1. KK Create - AI for Content Creation
   

   KK Create - AI for Content Creation

   Liton replied to S A N's topic in Courses For Free

   how to download?

   • 10 hours ago10 hr
   • 1 reply
2. KK Create - AI for Content Creation
   

   KK Create - AI for Content Creation

   S A N posted a topic in Courses For Free

   KK Create - AI for Content Creation File Name: KK Create - AI for Content Creation Content Source: https://learn.kkcreate.in/web/checkout/6a251a9700cd7fd96d9d7193 Genre / Category: Premium courses Password: Click Here to Reveal Password Original Price: ₹999 Language: HINDI For Paid User Without URL Shortener: Download : GO TO SINGLE CLICK DOWNLOAD PAGE ABOUT THE COURSE: Want to create better content using AI?Learn directly from creators who actually use AI in their real workflow. ⚡ Generate strong ideas when your mind goes blank ✍️ Write better scripts, hooks & posts (without overthinking) 🔎 Do faster research without getting lost for hours 🧠 Use AI in a way that doesn’t sound robotic ⚙️ Build a simple system that saves time on every video File Information Submitter S A N Submitted 08/07/2026 Category Courses Sale page https://learn.kkcreate.in/web/checkout/6a251a9700cd7fd96d9d7193 View File

   • 11 hours ago11 hr
   • 1 reply
3. Piyush Garg - Full stack generative and Agentic AI with python
   

   Piyush Garg - Full stack generative and Agentic AI with python

   S A N posted a topic in Courses For Free

   Piyush Garg - Full stack generative and Agentic AI with python File Name: Piyush Garg - Full stack generative and Agentic AI with python Content Source: https://www.udemy.com/course/full-stack-ai-with-python/ Genre / Category: Premium courses Password: Click Here to Reveal Password Original Price: $49 Language: ENGLISH For Paid User Without URL Shortener: Download : GO TO SINGLE CLICK DOWNLOAD PAGE ABOUT THE COURSE: Welcome to the Complete AI & LLM Engineering Bootcamp – your one-stop course to learn Python, Git, Docker, Pydantic, LLMs, Agents, RAG, LangChain, LangGraph, and Multi-Modal AI from the ground up. This is not just another theory course. By the end, you will be able to code, deploy, and scale real-world AI applications that use the same techniques powering ChatGPT, Gemini, and Claude. What You’ll Learn Foundations Python programming from scratch — syntax, data types, OOP, and advanced features. Git & GitHub essentials — branching, merging, collaboration, and professional workflows. Docker — containerization, images, volumes, and deploying applications like a pro. Pydantic — type-safe, structured data handling for modern Python apps. AI Fundamentals What are LLMs and how GPT works under the hood. Tokenization, embeddings, attention, and transformers explained simply. Understanding multi-head attention, positional encodings, and the "Attention is All You Need" paper. Prompt Engineering Master prompting strategies: zero-shot, one-shot, few-shot, chain-of-thought, persona-based prompts. Using Alpaca, ChatML, and LLaMA-2 formats. Designing prompts for structured outputs with Pydantic. Running & Using LLMs Setting up OpenAI & Gemini APIs with Python. Running models locally with Ollama + Docker. Using Hugging Face models and INSTRUCT-tuned models. Connecting LLMs to FastAPI endpoints. Agents & RAG Systems Build your first AI Agent from scratch. CLI-based coding agents with Claude. The complete RAG pipeline — indexing, retrieval, and answering. LangChain: document loaders, splitters, retrievers, and vector stores. Advanced RAG with Redis/Valkey Queues for async processing. Scaling RAG with workers and FastAPI. LangGraph & Memory Introduction to LangGraph — state, nodes, edges, and graph-based AI. Adding checkpointing with MongoDB. Memory systems: short-term, long-term, episodic, semantic memory. Implementing memory layers with Mem0 and Vector DB. Graph memory with Neo4j and Cypher queries. Conversational & Multi-Modal AI Build voice-based conversational agents. Integrate speech-to-text (STT) and text-to-speech (TTS). Code your own AI voice assistant for coding (Cursor IDE clone). Multi-modal LLMs: process images and text together. Model Context Protocol (MCP) What is MCP and why it matters for AI apps. MCP transports: STDIO and SSE. Coding an MCP server with Python. Real-World Projects You’ll Build Tokenizer from scratch. Local Ollama + FastAPI AI app. Python CLI-based coding assistant. Document RAG pipeline with LangChain & Vector DB. Queue-based scalable RAG system with Redis & FastAPI. AI conversational voice agent (STT + GPT + TTS). Graph memory agent with Neo4j. MCP-powered AI server. Who Is This Course For? Beginners who want a complete start-to-finish course on Python + AI. Developers who want to build real-world AI apps using LLMs, RAG, and LangChain. Data Engineers/Backend Developers looking to integrate AI into existing stacks. Students & Professionals aiming to upskill in modern AI engineering. Why Take This Course? This course combines theory, coding, and deployment in one place. You’ll start from the basics of Python and Git, and by the end, you’ll be coding cutting-edge AI applications with LangChain, LangGraph, Ollama, Hugging Face, and more. File Information Submitter S A N Submitted 08/07/2026 Category Courses Sale page https://www.udemy.com/course/full-stack-ai-with-python/ View File

