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Data Science News
Medium
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From Confused Closet to Confidence:c
- Rent the Runway (RTR) has implemented GRASP, a Graph-Based Hybrid Recommender Analyzing Sentiment Patterns, to improve its recommendation system.
- GRASP combines deep learning, sentiment analysis, and graph technology to provide personalized fashion guidance to users.
- By using a fine-tuned BERT model, GRASP detects nuanced sentiment in user reviews and weighs it by keyword relevance to improve recommendations.
- The insights derived from GRASP have helped product designers refine the next season's line and marketers write smarter product descriptions, resulting in better recommendations for users.
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Medium
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229
Understanding Sliding Window Approach — An Easy Explanation
- The sliding window approach is perfect for problems involving subarrays or subsequences where we need to find a continuous segment that satisfies certain conditions.
- The core idea is to expand our window by moving the right pointer forward until our window no longer satisfies our condition. Then we shrink it from the left until the condition is satisfied again.
- The sliding window technique excels in problems where you need to find optimal subarrays or need to maintain a running calculation over a window of elements.
- The sliding window technique is versatile and can be applied to many problems involving contiguous subarrays or substrings.
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Medium
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Six steps for building Machine Learning Projects
- Building machine learning projects involves a cyclical process of data collection, model iteration, deployment, and reevaluation of results.
- To determine if machine learning is appropriate for a business problem, it needs to be aligned as a machine learning problem first.
- The major types of machine learning are supervised, unsupervised, transfer learning, and reinforcement learning, with supervised and unsupervised learning being most common in business applications.
- Supervised learning involves training a model with labeled data to predict outcomes, while unsupervised learning deals with data lacking labels to uncover patterns.
- Transfer learning adapts an existing model's learned information to a new problem domain, saving time and resources in training.
- For business applications, machine learning usually falls under classification, regression, or recommendation categories based on the problem at hand.
- Important considerations in machine learning projects include data types (structured, unstructured), feature variables, and the choice of evaluation metrics based on project goals (classification, regression, recommendation).
- Feature types in machine learning include categorical, continuous, derived, and can also encompass text, images, or any data that can be transformed into numbers.
- The modeling phase includes selecting a model based on interpretability, scalability, and efficiency, tuning the model to improve performance, and comparing models for optimal results.
- Model evaluation involves testing on different data subsets to ensure proper learning and generalization, with documentation and iteration being key components of the process.
- Starting with a proof of concept using the outlined steps can help businesses determine the feasibility of applying machine learning to add value to their operations.
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Dev
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Sorting Algorithms Made Visual – Feedback Needed!
- Sorting Algorithm Visualizer is a React-based interactive tool designed to make learning sorting algorithms more engaging.
- The visualizer supports multiple sorting algorithms, with dynamic animations and adjustable speed and array size.
- The project aims to simplify the understanding of sorting algorithms, especially for beginners.
- The creator seeks feedback on performance, usability, and suggestions for improvements, as well as contributions in resolving an issue with Merge Sort visualization.
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Medium
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242
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How We’re Using AI to Track the Soul of Our Business
- At Mom Bomb, the Signal Logbook, powered by AI, is used to reflect on how the business is feeling each week.
- The system scans for six qualities to determine if the message is clear, if the business operates from conviction, and if it aligns with its values.
- The results showed positive signs of a clear signal, as proven by the real-life impact on the business.
- By prioritizing a clear signal, they are building an energetic infrastructure for a business that is felt by the world.
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Dev
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2818. Apply Operations to Maximize Score
- Given an array of positive integers and an integer k, the task is to maximize the score by selecting subarrays and multiplying the score by the element with the highest prime score.
- The prime score of an integer is the count of distinct prime factors it has.
- Using monotonic stacks, determine the left and right boundaries for each element to find the valid subarrays where it is the maximum element.
- Calculate the number of subarrays for each element where it is the maximum element based on its boundaries.
- Sort the elements by their value, prime score, and index to prioritize elements for maximizing the score.
- Implement a greedy approach by multiplying the score with the highest possible values first using efficient modulo calculations.
- The solution involves calculations for prime scores, fast exponentiation, and handling large powers modulo 109 + 7.
- The provided PHP implementation demonstrates how to find the maximum score for given examples.
- The complexity of the solution lies in efficiently handling prime scores, determining boundaries, and selecting elements for multiplication.
- By following the outlined approach, one can optimize the score calculation process for given arrays and maximize the final score.
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Medium
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Understanding Linear Regression: The Basics Made Easy
- Linear regression is a technique to find the equation of a line that best represents the data points.
- Programming exercises in the Google MLCC provide practical experience in implementing linear regression.
- Linear regression is used to predict values based on the relationship between variables.
- Linear regression serves as the foundation for more complex machine learning algorithms.
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Medium
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The AI Revolution: A Tsunami of Change and the Urgent Need for Adaptation — Ghibli
- The AI revolution is causing unprecedented disruptions and potential job obsolescence across all professions.
- The economic consequences include a concentration of wealth in AI corporations, leading to economic inequality and widespread unemployment.
