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Data Science News
Medium
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9 PyTorch Layers You Must learn it
- torch.nn.Linear: Applies a linear transformation to incoming data by multiplying the input with a weight matrix and adding a bias.
- Convolutional Layer: Applies convolutional filters to input data to extract spatial or temporal patterns.
- Recurrent Layers: Used to handle sequential data by keeping information over time and learning temporal dependencies.
- Embedding Layer: Converts input indices into dense vectors of fixed size, commonly used for representing discrete items.
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Medium
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The Current State of Data Science and What Lies Ahead in the Next 5 Years
- The applications of AI and machine learning have moved from niche to critical components in various sectors.
- Cloud computing has provided scalable solutions that support big data analytics without massive physical infrastructure.
- Increasing scrutiny on data privacy, security, and ethical use has led to regulations like the GDPR and CCPA.
- Data-driven decision-making is now a standard, enabling a shift towards company-wide data literacy and evidence-based strategies.
- Automation within data science is advancing rapidly, with AutoML streamlining the model development process.
- Explainable AI will become a significant focus, with new tools and frameworks.
- Real-time analytics and edge computing will grow, spurring advancements in data processing.
- More stringent privacy and security measures like differential privacy will become necessary.
- DataOps and MLOps will become crucial operational frameworks.
- The demand for skilled data professionals will continue to rise.
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Medium
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Revolutionizing Multilingual AI: How MYTE is Redefining Text Encoding
- Multilingual AI models often struggle with handling linguistic diversity and biased results.
- Standard UTF-8 encoding fails to capture morphological differences between languages.
- MYTE (Morphology-Driven Byte Encoding) is a novel byte encoding scheme designed to address these challenges.
- MYTE leverages morphological information to improve performance and fairness of multilingual AI models.
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VentureBeat
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Snowflake’s ‘data agents’ leverage enterprise apps so you don’t have to
- Snowflake Intelligence is a platform that helps enterprise users create and deploy dedicated 'data agents' for extracting relevant business insights from hosted data and third-party data sources beyond their data cloud instance.
- The data agents can take actions across different tools and applications, such as Google Workspace and Salesforce.
- The AI creation and deployment platform will live within the same cloud data warehouse or lakehouse provider, eliminating the need for another tool.
- Snowflake has integrated AI capabilities on top of its core data platform, including Document AI, Cortex AI, and Snowflake Copilot.
- Snowflake Intelligence expands these capabilities by enabling teams to set up enterprise-grade data agents that tap siloed third-party tools and structured/unstructured data sources across different systems.
- The platform uses Cortex Analyst and Cortex Search (part of Cortex AI architecture) to deploy agents that accurately retrieve and process specific data assets from both unstructured and structured data sources to provide relevant insights.
- The users interact with the agents in natural language, asking business-related questions while the agents identify the relevant internal and external data sources to provide answers.
- Once the relevant data is surfaced, the user can ask data agents to take specific actions around the generated insights, like entering the insights into an editable form or uploading the file to their Google Drive.
- Snowflake has not given a timeline on its availability, but it will go into private preview soon.
- Snowflake Intelligence will be natively integrated with the company's Horizon Catalog at the foundation level, allowing users to run agents for insights right where they discover, manage, and govern their data assets.
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Mit
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3 Questions: Inverting the problem of design
- The DeCoDE Lab at MIT is combining machine learning and generative AI techniques, physical modeling, and engineering principles to tackle design challenges and enhance the creation of mechanical systems.
- Linkages, one of the group's recent projects, explores ways planar bars and joints can be connected to trace curved paths.
- The project uses the idea of self-supervised contrastive learning approaches to learn the representation of the design and how it works.
- This technique helps to incorporate precision into generative AI models and contributes to automated discovery.
- With the Linkages project, the team showed that the approach enabled solving problems more precisely and significantly faster, at 28 times less error and 20 times faster than prior state-of-the-art approaches.
- The Linkage method uses contrastive learning between the represented mechanism joints in graphs and another model that creates an embedding for the curves, and then connects these two modalities using contrastive learning.
- The method can find new mechanisms with precision through additional optimization steps beyond identifying candidate mechanisms.
- This proof of concept shows that the method is effective on discrete and continuous systems and can potentially transfer to many engineering applications.
- Future work for the project includes exploring more complex mechanical systems and more physics, as well as thinking about how precision in langue models can be incorporated.
- The project was supported by the MIT-IBM Watson AI Lab.
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Medium
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Introduction to AI Assistance: Your Guide to the Future Helper
- AI assistance refers to software and tools powered by artificial intelligence that help humans perform tasks more efficiently.
- AI assistants learn from data, adapt to user preferences, and can perform a range of tasks.
- They use machine learning and natural language processing to interpret user requests and provide human-like responses.
- AI assistance can be integrated into daily routines to free up time for strategic thinking and creativity.
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Medium
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Data Science Collaboration In The Age Of AI
- Task-based collaboration is a tactical and transactional approach while relationship-based collaboration is a more strategic partnership.
- Task-based collaboration involves hiring someone to carry out specific tasks immediately while relationship-based collaboration involves finding a partner to own and manage part of your work for a longer period of time.
- Both types of collaboration have their advantages depending on your business needs.
- With generative AI on the rise, tactical tasks are more exposed to being replaced completely or shrunk in scope as AI handles more of the technical items.
- Organizations will continue to require both types of collaboration, but demand for relationship-based collaboration skills is expected to remain steadier compared to task-based skills.
- The demand for strategic, relationship-based collaboration skills will be much more resistant to AI replacement compared to task-based skills.
