inventory management and algorithmic trading. #AdvancedAnalytics #DataInnovation #TimeSeriesForecasting #MachineLearning #AI

Thoughts on this? >> Polymathic AI Releases ‘The Well’: 15TB of Machine Learning Datasets Containing Numerical Simulations of a Wide Variety of Spatiotemporal Physical Systems >> Comment below! >>> lqventures.com #IoT #industry40 #healthtech #AI #mhealth

📡 Our (Youngmin Lee, Xiaomin Ma, and myself) latest preprint is now available on arXiv: Modeling of Time-varying Wireless Communication Channel with Fading and Shadowing. We propose a novel approach using deep learning neural networks and mixture density networks to model channel characteristics more accurately and adaptively. This innovation leads to more reliable and efficient wireless networks, crucial for modern applications like IoT, 5/6G, and smart cars/cities. https://lnkd.in/dfUQhzzw #WirelessCommunication #DeepLearning

“Big AIs in Small Devices.” By Luc Andrea is an Engineering Director at Multiverse Computing specializing in Artificial Intelligence and Quantum Computing. …”As IoT devices that aim to collect more and more information to feed big AI models, those models in turn must work within the limited resources of edge devices. Large language models, which are leading the advances of natural language processing, exemplify this challenge. In this landscape, tensor networks emerge as potential candidates to remove this limit. By leveraging tensor networks, it’s possible to compress the massive architectures of LLMs without diluting their proficiency and make them manageable in embedded systems…” #MultiverseComputing #QuantumAI #quantum #AI #quantumcomputing

Thoughts on this? >> Polymathic AI Releases ‘The Well’: 15TB of Machine Learning Datasets Containing Numerical Simulations of a Wide Variety of Spatiotemporal Physical Systems >> Comment below! >>> lqventures.com #healthtech #IoT #AI #industry40 #mhealth

What’s Brewing in AI Renaissance?   AI & ML Pioneers: We’re not just a club; we’re a renaissance in the making! Explore artificial intelligence, machine learning, generative AI, and deep learning. IoT Integration: Bridging the digital and physical realms, we’re shaping the future of smart homes, healthcare, and more. Our Canvas:   Workshops & Training: Sharpen your skills in AI, ML, generative AI, deep learning, and IoT. Hackathons & Projects: Turn theory into practical solutions, fostering creativity and innovation. Guest Lectures: Learn from industry experts and gain insights into the latest advancements. Research Initiatives: Publish your findings and contribute to the field. Community Outreach: Impact society positively through awareness and collaboration. Join AI Renaissance! Let’s redefine technology together.      #EducationBeyondConvention #EngineeringHealthyMinds #bestmanagementcollegeinnoida #topmanagementcollegeinnoida #toparchitecturecollegeinnoida #bestarchitecturecollegeinnoida #topengineeringcollegeinnoida #bestengineeringcollegeinnoida #tophmcollegeinnoida #besthmcollegeinnoida

While everyone is obsessed with large language models… Small language models are every bit as good for many use cases. And when put into a CHAI ensemble, they play their part while gobbling up way less compute You can fine tune them to be experts at a specific task, and to interact with other machine learning modules, large language models, IOT data, or anything else you want to throw into the ensemble. Here’s an article we wrote on the topic. https://lnkd.in/g6Cgp6R9 #smalllanguagemodels #CHAI #artificialintelligence #CognitiveHiveAI

Finished another top quality course from DeepLearning.AI! I really enjoyed learning through this first course in the AI for Good Specialization. I particularly appreciate the various featured AI for Good projects on public health. Specifically close to my heart is the featured air quality project in Bogota since what's involved are IoT and sensors (something that I have knowledge and experience in). Aside from the lessons and case studies presented, I also liked exploring the provided Jupyter notebooks. I was in awe with the notebooks and (even though the focus of the course is not on the technicalities) I really learned a lot by exploring the notebooks. Personally, it helped me in thinking how to better demonstrate my code through a Jupyter notebook. All in all, I really like this course and specialization. So far, I really got inspired by the featured projects and it has helped me think through how to make AI have a better impact to the society, beyond the metrics that are being optimized in every AI project. I am excited to go through the next course on AI and climate change. #ai4good #artificialintelligence

As someone passionate about making machine learning more accessible, I'm excited to share my article summarizing the groundbreaking "Deep Compression" research. This seminal paper, published by Song Han a few years ago, introduced a novel technique to dramatically reduce the memory and energy requirements of large neural networks. By compressing these models, they can fit entirely on fast, power-efficient on-chip SRAM instead of relying on power-hungry DRAM. The implications of this are huge - it opens the door for deploying high-performance AI on a wide range of resource-constrained devices, from smartphones to IoT sensors. No longer will AI be confined to only the most GPU-rich environments. Check out the article here https://lnkd.in/gKA2MDsz Let me know what you think! #MachineLearning #DeepCompression #AI #NeuralNetworks #DataScience #Research #MLSystems #TechInnovation #ArtificialIntelligence #Efficiency

Thoughts on this? >> From Fixed to Random Designs: Unveiling the Hidden Factor Behind Modern Machine Learning ML Phenomena >> Comment below! >>> lqventures.com #industry40 #IoT #AI #healthtech #mhealth