1. Maven - Fine-Tuning Open-Weight LLMs for Engineers
   

   Maven - Fine-Tuning Open-Weight LLMs for Engineers

   S A N posted a topic in Courses For Developers

   Maven - Fine-Tuning Open-Weight LLMs for Engineers File Name: Maven - Fine-Tuning Open-Weight LLMs for Engineers Content Source: https://maven.com/dvgodoy/finetuning Genre / Category: Courses for Developers Language: ENGLISH Original Price: $597 What you'll learn: This practical course on fine-tuning LLM with open weights will help engineers master the full cycle of fine-tuning large language models: from task selection and dataset preparation to LoRA/QLoRA tuning , quality assessment, and application of the results in real-world projects. About the courseMost materials on fine-tuning large language models are either overly theoretical or designed for large teams, large datasets, and expensive computing infrastructure. In practice, a more accessible and reproducible approach suffices for many engineering problems. This course focuses on efficient retraining of open-weight LLMs using modern parameter-efficient learning methods: LoRA and QLoRA . You'll learn how to select a suitable task, prepare a specialized dataset, train an adapter on available hardware, and objectively evaluate the results. The main goal of the course is to understand when fine-tuning is truly justified: it helps improve response quality, reduce costs, decrease latency, achieve a stable output format, or reduce dependence on commercial APIs. The training takes the form of a practical workshop with a small group of participants. This allows participants to discuss their own projects, receive feedback from the instructor, and explore real-world limitations related to data, models, and computing resources. What you will learnSelect tasks for further LLM trainingDetermine when to use fine-tuning and when Prompt Engineering, RAG, or input data enhancement is sufficient. Evaluate tasks based on repeatability, result requirements, data availability, verification complexity, and implementation cost. Translate broad business problems into specific scenarios suitable for model training. For example, instead of the general task of “answering customer questions,” formulate a more precise task: classifying requests by category, urgency, or other parameters. Use a system of criteria to make an informed decision about the feasibility of fine-tuning LLM. Prepare datasets for fine-tuningCreate specialized data sets for specific tasks. Bring training examples to a unified structure Instruction / Input / Output. Identify ambiguous, contradictory and uninformative examples. Clean, normalize, and structure data before training. Improve the quality of the training sample without having to collect huge amounts of data. Retrain models using LoRA and QLoRALoad base language models with open weights in a Jupyter Notebook environment. Understand the principles of parameter-efficient feasibility training (PEFT). Configure key LoRA and QLoRA parameters: Target Modules — target modules of the model. Rank — adapter rank. Learning Rate — learning speed. Batch Size —batch size. Sequence Length — the length of the sequence. Conduct a full training cycle using available computing resources. Save trained adapters for further testing and use. Evaluate the quality of the retrained modelCompare the results of the base and retrained LLM. Create separate test datasets that were not used in training. Assess the accuracy of task completion, adherence to the response format, and the stability of the model's behavior. Analyze typical mistakes and find patterns in unsuccessful results. Determine whether retraining has produced real improvements over the original model. Diagnose problems and improve resultsFind the reasons for unsatisfactory fine-tuning results. Identify issues with data labeling, problem definition, and test sample quality. Understand when to change hyperparameters, choose a different model, or revise the dataset structure. Recognize common mistakes when training on small datasets. Decide on your next steps: continue learning, collect additional samples, use RAG, or abandon fine-tuning. Apply fine-tuning to your own projectsUse a ready-made Jupyter Notebook for data preparation, training, inference, and model evaluation. Adapt the fine-tuning process to your own tasks and data sets. Apply pre-trained models for classification, data mining, and query routing. Configure the generation of structured responses and adaptation of text style. Design reproducible experiments to test different models and approaches. Assess potential resource savings and reduced dependence on proprietary LLMs. What will you get as a result?By the end of the course, you will have practical artifacts that you can use as a basis for your own experiments and work projects. A prepared dataset is a cleaned and structured data set for a specific task. The trained LoRA/QLoRA adapter is the result of practical retraining of a language model with open weights. Comparative evaluation of models - test results before and after fine-tuning. A reusable Jupyter Notebook is a ready-to-use workflow for data preparation, training, inference, and evaluation. The Decision Checklist is a system of criteria that helps determine when retraining is justified and when it is more effective to use prompting, RAG, or improving the initial data. Who is this course for?This course is suitable for developers, ML engineers, AI engineers, data scientists, and technical managers who want to gain practical experience in retraining large language models without the need for expensive infrastructure. It is especially useful for those who already work with LLM via the API but want to move to models with open weights, gain more control over their behavior, and learn how to adapt models to specialized tasks. For developers who implement LLM in products and internal tools. For ML and AI engineers who need a practical fine-tuning pipeline. Data scientists working with classification, data extraction, and structured responses. Technical managers who want to evaluate the feasibility of additional LLM training for business needs. What tasks can be solved after the course?Classification of text messages, documents and messages. Extracting structured information from unstructured text. Routing requests by category, priority, or responsible teams. Generating responses in a given format. Adapting the model's style to the internal requirements of the product or company. Comparing fine-tuning, RAG, and prompt engineering to select the optimal approach. PrerequisitesTo successfully complete the course, it is recommended to have basic Python programming experience and a general understanding of the LLM. In-depth research background in machine learning is not required: the emphasis is on practical processes, engineering solutions, and reproducible experiments. Final learning outcomeAfter the course, you will be able to independently conduct a full cycle of fine-tuning LLM: select a suitable task, prepare a dataset, train a LoRA/QLoRA adapter, compare the quality of the baseline and fine-tuned models, and make an informed decision about applying the results to a real project. File Information Submitter S A N Submitted 10/27/2026 Category Paid Coding Courses Sale page https://maven.com/dvgodoy/finetuning View File

