Videos de Data Analytics
Videos etiquetados con "Data Analytics"
Data Analytics 4 videos
Anthropic Just Dropped Their Internal Data Playbook (copy this)
Anthropic just dropped their entire internal data playbook. Here's what they're doing and how it affects your career. 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://dcj.app/newsletter-DTgIjn 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://dcj.app/training-DTgIjn 👩💻 Want to land a data job in less than 90 days? 👉 https://dcj.app/daa-DTgIjn 👔 Ace The Interview with Confidence 👉 https://dcj.app/interviewsimulator-DTgIjn 📄 Read Anthropic's full data playbook 👉 https://claude.com/blog/how-anthropic-enables-self-service-data-analytics-with-claude ⌚ TIMESTAMPS 00:00 – Anthropic dropped their data playbook 02:39 – Why AI analytics keeps failing 05:24 – How they hit 95% accuracy 09:24 – What a Claude skill is 14:39 – None of this is actually new 17:09 – Still hiring data people 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://dcj.app/youtube-averysmith-DTgIjn 🤝 LinkedIn: https://linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://tiktok.com/@verydata 💻 Website: https://dcj.app/datacareerjumpstart-DTgIjn
The Math Behind Deepfakes (GANs Explained)
Discover the math behind deepfakes and how Generative Adversarial Networks (GANs) changed AI forever. Before 2014, teaching a machine to create highly realistic data from scratch involved incredibly slow and complex probabilistic calculations. That all changed when Ian Goodfellow and his team introduced a completely new paradigm. By pitting two neural networks against each other in a continuous Minimax game, they bypassed the heavy calculus and unlocked the modern era of generative AI. In this video, we break down the exact mechanics of GANs. You will learn how the Generator acts as a counterfeiter trying to create perfect fakes, while the Discriminator acts as the police trying to catch them. We also dive into the training loop, the significance of the Jensen-Shannon divergence, and the mathematical proof that guarantees these models can perfectly mimic reality. 00:00 - The dense math of early generative models 00:58 - Re-framing generation as an adversarial game 02:27 - The Counterfeiter and the Police analogy 03:11 - The Minimax game and training loop steps 04:24 - Visualizing the push and pull of data distribution 05:58 - Proving the perfect fake mathematically 06:40 - The Jensen-Shannon divergence explained 07:31 - Why GANs matter for modern AI and deepfakes 🔗 Stay Connected 👉 Subscribe on YouTube: https://www.youtube.com/@insightforge_9 👉 Read the Blog (AI, Chatbots & Automation): https://insightforge-ai.blogspot.com/ 👉 Connect on LinkedIn: https://www.linkedin.com/in/mohit-rathod-7991241b5/ 👉 Join the Newsletter: https://www.linkedin.com/newsletters/7330620395449937920/ 👉 Follow on Instagram: https://www.instagram.com/insightforge.ai/ #GenerativeAI #MachineLearning #Deepfakes
Math for Machine Learning 10: Matrix Algebra Explained | Linear Algebra for AI & ML #MathForML
Mathematics is the foundation of Machine Learning, Artificial Intelligence, Data Science, Deep Learning, Computer Vision, Natural Language Processing, and modern computational technologies. Among all mathematical concepts used in Machine Learning, Matrix Algebra plays a critical role because almost every machine learning algorithm relies on matrix operations, vector spaces, transformations, and linear algebraic computations. In this video, we provide a detailed explanation of Matrix Algebra for Machine Learning as part of the Math for ML series. This session focuses on understanding matrices, matrix operations, matrix multiplication, determinants, inverses, eigenvalues, eigenvectors, vector spaces, and their practical applications in Machine Learning and Artificial Intelligence. Whether you are a beginner in Machine Learning, a Data Science student, an Artificial Intelligence enthusiast, a Computer Science learner, or a professional looking to strengthen your mathematical foundation, this lecture will help you understand one of the most important mathematical tools used in modern AI systems. 📚 Topics Covered in This Video ✅ Matrix Algebra Fundamentals ✅ Linear Algebra for Machine Learning ✅ Matrix Operations ✅ Matrix Addition and Subtraction ✅ Matrix Multiplication ✅ Matrix Transpose ✅ Determinants ✅ Matrix Inverse ✅ Rank of a Matrix ✅ AI Mathematical Foundations ✅ Data Science Mathematics 📖 Why Matrix Algebra is Important in Machine Learning Matrix Algebra helps us: • Represent large datasets efficiently • Process high-dimensional information • Build recommendation systems • Train neural networks • Develop computer vision applications • Solve complex mathematical problems Matrix operations are at the heart of almost every Machine Learning and Artificial Intelligence algorithm. 🎯 Applications of Matrix Algebra in AI & Machine Learning Matrix Algebra is widely used in: ✔ Machine Learning Algorithms ✔ Artificial Intelligence Systems ✔ Deep Learning Models ✔ Neural Networks ✔ Computer Vision ✔ Data Mining ✔ Robotics ✔ Scientific Computing ✔ Financial Analytics A strong understanding of matrix algebra significantly improves your ability to understand advanced Machine Learning concepts. 