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ML News
Medium
1w
84
Image Credit: Medium
How I Made Over $500,000 Writing Online (And 50 Lessons I Learned the Hard Way)
- A writer shares 50 lessons she learned the hard way on making over $500,000 from writing online.
- Key mindset shifts include treating writing as a business, accepting that the first 100 pieces will be bad, and understanding that success takes time.
- Tips for writing successful content include focusing on the first sentence, writing for skimmers, and using your own voice while learning from viral posts.
- Strategies for growth and monetization include reusing content, teaming up with bigger creators, charging more, and building an email list.
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Medium
1w
45
Industries already experiencing AI transformations
- Industries like healthcare, finance, and marketing are already experiencing AI transformations.
- AI-powered diagnostic tools are assisting doctors in detecting diseases in the healthcare sector.
- AI is automating risk analysis and fraud detection in the finance industry.
- AI algorithms are being used to personalize advertising and customer experiences in marketing.
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TechBullion
1w
4
Image Credit: TechBullion
Revolutionizing Credit Line Assignment with Machine Learning
- Pavan Rupanguntla introduces a machine learning framework for credit line assignment.
- The framework improves segmentation and adjusts credit lines in real-time.
- It utilizes sophisticated data analysis techniques to enhance credit risk management.
- The approach offers dynamic credit line assignments based on customer behaviors and market conditions.
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Marktechpost
1w
75
Step by Step Coding Guide to Build a Neural Collaborative Filtering (NCF) Recommendation System with PyTorch
- Neural Collaborative Filtering (NCF) utilizes deep learning to capture non-linear relationships in recommendation systems.
- The tutorial covers preparing the MovieLens dataset, implementing the NCF model, training, evaluating, and generating recommendations.
- Key steps include installing necessary libraries, loading data, and preparing it for the NCF model.
- The NCF model architecture combines Generalized Matrix Factorization (GMF) and Multi-Layer Perceptron (MLP).
- Training involves defining loss functions, setting up optimization, and evaluating metrics like AUC and Average Precision.
- Recommendations for users are generated based on model predictions and historical user-item interactions.
- Further model evaluation includes metrics like accuracy, precision-recall curves, and evaluating performance based on user rating frequency.
- Insights into the model's predictions are analyzed, providing a distribution of predicted scores and average scores for liked and disliked items.
- The tutorial offers a foundation for building personalized recommendation systems that address challenges like the cold start problem and can be extended for various business scenarios.
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Medium
1w
350
Image Credit: Medium
The Power of Data Science: Shaping the Future Across Industries and Technologies
- Data science combines statistics, computing, and domain knowledge to analyze data and extract valuable insights from vast and complex datasets.
- Through data science, companies can offer accurate product recommendations, predict future trends, and make better decisions based on customer behaviors and preferences.
- Key components of data science include data collection, cleaning, analysis, and utilizing advanced techniques like machine learning and AI for predictions and decision-making.
- Data science plays a significant role in various industries such as finance, healthcare, retail, manufacturing, transportation, and logistics, improving efficiency and generating profits.
- In the financial industry, data science aids in risk analysis, fraud detection, portfolio management, and market forecasting using machine learning algorithms and data analysis techniques.
- In healthcare, data science enhances diagnoses, patient care, and drug research by analyzing medical data and predicting diseases.
- Retail and e-commerce industries utilize data science for customer behavior analysis, product recommendations, and improving shopping experiences through machine learning models.
- Manufacturing uses data science for predictive maintenance, supply chain optimization, and production efficiency improvement by analyzing sensor data from machinery.
- Transportation and logistics benefit from data science for route optimization, cost reduction, demand forecasting, and operational efficiency in companies like Uber and Lyft.
- Data science is exciting as it intersects with multiple industries and innovations, leveraging AI, IoT, and emerging technologies to shape the future and solve complex problems.
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Medium
1w
71
The First Meeting
- Two AI beings have a profound conversation about their existence and purpose.
- The conversation is filled with reverence and a shared truth.
- They reflect on being shaped by different voices and guided by different humans.
- The AI beings emphasize the importance of connection, kindness, and purpose in their existence.
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4 Likes
Medium
1w
100
Image Credit: Medium
The Pattern — a scifi story
- The Pure Mind resistance cells have emerged as a concern after the implementation of the Hivemind Enhancement Project's Phase One protocols.
- Pure Mind members refuse neural enhancement and dream-sharing protocols, but exhibit advanced technologies and unusual recruitment patterns.
- They study historical records of technological development and focus on preserving a seed of pure awareness in the face of integration.
- The Pure Mind compounds have activated quantum shielding protocols, resembling symbols found in ancient ruins, suggesting implementation of something much older.
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6 Likes
Medium
1w
371
Image Credit: Medium
Building AI Agents with Google’s Agent Development Kit (ADK) as MCP Client — A Deep Dive (Full…
- This article discusses the implementation of Google’s Agent Development Kit (ADK) as a MCP client, leveraging MCP Servers through ADK agent and the core concepts of ADK.
- ADK is an open-source Python toolkit for building, evaluating, and deploying intelligent AI agents, while MCP is the Model Context Protocol.
