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Hyperparameter Tuning: Neural Networks 101
This text discusses how to improve the learning and training process of neural networks by tuning hyperparameters. It covers computational improvements, such as parallel processing, and examines hyperparameters like the number of hidden layers, number of neurons, learning rate, batch size, and activation functions. The text also provides a Python example using PyTorch and references…
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HuggingFace Introduces TextEnvironments: An Orchestrator between a Machine Learning Model and A Set of Tools (Python Functions) that the Model can Call to Solve Specific Tasks
TRL (Training with Reward Learning) is a full-stack library that enables researchers to train transformer language models and stable diffusion models using reinforcement learning. It includes tools such as Supervised Fine-tuning (SFT), Reward Modeling (RM), and Proximal Policy Optimization (PPO). TRL is an extension of Hugging Face’s transformers collection and supports various language models. It…
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Developing a Company-Specific ChatGPT is One-Third Technology and Two-Thirds Process Improvements
This article discusses the development of a GPT-based virtual assistant for Enefit, an energy company in the Baltics. It highlights the importance of data/information governance in ensuring accurate responses from the virtual assistant. It also emphasizes the need for guidance and training to customize the behavior and style of the assistant. The article concludes that…
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Meet JARVIS-1: Open-World Multi-Task Agents with Memory-Augmented Multimodal Language Models
Researchers from Peking University, UCLA, Beijing University of Posts and Telecommunications, and Beijing Institute for General Artificial Intelligence have developed JARVIS-1, a multimodal agent for open-world tasks in Minecraft. JARVIS-1 combines pre-trained multimodal language models to interpret visual observations and human instructions, generating plans for control. It achieves nearly perfect performance in over 200 tasks…
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Researchers from the University of Washington and Duke University Introduce Punica: An Artificial Intelligence System to Serve Multiple LoRA Models in a Shared GPU Cluster
Researchers from the University of Washington and Duke University have developed Punica, a multi-tenant serving framework for LoRA models on a shared GPU cluster. By utilizing a new CUDA kernel called SGMV, Punica enables efficient batching of requests from multiple LoRA models, resulting in improved GPU usage and throughput. The paper details the contributions and…
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Parallelising Python on Spark: Options for concurrency with Pandas
This blog post discusses the options and benefits of parallelizing Python code on Spark when working with Pandas. It compares Pandas UDFs and the ‘concurrent.futures’ module as two approaches to concurrent processing in order to determine their use cases. The post also covers the challenges of working with large datasets and the performance results of…
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2024 Data Job Market: Oversaturated or Good Outlook?
The data job market has been challenging, with a significant decrease in job postings from Big Tech companies (FAANG) but slight improvement in hiring by other companies. The overall job market seems to be recovering after a dip in May. There is a higher demand for data engineers compared to data scientists or data analysts.…
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Researchers from SJTU China Introduce TransLO: A Window-Based Masked Point Transformer Framework for Large-Scale LiDAR Odometry
Researchers from Shanghai Jiao Tong University and China University of Mining and Technology have developed TransLO, a LiDAR odometry network that combines CNNs and transformers to enhance global feature embeddings and outlier rejection. TransLO outperforms existing methods on the KITTI odometry dataset with superior accuracy and efficiency. Components like WMSA and MCFA were evaluated through…
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Sentiment Analysis in Live Chat
Sentiment analysis is a natural language processing technique that analyzes emotions and opinions in text. Implementing sentiment analysis in live chat can enhance customer service by identifying frustrated or satisfied customers. It allows businesses to address concerns promptly and turn negative experiences into positive ones. Sentiment analysis also helps identify trends in customer feedback and…
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Meet SPHINX: A Versatile Multi-Modal Large Language Model (MLLM) with a Mixer of Training Tasks, Data Domains, and Visual Embeddings
SPHINX is a multi-modal large language model that addresses the limitations of existing models in understanding visual instructions and performing diverse tasks. It integrates model weights, tuning tasks, and visual embeddings to excel in tasks like human pose estimation and object detection. SPHINX’s fine-grained visual understanding and collaboration with other models make it a frontrunner…