📚 Important Concepts Potentially Covered ✔ Matrix Representation ✔ Matrix Operations ✔ Matrix Multiplication ✔ Determinants ✔ Inverse Matrices ✔ Rank of Matrices ✔ Linear Independence ✔ Singular Value Decomposition ✔ Matrix Factorization ✔ Linear Transformations ✔ Numerical Computation 🎓 Useful For • Machine Learning Students • Data Science Aspirants • Artificial Intelligence Learners • Computer Science Students • Research Scholars • Software Developers • AI Professionals 📚 Relevant Courses and Examinations This lecture is useful for: • Machine Learning Courses • Artificial Intelligence Programs • Data Science Courses • Research Methodology Courses • Advanced Mathematics Courses • AI Certification Programs • Professional Analytics Training 📝 Learning Strategy To master Matrix Algebra for Machine Learning: 📌 Understand matrix concepts thoroughly 📌 Practice matrix operations regularly 📌 Learn linear algebra fundamentals 📌 Understand geometric interpretations 📌 Practice computational methods 📌 Build conceptual clarity 📌 Connect mathematics with AI applications 📚 Learning Outcomes After watching this lecture, you will be able to: ✔ Understand Matrix Algebra concepts ✔ Perform matrix operations confidently ✔ Apply linear algebra in Machine Learning ✔ Understand AI mathematical foundations ✔ Build a strong Data Science foundation ✔ Understand neural network mathematics ✔ Prepare for advanced AI concepts This lecture is part of a comprehensive Math for Machine Learning series designed to help students build a strong mathematical foundation for Artificial Intelligence, Data Science, Machine Learning, Deep Learning, and advanced computational fields. If you found this lecture helpful, please Like, Share, and Subscribe for more Machine Learning Mathematics lectures, AI tutorials, Data Science concepts, Linear Algebra discussions, and advanced educational content. 📞 Academic Guidance & Machine Learning Preparation Support Sourav Sir's Classes Helpline: 9836870415 Website: www.souravsirclasses.com #MachineLearning #MathForML #MatrixAlgebra #LinearAlgebra #ArtificialIntelligence #DataScience #DeepLearning #NeuralNetworks #AI #ML #ComputerScience #Mathematics #Statistics #DataAnalytics #MachineLearningCourse #AIEngineering #ComputerVision #NLP #DataMining #PredictiveAnalytics #EngineeringMathematics #MathTutorial #MLTutorial #AICourse #LinearTransformations #Eigenvalues #Eigenvectors #DataScienceTraining #TechnologyEducation #Analytics
The AI Factory: Engineering Modern LLM Inference Pipelines | Uplatz
Modern AI systems are no longer simple models running isolated predictions—they operate like massive digital factories processing billions of requests, orchestrating GPUs, managing memory, and delivering intelligent responses at global scale. In this video, we explore “The AI Factory” and break down how modern LLM inference pipelines are engineered for performance, scalability, and efficiency. This video is by Uplatz. You’ll learn how large language model inference works behind the scenes, from token generation and request routing to distributed GPU execution and response optimization. We explain why inference engineering has become one of the most critical challenges in the generative AI era. The video dives into core components of modern inference pipelines including model serving, batching strategies, KV cache management, GPU scheduling, vector databases, retrieval-augmented generation (RAG), and low-latency orchestration systems. You’ll understand how organizations optimize infrastructure to reduce inference costs while maintaining performance and responsiveness. We also explore technologies and frameworks used in production AI systems such as Kubernetes, Ray, and vLLM for scalable AI deployment and inference acceleration. Additionally, we discuss concepts like model quantization, mixture-of-experts (MoE) architectures, inference parallelism, autoscaling, observability, and AI infrastructure optimization. Learn how companies engineer AI platforms capable of serving millions of users across enterprise applications, copilots, AI agents, and multimodal systems. Whether you're an AI engineer, platform architect, DevOps professional, cloud engineer, researcher, or technology enthusiast, this video provides a practical and structured understanding of how modern LLM inference factories operate at scale. For full course browse https://uplatz.com/online-courses #LLM #GenerativeAI #AIInfrastructure #MLOps #LLMOps #ArtificialIntelligence #vLLM #GPUComputing #AIEngineering #Uplatz ---------------------------------------------- 🌐 Welcome to Uplatz – Your Gateway to Career Transformation! To access full courses or training bundles: 🌐 https://uplatz.com 📧 support@uplatz.com 🎓 About Uplatz Uplatz is a global leader in online IT and professional training, offering comprehensive courses in AI, machine learning, data science, cloud computing, cybersecurity, and enterprise technologies such as SAP, Oracle, Salesforce, and ServiceNow. With expert-led programs and real-world learning paths, Uplatz empowers learners and organizations across 190+ countries to build future-ready skills and thrive in the digital era. 📘 Explore Uplatz Course Portfolio Learn the most in-demand and emerging technologies with Uplatz: ✅ AI & Machine Learning – Agentic AI, LLMs, LangChain, Deep Learning, MLOps, LLMOps ✅ Cloud & DevOps – AWS, Azure, GCP, Docker, Kubernetes, Terraform, CI/CD ✅ Data & Analytics – Data Science, Data Engineering, Power BI, Tableau, Big Data (Spark, Kafka) ✅ Programming & Frameworks – Python, FastAPI, Django, Java, JavaScript, SQL ✅ Cybersecurity & Blockchain – Ethical Hacking, Cloud Security, Zero Trust, Blockchain & Web3 ✅ IoT & Embedded Systems – IoT Platforms, Edge Computing, Embedded C, Microcontrollers ✅ ERP & CRM – SAP (all modules), Salesforce, Oracle ERP, Microsoft Dynamics ✅ Web & App Development – Full-Stack Development, React, Angular, Node.js, Flutter 🎓 Master cutting-edge skills. Build your tech career with Uplatz. 🌐 Learn more: https://uplatz.com 🎯 Why Choose Uplatz ✔️ Job-focused, project-based learning ✔️ Globally recognized certifications ✔️ Lifetime access & affordable pricing ✔️ Career guidance and mentorship 🔔 Subscribe for weekly tech tutorials, demos, and success stories. 📲 Follow us on LinkedIn, Instagram, Twitter, and Facebook. #Uplatz #Tech #Technology #MachineLearning #CloudComputing #Learning