- People who offer strategic, relationship-based collaboration skills will likely be in greater demand over time as compared to those who offer basic coding tasks.
- One's requirement of either of the models of collaboration depends on one's preference.
- With generative AI on the rise, it is essential for individuals to build the skills required to be a relationship-based partner to remain relevant in the future.
- Regardless of whether one plans to own a business or collaborate within their organization, focus should be on building strategic, relationship-based collaboration skills.
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Medium
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Gradient Boosting Machine Explained in Detail
- Gradient boosting is a technique based on boosting that involves building many weak learners.
- In the case of gradient boosting, each subsequent model focuses on pseudo-residuals instead of directly on the errors of the previous one.
- Each new tree is trained to minimize the gradient of the loss function with respect to the current ensemble's predictions.
- The gradient boosting algorithm was introduced by Jerome H. Friedman in 1999 and is widely used today.
- There are numerous variations of the gradient boosting algorithm, including GBM, XGBoost, LightGBM, and CatBoost.
- In gradient boosting, trees are constructed sequentially, meaning each tree is built based on information from previously built trees.
- The algorithm for gradient boosting involves initialization, calculating pseudo-residuals, creating the next tree, and updating the model.
- An important part of the gradient boosting method is regularization by shrinkage in the update rule.
- For example, gradient boosting algorithm was explained step by step for a regression problem, with code implementation in Python.
- The quality of the regressor should be evaluated on a test set.
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Datarobot
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The next evolution of AI for business: our brand story
- DataRobot announces a new approach to AI focused on business outcomes.
- The company aims to deliver AI that maximizes impact and minimizes risk for businesses.
- DataRobot offers an open platform, user-friendly tooling, and high-impact applications to seamlessly integrate AI into existing processes.
- They also provide built-in interoperability, governance, and observability capabilities to scale AI with confidence.
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Datarobot
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The DataRobot Enterprise AI Suite: driving the next evolution of AI for business
- DataRobot has announced the availability of the DataRobot Enterprise AI Suite.
- The platform gives organisations everything they need to infuse AI into their business, secure outcomes, and empower teams.
- The customisable AI apps and agents codify patterns in AI use cases into fully customisable templates.
- This enables businesses to focus on outcomes instead of piecing together pipelines, sharing built apps as templates or adding them to the DataRobot AI App Gallery for other teams to use.
- The platform automates the process from data prep and testing to CI/CD pipelines and governance.
- The DataRobot Enterprise AI Suite allows businesses to apply AI that aligns with their goals.
- Capabilities include: agentic flows, predictive insights and retrieval-augmented generation.
- The DataRobot Enterprise AI Suite delivers AI which is “compliance-ready,” integrating red-team testing, real-time governance and intervention and moderation across all agentic and generative AI solutions.
- It also allows for integration and work with the most popular LLMS and models, or with the tools preferred by businesses.
- Optical character recognition transforms unstructured documents into generative AI-ready data, accelerating model training and enhancing outcomes.
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VentureBeat
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Edge data is critical to AI — here’s how Dell is helping enterprises unlock its value
- Dell is introducing advancements to its Dell NativeEdge edge operations software platform.
- The platform aims to simplify the deployment, scaling, and use of AI in edge environments.
- It offers high-availability capabilities, virtual machine migration, and pre-built blueprints for AI deployment.
- Dell NativeEdge has been deployed by customers in various industries, including manufacturing, farming, and infrastructure inspection.
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Analyticsindiamag
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Connecty AI raises $1.8M to Transform Enterprise Intelligence
- Connecty AI, an enterprise agent company, has raised $1.8 million in pre-seed funding.
- The funding will be used to expand their context engine and enhance additional data sources.
- Connecty AI aims to transform enterprise intelligence by providing a context-aware platform.
- The global AI Analytics market is projected to reach $223 billion by 2034.
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Analyticsindiamag
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This Bengaluru Startup Made the Fastest Inference Engine, Beating Together AI and Fireworks AI
- Bengaluru-based startup Simplismart is a leader in creating high-performance AI deployment tools.
- It competes in inference speed on the software side, not focusing on the hardware.
- Simplismart’s inference engine optimizes for all model deployments and supports a range of models.
- The company’s platform offers a declarative language to simplify the fine-tuning, deployment and monitoring of AI models.
- In October 2021, Simplismart secured $7 million in a Series A funding round led by Accel.
- The company offers a comprehensive solution letting business build and manage personalized inference engines.
- Simplismart aims to give enterprises the autonomy they need to make AI work for them on their terms.
- The company’s platform allows enterprises to host and manage AI models on their premises, ensuring greater security and control.
- The platform offers a level of customisation that APIs can’t match.
- Simplismart’s innovation lies in its MLOps platform, designed for on-premises enterprise deployments.
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Medium
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Apply Now for the Best Data Science Course: Get Microsoft and IBM Certified for Career Growth |…
- Digicrome offers an exclusive opportunity for a high-paying career in data science.
- The Digicrome Data Science Course offers a duration of 43 weeks with live interactive sessions.
- Upon completion, students will receive Microsoft and IBM certifications.
- The course covers various tools such as Python, SQL, Power BI, Tableau, TensorFlow, and more.
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Medium
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The True Value of Data: Insights from Industry Leaders.
- The data market is booming, with major players offering a range of services.
- Differentiating valuable data providers depends on their ability to provide accurate and relevant insights.
- Choosing the right data provider involves understanding data collection and structure.
- Success in monetizing data depends on addressing pain points and enhancing decision-making.
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