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Data Science News
Medium
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Code Like a Poet, Think Like a Politician
- The connection between coding and creativity and how programmers can be poets and politicians.
- The use of rhythm, structure, and emotion in coding to bring meaning, similar to how a poet uses these elements in their work.
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Nycdatascience
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Predicting the Unpredictable: Revolutionizing E-commerce Delivery with Machine Learning
- Accurately predicting delivery timelines in the e-commerce industry is a complex challenge due to various factors.
- A machine learning system utilizing the Olist e-commerce dataset was developed to forecast delivery times with high accuracy.
- Challenges include extreme variability, geographic complexity in Brazil, and non-linear relationships affecting delivery predictions.
- A multi-layered approach involving meticulous data preparation, feature engineering, and advanced modeling strategies was implemented.
- A region-specific clustering model identified three distinct delivery regions: Business Centers, Mid-Tier Regions, and Remote Areas.
- Specialized XGBoost models tailored to each cluster improved prediction accuracy significantly.
- The system also quantified prediction uncertainty, transforming point estimates into probability distributions.
- Results showed dramatic improvement in prediction accuracy, particularly in Business Centers compared to Remote Areas.
- An interactive chatbot interface was developed to make complex predictions accessible to business users.
- The system offers benefits like smarter promise dates, region-specific strategies, and operational optimization for businesses.
- Future research directions include temporal modeling, external data integration, online learning, and causal modeling.
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Medium
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Bonus Round! Spark: The Silent Engine That Powers Your Day (and the Future of Technology)
- Spark is the invisible glue that powers the information age by enabling personalized experiences and fast data processing.
- Apache Spark was born at the University of Berkeley in 2014 as a project to process data faster than Hadoop.
- Spark operates in memory, avoiding the need for constant reading and writing to storage, resulting in efficient performance.
- Spark quickly gained popularity in the tech industry due to its revolutionary approach to data processing.
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Towards Data Science
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Write for Towards Data Science
- Towards Data Science is seeking writers to contribute content on data science, machine learning, AI, and programming.
- The platform aims to reach a wide audience and provides guidelines for submitting articles effectively.
- Authors are required to submit original work, adhere to author terms, and understand the privacy policy.
- Articles should offer unique insights, avoid plagiarism, and not be AI-generated.
- Authors are encouraged to pitch articles via the online form and ensure originality in content.
- Content should be well-structured, focused, and catered to the data science community.
- Articles must have a clear central message, concise title, and valuable content for readers.
- Incorporating visuals, clear code display, and well-researched facts is essential for engaging readers.
- Authors must appropriately credit sources, use precise tags, and select impactful images.
- Data used in articles should be from reputable sources, with proper licenses for commercial use.
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Towards Data Science
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Debugging the Dreaded NaN
- To implement a NaN capturing solution in PyTorch, one can use PyTorch Lightning's callback interface.
- A NaNCapture Lightning callback is created to handle NaN events during training.
- The callback stores corrupted models and halts training upon encountering NaN values.
- Reproducibility is ensured by including NaNCapture state in the checkpoints for debugging.
- Loading the stored training batch for debugging relies on Lightning's LightningDataModule.
- Testing the callback involves creating a problematic model to trigger NaN occurrences.
- Runtime performance is minimally impacted by the NaNCapture callback, providing valuable debug capabilities.
- Enhancements like capturing and restoring random states for reproducibility are also discussed.
- Encountering NaN failures in machine learning can be challenging and indicate model issues.
- The proposed approach using Lightning callback streamlines NaN error debugging.
- This solution can save developers significant time and effort in debugging NaN errors.
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Towards Data Science
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How LLMs Work: Reinforcement Learning, RLHF, DeepSeek R1, OpenAI o1, AlphaGo
- Reinforcement Learning (RL) is a critical part of the training pipeline for Large Language Models (LLMs) as it allows the model to learn from its own experience.
- RL enables the model to explore different token sequences and receive feedback on which outputs are most useful, leading to better alignment with human intent over time.
- LLMs are stochastic, meaning their responses vary even with the same prompt due to sampling from a probability distribution, allowing for exploration of different paths.
- By training LLMs using reinforcement learning, they can discover and refine strategies beyond human knowledge, as seen in DeepMind's AlphaGo surpassing human-level play through self-play.
- RL involves the agent taking actions in an environment, receiving rewards as feedback, and gradually learning the optimal strategy to maximize total rewards over time.
- A key RL setup involves the policy determining the agent's strategy and the value function estimating the long-term expected reward for a given state.
- Deepseek-R1-Zero and Deepseek-R1 are open-source reasoning models that showcase the power of RL algorithms like Group Relative Policy Optimization (GRPO) over Proximal Policy Optimization (PPO).
- GRPO addresses challenges faced by PPO in reasoning tasks by using relative evaluation within a group to converge towards higher quality performance over time.
- DeepSeek-R1-Zero skipped supervised fine-tuning, allowing direct exploration of CoT reasoning, leading to improved complex reasoning capabilities.
- RL training can lead to emergent properties like chain-of-thought reasoning and unexpected outcomes, as seen in DeepSeek-R1-Zero refining its reasoning autonomously.
- Human feedback plays a crucial role in evaluating AI responses, especially in areas like summarization and creative writing, where there is no single 'correct' answer.
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VentureBeat
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OpenAI releases ‘largest, most knowledgable’ model GPT-4.5 with reduced hallucinations and high API price
- OpenAI has released GPT-4.5, a powerful large language model for chat applications, but it comes with a high price tag.
