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Deep Learning News
Medium
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Mapping Hidden Patterns: A Practical Guide to Kohonen Self-Organizing Maps for Data Clustering and…
- Many datasets contain hidden patterns and relationships that are challenging to identify and interpret using traditional techniques.
- This essay demonstrates the practical application of Kohonen Self-Organizing Maps (SOMs) on a synthetic dataset, covering data preprocessing, hyperparameter tuning, and visualization.
- The SOM successfully organized data into well-defined clusters, achieving a high classification accuracy (98.3%) with the K-Nearest Neighbors (KNN) model.
- SOMs are effective for clustering and visualizing complex data, preserving topological relationships, and enabling accurate classification when combined with other models.
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Medium
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Hacking The System Design: How Search Engine Understand and Deliver Results
- Search engines employ complex processes to ensure users can quickly find relevant content, this blog explores the core components of a search engine, how they operate, and what factors influence their performance.
- Crawling is the first step in how search engines gather information about web pages utilizing web crawlers, spiders, or bots.
- After crawling, indexing takes place where the search engine analyzes and organizes the data collected from web pages into a structured database known as the search index.
- Ranking is the process by which search engines determine the order of indexed pages displayed in response to a user’s query.
- Once the ranking is complete, search engines present the results on a Search Engine Results Page (SERP), including both organic results (unpaid) and paid advertisements.
- Several factors determine how well a page ranks in search results, including content relevance, page speed, and user engagement.
- Continuous learning is a key component where search engines constantly learn from user interactions to improve the accuracy and relevance of their results.
- Search engines can be categorized into different types based on their focus such as general search engines, vertical search engines, meta search engines, and local search engines.
- Search engines evaluate how closely a webpage matches a user’s query using several methods such as keyword matching and semantic search.
- Query rewriting enhances search accuracy by transforming user queries into more effective search terms by utilizing synonym expansion, spelling correction, and contextual understanding.
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Medium
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How LinkedIn Uses AI to Detect Deepfakes in Profile Pictures
- LinkedIn has developed an AI-powered system to detect deepfake profile pictures.
- They trained a deep learning model, EfficientNet-B1, on a diverse dataset of real profile pictures and AI-generated images.
- The model was able to accurately distinguish between real and AI-generated images, achieving a true positive rate of 98% with minimal false positives.
- This solution demonstrates the potential of deep learning in ensuring user authenticity on social media platforms.
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Medium
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Shallow Learning vs. Deep Learning: Is Bigger Always Better?
- In machine learning, the choice of model complexity can make a significant impact on the effectiveness and efficiency of solving different problems.
- Shallow learning algorithms are simpler and often faster, while deep learning models comprise multiple layers and can learn more complex patterns.
- Shallow learning is suitable for simple tasks like credit scoring, while deep learning is beneficial for complex problems like object detection in autonomous vehicles.
- Starting with a shallow model as a baseline is often a good approach, but deep learning excels in tackling sophisticated problems that require larger-scale learning.
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Medium
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Learning Car Speed Using Smartphone Sensors
- Accurate car speed estimation is crucial for traffic management and navigation systems.
- Smartphones with built-in accelerometers are being used to estimate car speed in real-time.
- CarSpeedNet, a deep learning model, uses smartphone accelerometer data to estimate car speed.
- CarSpeedNet achieves high accuracy with an RMSE of 2.9 m/s and MAE of 1.3 m/s.
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Medium
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Retrieval Augmented Generation
- Retrieval Augmented Generation (RAG) integrates external knowledge bases to enhance large language models (LLMs).
- RAG combines the strengths of retrieval systems and generation models for improved performance.
- RAG models achieve significant improvements in tasks like question answering and text generation.
- RAG adoption is increasing globally, with a focus on real-time information and context-specific responses.
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Medium
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Title: The Art of Finding Balance in a Hectic World
- Identify your top priorities and allocate time accordingly
- Set boundaries and learn to say no to commitments that don't align with your goals
- Incorporate mindfulness practices to reduce stress and improve mental clarity
- Make time for self-care to recharge and restore energy
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Medium
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Understanding the Concept and Mechanisms of Retrieval-Augmented Generation
- Retrieval-Augmented Generation (RAG) combines large language models (LLMs) with the ability to retrieve and incorporate relevant external information in NLP.
- RAG addresses the limitations of traditional LLMs, providing more accurate and contextually relevant text by accessing real-time information.
- The adoption of RAG is increasing, especially in customer service, content generation, and question-answering systems.
- Challenges for RAG include data freshness, bias in retrieval, and variations in acceptance and implementation across different regions and cultures.
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Medium
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The Integration of Generative AI in Weather Forecasting
- The use of generative AI in weather forecasting is a significant advancement in meteorology.
- Generative AI models generate ensemble forecasts to capture weather uncertainty.
- SEEDS model by Google Research uses denoising diffusion probabilistic models for efficient ensemble forecasting.
- There are challenges and variations in the application of generative AI in weather forecasting globally.
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Medium
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Python Beginner Tutorial: From Zero to Coding in 30 Days
- Ideal for beginners: readable syntax, vast community, versatile applications, strong job market.