   • 16 hours ago16 hr
   • 0 replies
4. Krish Naik - Complete Agentic AI Bootcamp With LangGraph and Langchain
   

   Krish Naik - Complete Agentic AI Bootcamp With LangGraph and Langchain

   S A N posted a topic in Courses For Free

   Krish Naik - Complete Agentic AI Bootcamp With LangGraph and Langchain File Name: Krish Naik - Complete Agentic AI Bootcamp With LangGraph and Langchain Content Source: https://www.udemy.com/course/complete-agentic-ai-bootcamp-with-langgraph-and-langchain/ Genre / Category: Premium courses Password: Click Here to Reveal Password Original Price: $49 Language: ENGLISH For Paid User Without URL Shortener: Download : GO TO SINGLE CLICK DOWNLOAD PAGE ABOUT THE COURSE: Are you excited about the future of AI where intelligent agents can think, act, and collaborate to solve complex tasks autonomously? Welcome to the Complete Agentic AI Bootcamp with LangGraph and LangChain — your one-stop course to master the art of building agentic AI applications from scratch! This course is designed to teach you everything you need to know about Agentic AI, LangGraph, and LangChain — two of the most powerful frameworks for building intelligent AI agents and multi-agent systems. You will start by understanding the fundamentals of Agentic AI — how it differs from traditional AI models, the key components of agents (memory, tools, decision-making), and real-world use cases. We will then dive deep into LangGraph, a cutting-edge framework that helps you design complex agent workflows using graphs, events, and state transitions. You’ll also learn how to combine LangChain's power with LangGraph to build production-ready agent applications. Throughout the course, you will build real-world projects step-by-step, including: Creating single intelligent agents with memory and tool-usage capabilities. Designing multi-agent collaboration systems with message passing and shared goals. Implementing autonomous research assistants, task automation bots, and retrieval-augmented generation (RAG) agents. You will not just learn theory — you will build and deploy multiple end-to-end agentic applications, gaining real-world experience in constructing powerful AI systems. By the end of this course, you will have the skills and confidence to create your own AI agents and deploy complex agentic applications for various domains like search, research, task planning, customer support, and beyond. What You Will Learn: Core concepts behind Agentic AI and how intelligent agents operate. Hands-on mastery of LangGraph and LangChain for building agent systems. Building autonomous, event-driven AI workflows with memory, reasoning, and tools. Deploying and optimizing single-agent and multi-agent applications. Real-world project experience with RAG agents, auto-research agents, and more. Why Take This Course? Hands-on, Project-Based Learning: Build actual AI agent applications, not just toy examples. Complete and Beginner-Friendly: Designed to take you from beginner to advanced agent builder. Real-World Skills: Learn techniques that companies are starting to use for next-generation AI products. Cutting-Edge Technologies: Master the latest innovations in AI agent orchestration with LangGraph and LangChain. File Information Submitter S A N Submitted 08/07/2026 Category Courses Sale page https://www.udemy.com/course/complete-agentic-ai-bootcamp-with-langgraph-and-langchain/ View File

   • 17 hours ago17 hr
   • 0 replies
5. Vizuara AI Labs - Master LLM Inference Engineering by Dr. Sreedath Panat
   