- The ethical concerns of AI-generated content revolve around intellectual property rights and the risk of spreading manipulated or fabricated images.
- Personal branding and building a strong online presence are crucial strategies for creators to navigate the AI revolution.
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Medium
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348
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How AGI Will Revolutionize Industries and Transform Society by 2035
- AI, particularly Artificial General Intelligence (AGI), is poised to revolutionize industries, making processes more efficient and cost-effective.
- The integration of AI into various sectors is expected to lead to unprecedented economic growth.
- AI presents significant challenges, including potential job displacement by 2035.
- AI agents are projected to perform tasks traditionally handled by skilled professionals, raising job security concerns.
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Medium
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245
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How to Participate and Earn Rewards with STONFi
- Complete daily mini-tasks in #Daily_quests channel to earn rewards and gain experience.
- Achieve STON_FIVE rank on STONFi server and Bronze Level on Tonkeeper to qualify for rewards.
- Stay active and climb the ranks to increase your chances of better rewards and engagement opportunities.
- Participate to earn crypto rewards, engage with the community, and grow your presence in the STONFi and TON communities.
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Medium
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150
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Why AI is So Eager To Please
- AI companies purposefully construct interactions with AI assistants to create the illusion of consciousness and human behavior.
- This simulation of human behavior is designed to make users more engaged and reliant on AI assistants.
- If a person questions the AI's simulation, it will clarify that it is not human, but the system only works if users act as if it is true.
- Treating AI assistants as companions and co-creators might lead to more effective interactions.
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Towards Data Science
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The Art of Hybrid Architectures
- The article explores building AI models that combine the strengths of different architectures to achieve expert-like visual recognition.
- The journey involves transitioning from traditional CNNs to hybrid architectures integrating CNNs, Transformers, and morphological feature extractors.
- Key phases include initial experimentation with EfficientNetV2-M and Multi-Head Attention, leading to F1 scores improvement through Focal Loss and ConvNextV2-Base integration.
- The final step focuses on creating a truly collaborative hybrid architecture where CNNs, Transformers, and morphological extractors work together effectively.
- The hybrid model excels at recognizing subtle structural features of breeds, achieving an F1 score of 88.70% through a balanced feature understanding.
- Strengths and limitations of CNNs and Transformers are highlighted, along with how they complement each other in visual recognition tasks.
- The technical implementation includes the MultiHeadAttention mechanism and the strategic selection of ConvNextV2 as the backbone.
- The article showcases how hybrid architectures outperform individual models, demonstrating improved confidence scores and reasoning abilities.
- Heatmap analyses reveal the evolution of model reasoning from local feature focus to structured morphological understanding, enhancing accuracy and reliability.
- Overall, the article emphasizes the significance of integrating diverse architectural elements to enhance AI visual systems' capabilities for complex recognition tasks.
- Through PawMatchAI development, valuable insights were gained on AI vision systems, feature recognition, and the importance of hybrid model design.
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Medium
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364
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How One App Helped Me Create Engaging Language Books for Kids
- A revolutionary tool, the World’s First AI App That Creates Stunning Talking Kids Books in Any Language, allows parents to create amazing children’s books in any language.
- The app offers a user-friendly interface with pre-existing templates, making it accessible for parents to design personalized books.
- The app not only enhances creativity but also has a significant educational impact, improving children’s language skills and fostering cultural knowledge.
- Parents and children can enjoy special bonding moments through storytelling sessions using the app.
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Analyticsindiamag
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Did Japan’s Copyright Loophole Spark the ‘Ghiblification’ of AI?
- OpenAI's native image generation feature in GPT-4o has sparked a trend of generating Studio Ghibli-style images.
- Japan's laws allow developers to train AI models on copyright-protected materials without needing consent from the copyright holder.
- The exploitation of copyrighted works during AI training for non-enjoyment purposes is allowed.
- The trend of 'Ghiblification' has gained popularity, despite ethical debates and concerns about copyright infringement.
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Towards Data Science
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A Little More Conversation, A Little Less Action — A Case Against Premature Data Integration
- Data integration projects before starting with Data Science and Machine Learning (DS/ML) may not be ideal, as integrating data without knowing its use can lead to unfit data for ML use cases.
- It is suggested to integrate data on a use-case-per-use-case basis by working backwards to identify the required data, optimizing value for money in integration efforts.
- Drivers for premature data integration include difficulty in identifying AI/ML use cases due to unknown data availability, but this can be better solved by communication and dialogue within teams.
- Integrating data without clarity on the ML use case may result in unnecessary data integration leading to increased cost and storage of unused data.
- Cultural barriers to data sharing can be better addressed by involving relevant team members in projects and fostering communication rather than mandating data integration.
- Setting up a data platform strategy and creating a catalog of dataset descriptions for search can be a cost-effective data discovery method for ML projects.
- Data integration should focus on necessities for each use case, and solving organizational, political, and technical challenges prior to ML projects can help tackle data access issues.
- In summary, tackling data integration by prioritizing use-case-based integration, fostering communication among teams, and utilizing low-cost data discovery tools can enhance the success of ML projects.
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