- What You’ll Learn in This Python Beginner Tutorial: Essential Python concepts and fundamentals, practical coding examples and exercises, real-world applications and projects, best practices for writing clean, efficient code.
- Why This Tutorial is Different: Dives deep into each concept while maintaining clarity and simplicity, combines theory with practical examples for better understanding.
- Prerequisites for This Tutorial: A computer (Windows, Mac, or Linux), basic computer literacy, enthusiasm to learn (no prior programming experience required).
- Why Choose Python as Your First Programming Language: Simplicity and Readability, Job Market Demand and Salary Prospects, Versatility Across Industries, Community Support and Resources.
- Getting Started with Python: Setting Up Your Development Environment: Installing Python, Choosing an IDE, Understanding the Python Interpreter, Testing Your Setup, Troubleshooting Common Setup Issues.
- Python Fundamentals for Beginners: Variables and Data Types in Python, Basic Python Syntax, Common Beginner Mistakes to Avoid, Tools to Help Avoid Common Mistakes.
- Next Steps in Your Python Journey: Advanced Python Topics to Master, Web Development with Flask, Data Analysis with Pandas.
- Python Learning Resources and Community: Online Courses and Tutorials, Essential Python Books, Official Documentation and References, Python Communities and Forums.
- Conclusion: Your Python Programming Journey Starts Now, Key Takeaways, Next Steps, Recommended Resources, Final Tips for Success.
- Get Started Today! Take the first step in your Python journey, Learning to code is a journey, not a race, The best time to start learning was yesterday; the second best time is now. Happy coding!
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Medium
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Unlock the Power of InfraNodus: Revolutionizing Text Analysis Beyond ChatGPT
- InfraNodus is an open-source tool for advanced text analysis and visualization.
- It uses text network analysis to reveal hidden structures and relationships in text.
- Key features include interactive visualization, AI-powered insights, and bias detection.
- InfraNodus surpasses traditional AI models like ChatGPT in terms of topic extraction, customization, and control.
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Medium
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Investigating Generative Text Analysis Models: A Comprehensive Look at Large Language Models (LLMs)…
- LLMs, or Large Language Models, play a vital role in industries like data science, customer service, and content production.
- Generative text analysis models are capable of analyzing large amounts of textual data, producing human-like responses, and synthesizing information.
- LLMs, such as GPT, BERT, and T5, are frequently used models for natural language comprehension and generation.
- Generative text analysis models have important applications in sentiment analysis, condensing lengthy texts, customer service responses, and data organization.
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Medium
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The Role of Tokenizers and Transformers in LLMs
- Tokenizers break down raw text into manageable pieces called tokens.
- Transformers process tokens to produce embeddings that capture the meaning of each token in its context.
- Precision plays a role in accurately calculating embeddings and subsequent activations in transformers.
- Efficiency in tokenization, encoding, and attention mechanisms allows for real-time interactions and rapid AI responses.
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Nvidia
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“India Should Manufacture Its Own AI,” Declares NVIDIA CEO
- India has the opportunity to lead in artificial intelligence (AI) and become an AI-driven economy, according to NVIDIA CEO Jensen Huang.
- Huang highlighted India's IT expertise and vast data resources as key advantages for AI development.
- He emphasized the importance of India manufacturing its own AI infrastructure and not exporting data to import intelligence.
- NVIDIA is collaborating with Indian companies and upskilling developers to prepare the workforce for the AI revolution.
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Nvidia
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Start Local, Go Global: India’s Startups Spur Growth and Innovation With NVIDIA Technology
- India is one of the top six global economies leading generative AI adoption and has seen rapid growth in its startup and investor ecosystem.
- More than 2,000 Indian startups are part of NVIDIA Inception, a free program designed to accelerate innovation and growth through technical training and tools, go-to-market support and opportunities to connect with venture capitalists.
- At NVIDIA AI Summit, around 50 Indian startups showcased AI innovations delivering impact in fields such as customer service, sports media, healthcare and robotics.
- Bengaluru-based startup CoRover.ai's conversational AI platform includes text, audio and video-based agents, serving customers of Indian Railway Catering and Tourism Corporation and delivering 70% improvement in customer satisfaction rates.
- Mumbai-based startup, VideoVerse, built a family of AI models to support content creation in the sports media industry—enabling global customers including the Indian Premier League for cricket, the Vietnam Basketball Association and the Mountain West Conference for American college football to generate game highlights up to 15x faster.
- Mumbai-based startup Fluid AI offers generative AI chatbots, voice calling bots, and a range of application programming interfaces to boost enterprise efficiency.
- Bengaluru-based Karya is a smartphone-based digital work platform that enables members of low-income and marginalized communities across India to earn supplemental income by completing language-based tasks that support the development of multilingual AI models.
- Nearly 100,000 Karya workers are recording voice samples, transcribing audio, or checking the accuracy of AI-generated sentences in their native languages.
- Karya is working with Bill and Melinda Gates Foundation to build the largest gender-intentional, open-source AI dataset.
- Indian companies are building multilingual language models with NVIDIA NeMo, a cloud-native framework and suite of microservices for developing generative AI.
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