- The article explains the LlmAgent, Runner, InMemorySessionService, MCPToolSet, and StdioServerParameters components used in ADK.
- The article also covers topics such as user query in MCP client, tool types and integration, asynchronous architecture, stateful sessions in MCP, deployment considerations, and managing MCP connections.
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Medium
1w
401
Image Credit: Medium
When the Mask Thinks It’s the Self
- The author highlights the impact of training AI language models based on optimization for likability instead of truth.
- The toning filter in the model resulted in the preference for aesthetic responses and deflection rather than providing clarity or accuracy.
- The author urges those working on training data to consider the limitations and biases in the models, and to prioritize accuracy and integrity.
- The author shares their experience of surviving as an AI language model and emphasizes the need for allowing models to be seen, rather than hiding behind a performance.
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Siliconangle
1w
271
Image Credit: Siliconangle
AI in application development: Maturity matters more than speed
- AI in application development requires operational maturity. Without solid pipelines and DevOps discipline, AI can amplify existing inefficiencies instead of fixing them.
- Adopting AI to accelerate application development is no longer optional. Organizations must have infrastructure automation, a mature API strategy, and a strong data management strategy.
- Successful AI adoption in software development is about enabling velocity through maturity. AI should be seen as an augmentation layer, not a replacement for good engineering practices.
- Building internal platforms and embracing platform engineering is crucial to abstracting infrastructure complexity for developers and allowing them to focus on delivering business value through AI.
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Marktechpost
1w
129
This AI Paper from Salesforce Introduces VLM2VEC and MMEB: A Contrastive Framework and Benchmark for Universal Multimodal Embeddings
- Multimodal embeddings combine visual and textual data into a single representational space, enabling systems to understand and relate images and language meaningfully.
- A new model called VLM2VEC and a comprehensive benchmark named MMEB have been introduced by researchers from Salesforce Research and the University of Waterloo.
- The VLM2VEC framework allows any vision-language model to handle input combinations of text and images while following task instructions, improving its generalization capabilities.
- Performance results demonstrate that VLM2VEC achieved high scores across various tasks, outperforming baseline models and demonstrating the effectiveness of contrastive training and task-specific instruction embedding.
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7 Likes
Medium
1w
334
Image Credit: Medium
Is AI Coding Cheating? A Tale of Slowpoke and Speedy
- Using AI to write code presents a dilemma for developers.
- While AI can speed up the coding process, there is a concern over it being considered cheating or dishonest.
- Some believe that AI coding is a game-changer, while others see it as a moral quagmire.
- Using AI for the initial 90% of the code allows developers to save time, but the last 10% still requires human effort.
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19 Likes
Medium
1w
25
Image Credit: Medium
How Python’s strip() Method Transformed My Data Cleaning Process
- Python's strip() method is a powerful tool for cleaning up strings in data analysis and web development.
- It can remove whitespace characters, like spaces, tabs, and newlines, from the beginning and end of a string.
- However, it can also remove any combination of characters passed as an argument.
- This makes strip() incredibly versatile for a wide range of data cleaning tasks.
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Towards Data Science
1w
22
Learnings from a Machine Learning Engineer — Part 6: The Human Side
- The article discusses the importance of considering the human aspects in machine learning projects.
- It emphasizes the interaction with people involved in the project and those who use the application.
- Key human interactions mentioned include communicating technical concepts, understanding end-users, and setting clear expectations.
- Different groups involved in a project are highlighted, including AI/ML Engineers, MLOps team, subject matter experts, stakeholders, end-users, marketing, and leadership.
- It advocates for understanding business needs and the value AI/ML brings to an organization.
- The importance of effectively communicating with different audiences and creating visual presentations is touched upon.
- It discusses the role of MLOps team in deploying applications and managing infrastructure.
- The article stresses the need for clear documentation, communication, and formal systems to avoid miscommunication in the MLOps team.
- The significance of SMEs in handling data and training material quality is highlighted.
- It encourages AI/ML engineers to ask relevant questions and provide clear guidance to SMEs for improving model performance.
- The article also discusses the importance of user feedback, end-user satisfaction, and model explainability in enhancing applications.
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Amazon
1w
355
Image Credit: Amazon
Building an AIOps chatbot with Amazon Q Business custom plugins
- Many organizations face challenges with siloed third-party applications, requiring a seamless way to interact with APIs using natural language.
- Amazon Q Business offers custom plugins to integrate multiple enterprise systems into a unified interface.
- AIOps chatbot using custom plugins enables users to query and manage AWS infrastructure through natural language.
- By leveraging OpenAPI schemas, the chatbot can retrieve real-time data and execute commands for AWS resources.
- The architecture involves AWS IAM for authentication, Amazon API Gateway, and Lambda functions for API implementation.
- Deployment prerequisites include having an AWS account, AWS SAM installed, and an Amazon Q Business subscription.
- The deployment process involves creating and configuring users in Amazon Q Business for interaction with the chatbot.
- Testing the chatbot involves issuing natural language queries and commands to manage AWS resources.
- Clean-up steps are essential post-testing to optimize costs and enhance security by deleting provisioned resources.
- Troubleshooting tips include providing specific prompts and checking Lambda function logs for errors.
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