- GPT-4.5 is not a 'reasoning model' but is described as feeling like talking to a thoughtful person.
- Due to GPU supply constraints, access to GPT-4.5 is limited initially.
- The model is available to ChatGPT Pro subscribers and developers across all paid API tiers.
- GPT-4.5 offers improved natural and intuitive interactions, emotional intelligence, and follows user intent more accurately.
- It is designed for various applications including writing assistance, programming support, and problem-solving.
- GPT-4.5 aims to create warm, human-like conversations and understand subtle cues for better collaboration.
- It is trained on data from smaller AI models, enhancing its 'world model' and performance.
- OpenAI is hosting a livestream event to discuss GPT-4.5's development and capabilities.
- The model is priced higher than its predecessors but offers significant advancements in AI training and performance.
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Medium
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Short Introduction of PDF and CDF
- PDF stands for Probability Density Function. It describes the relative likelihood of a continuous random variable taking on a particular value.
- The PDF of a continuous random variable is denoted as f(x) and is used to calculate the probability of the variable lying between a specific range of values.
- The CDF, or Cumulative Distribution Function, gives the probability that a random variable takes a value less than or equal to a given value.
- The CDF accumulates probabilities from the leftmost possible value up to a given value and satisfies specific properties, such as starting at 0 and approaching 1.
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Medium
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Early steps toward cross-silo collaborations between different customer researchers
- Organizations can benefit from cross-silo collaborations between different customer researchers.
- Shared knowledge management can begin with smaller steps and progress towards specific impacts on product planning.
- Creating a messaging space, building a shared intranet destination, and starting a regular meeting series can facilitate collaboration.
- Researchers should document their standards and successes and encourage others to find valuable ways to collaborate.
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VentureBeat
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Qodo’s open code embedding model sets new enterprise standard, beating OpenAI, Salesforce
- Qodo has introduced Qodo-Embed-1-1.5B, a new open-source code embedding model designed to enhance code search, retrieval, and understanding.
- The model outperforms larger solutions from OpenAI and Salesforce, providing top-tier results on industry benchmarks.
- Qodo’s innovation improves AI-driven software engineering workflows for enterprise development teams working with vast codebases.
- Qodo-Embed-1-1.5B addresses the challenge of context awareness in large-scale software systems, emphasizing the importance of high-quality code.
- Unlike traditional focus on code generation, Qodo’s model excels at code retrieval by efficiently searching and retrieving relevant snippets.
- The model's balance of efficiency and accuracy with 1.5 billion parameters outperforms larger models on the Code Information Retrieval Benchmark.
- Qodo's unique training approach ensures that functionally distinct code is correctly identified, preventing errors in software development.
- The model is optimized for the top 10 programming languages and offers broader language support for future iterations.
- Qodo makes the model accessible through various channels, including Hugging Face, NVIDIA’s NIM platform, and AWS SageMaker JumpStart.
- Qodo’s focus on code understanding, retrieval, and quality assurance aligns with the evolving landscape of AI-powered coding tools.
- By offering a high-performance alternative, Qodo’s embedding model aids enterprise teams in intelligent code search and quality control within complex software ecosystems.
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Analyticsindiamag
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Karnataka Approves ₹400 Crore for Infrastructure, Semicon and Tech Developments
- Karnataka's Cabinet approves ₹400 crore for infrastructure, semicon, and tech developments.
- Initiatives include ₹75 crore seed fund for startups in tier-2 and 3 cities, ₹50 crore for semiconductor lab, and ₹285 crore for expanding IIIT-B.
- Seed fund aims to promote startups in sectors like agritech, deep tech, AI, and manufacturing.
- This investment is part of Karnataka's efforts to enhance technology infrastructure and drive regional growth.
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Analyticsindiamag
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IBM Expands Granite Model Family with New Multimodal and Reasoning AI
- IBM has released Granite 3.2, an update to its large language model (LLM) series, designed for business use with smaller, more efficient AI solutions.
- The update introduces a new vision language model (VLM) for document understanding, trained with IBM's open-source Docling toolkit.
- Granite 3.2 includes models with chain-of-thought reasoning, improving tasks like following instructions and solving math problems.
- IBM has also updated its TinyTimeMixers (TTM) models for long-term forecasting in financial analysis, supply chain forecasting, and retail inventory planning.
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Medium
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Building an AI-Powered C++ to Pseudo-Code Converter
- This blog introduces an AI-powered C++ to Pseudo-Code Generator.
- The project aims to simplify C++ code into a human-readable format using deep learning and NLP.
- The project utilizes a structured dataset, a Transformer-based Encoder-Decoder model, and multiple components.
- A Streamlit-based UI is provided for user accessibility.
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Medium
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446
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Data and Healthcare service improvement, Air and Lungs.
- The improvement of patient care relies heavily on data.
- With data, we can study and observe trends of diseases and allow for better preparedness in their management.
- In drug development, the data obtained from clinical trials are used to determine whether the drug is suitable.
- With the advent of artificial intelligence, disease progression and complications can now be predicted and prevented.
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Medium
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Wine and AI
- Artificial intelligence (AI) is revolutionizing the winemaking process, blending tradition with cutting-edge technology.
- AI is employed at various stages of winemaking, from precision viticulture to fermentation control.
- AI enhances efficiency, sustainability, and quality of wine production by providing insights based on vast amounts of data.
- These advancements help winemakers make informed decisions, reduce waste, and ensure consistent results.
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