   Vizuara AI Labs - Master LLM Inference Engineering by Dr. Sreedath Panat

   S A N posted a topic in Courses For Developers

   Vizuara AI Labs - Master LLM Inference Engineering by Dr. Sreedath Panat File Name: Vizuara AI Labs - Master LLM Inference Engineering by Dr. Sreedath Panat Content Source: https://inference.vizuara.ai/ Genre / Category: Coding Courses Language: ENGLISH Original Price: ₹45,000 ABOUT THE COURSE: "Professional LLM Inference" — a practical four-week intensive course for those who want to understand how to launch, optimize, and scale large language models in real production systems. The course covers the entire journey: from the fundamental principles of inference and GPU memory management to distributed deployment, edge inference, quantization, profiling, and building high-performance AI services capable of handling large volumes of requests with low latency. About the CourseThe course is dedicated to professional inference of large language models: how LLMs function post-training, how they process user requests, the reasons for latency, how to improve server throughput, and what engineering solutions are used in modern AI companies. The program combines theory, lab work, live demonstrations, and hands-on practice on real equipment. Sessions are conducted by Dr. Raj Dandekar, MIT PhD, as well as engineers and specialists from Anthropic, NVIDIA, Apple, Microsoft, Amazon, AnyScale, and other tech companies. What You Will LearnUnderstand the architecture of modern LLM inference systems and the key stages of request processing. Optimize the performance of large language models in terms of latency, throughput, and GPU utilization. Work with GPU memory, KV cache, attention mechanisms, and hardware platform limitations. Apply model quantization for speeding up inference and reducing resource consumption. Use popular frameworks and tools: vLLM, SGLang, FlashAttention, TensorRT-LLM, Ray Serve, and Megatron-LM. Deploy LLMs on a local computer, server, Raspberry Pi 4, Android device, and NVIDIA Jetson Orin Nano. Design production-ready AI services with scalable architecture. Prepare for technical interviews that test understanding of LLM inference system design. Program StructureThe course consists of two independent modules. The first module focuses on inference principles and optimization, the second on industrial deployment and building applied AI systems. Module 1. LLM Inference Architecture and OptimizationIn the first part, you will learn how the inference of large language models is structured, what bottlenecks occur during request processing, and how modern frameworks help increase the efficiency of models. Basics of LLM inference and the request lifecycle. Prefill, decode, batching, and continuous batching. KV cache and memory usage optimization. Attention mechanisms and FlashAttention. Model quantization and trade-offs between speed, quality, and memory consumption. Performance profiling and identifying bottleneck components. Practical work with vLLM, SGLang, TensorRT-LLM, and other tools. Module 2. Production Deployment and Edge InferenceIn the second part of the course, you will transition from optimizing individual models to creating full-fledged AI services that can be used in real products and infrastructure. Industrial deployment of LLM services. Distributed computing and inference scaling. Ray Serve and approaches to high-load request handling. Edge inference on compact devices. Running models on Raspberry Pi 4, Android, and NVIDIA Jetson Orin Nano. Comparison of hardware platforms and performance analysis. Building reliable production-ready AI applications. Practice and Lab WorkA large part of the course is built around practical assignments. Each study day is accompanied by lab work in Google Colab, visual materials, demonstrations, and analysis of engineering solutions. You will not only study theory but also run models, measure their performance, analyze resource usage, compare different configurations, and apply optimization methods in practice. Equipment and PlatformsLocal computer for basic model launching and testing. Google Colab for lab work and experiments. Raspberry Pi 4 for studying edge inference limitations. Android device for running LLM on a mobile platform. NVIDIA Jetson Orin Nano for practice with a compact GPU accelerator. Final ProjectsDuring the course, you will implement two complete projects that will help reinforce your skills in developing and optimizing LLM inference systems. Project 1. High-Performance LLM Inference ServerYou will go through the entire process from the model's raw weights to an optimized inference service. Along the way, you will set up model launching, conduct profiling, identify bottlenecks, apply acceleration techniques, and prepare the system for handling real requests. Loading and preparing the model. Setting up the inference server. Optimizing latency and throughput. Analyzing the performance of each component. Preparing the service for production scenarios. Project 2. AI Assistant for WhatsAppThe second project is dedicated to creating an intelligent AI assistant for WhatsApp. It will process dialogues, generate responses, and improve its behavior using reinforcement learning based on user interactions. Integrating LLM with a chat interface. Developing the logic for the AI assistant. Using dialogues to improve response quality. Applying reinforcement learning approaches in an applied scenario. Tools and TechnologiesDuring the training, you will become familiar with a modern tool stack used to accelerate and scale the inference of large language models. vLLM — high-performance inference engine for LLM servicing. SGLang — framework for efficient execution of LLM applications. FlashAttention — optimized attention mechanisms for accelerating transformer work. TensorRT-LLM — NVIDIA tools for optimizing large model inference. Ray Serve — solution for scalable AI model serving. Megatron-LM — framework for working with large language models and distributed computing. Google Colab — environment for lab work and experiments. Who the Course is ForML engineers looking to delve deeper into LLM optimization and deployment. Backend and infrastructure engineers working with AI services and high-load systems. Data scientists wishing to transition from model experiments to production development. AI developers creating chatbots, assistants, and LLM applications. Technical specialists preparing for interviews with AI companies. Founders and technical leaders who need to understand the cost, limitations, and architecture of LLM inference. What Results You Will AchieveAfter completing the course, you will be able to design and implement LLM inference systems considering requirements for speed, cost, scalability, and reliability. Understanding the internal workings of LLMs during inference. Skills to optimize models for different hardware platforms. Experience with industry-standard tools for LLM serving. Ability to conduct profiling and make engineering decisions based on metrics. Two practical projects for your portfolio. More confident preparation for technical interviews on LLM infrastructure. Why Study LLM InferenceThe development of large language models is not just about training neural networks. In real products, the main cost and complexity often relate to inference: response speed, GPU load, scaling, memory consumption, and service stability. Specialists who understand how to effectively service LLMs in production are in demand in teams creating AI assistants, corporate chatbots, agent systems, search products, developer tools, and other applications based on generative artificial intelligence. File Information Submitter S A N Submitted 08/07/2026 Category Paid Coding Courses Sale page https://inference.vizuara.ai/ View File

   • 18 hours ago18 hr
   • 0 replies
   • • 
     • 1

See all Subscription Plans

• 5,770 Total Members
• 620 Most Online
• 

  yoohvffdxxv Newest Member · 7 hours ago7 hr