   • 4 hours ago4 hr
   • 0 replies
2. Agentic Engineering Course
   

   Agentic Engineering Course

   S A N posted a topic in Courses For Developers

   Agentic Engineering Course File Name: Agentic Engineering Course Content Source: https://jsmastery.com/course/agentic-engineering-course Genre / Category: Courses for Developers Language: ENGLISH Original Price: ₹15,981 What you'll learn: using artificial intelligence. You'll learn how to use AI not just for code generation, but as a full-fledged engineering tool for creating high-quality, scalable, and maintainable applications. Instead of chaotic vibe coding and endless prompts, the course offers a systematic approach: context management, architecture planning, quality control, and verification of AI results. What is the course about?The training is based on JSM Skills Workflow , a practical AI development system created through 12 months of experimentation with artificial intelligence tools. The methodology combines the automation of routine processes with engineering control, helping to effectively work on projects of varying complexity. The course demonstrates how to transform AI from a random assistant into a controlled participant in the development process: from task definition and context preparation to implementation, review, and release of finished functionality. What you will learnManage the AI context. Determine what information to pass to the model and what to exclude to obtain accurate results without overloading the context window. Monitor the quality of code generation. Identify errors, inconsistencies, and deviations from the original plan early in development. Correct AI performance. Return the model to the correct solution without having to start the process over again. Plan the application architecture. Create a stable project structure that maintains integrity as new features are added. Work with the JSM Skills Workflow. Use ready-made commands and tools to automate the full development cycle of individual features. Automate development. Transform a brief task description into a ready-made implementation, taking into account the existing codebase and project requirements. Review code using multiple AI models. Organize independent reviews using a second model to improve the quality and reliability of solutions. Make engineering decisions. Understand when AI recommendations can be trusted, when intervention is necessary, and which decisions should not be delegated to the model. Optimize costs. Reduce the costs of developing, maintaining, and scaling applications through a well-designed AI workflow. How JSM Skills Workflow WorksThe development process is organized as a repeatable cycle for each new feature. This approach makes working with artificial intelligence more predictable and helps maintain control over the architecture, code quality, and deadlines. Main stages of workflowProblem statement. You describe the required functionality with a single command, for example /develop build the checkout page. Context preparation. The system analyzes the existing code and project plan, then generates detailed instructions for the AI. Coordination of solutions. You review the proposed approach, make adjustments, and confirm further work. Implementation. AI writes code taking into account the architecture, conventions, and existing project templates. Independent verification. Another AI model analyzes the results and helps identify potential issues. Task completion. Once verified, the functionality is ready for release, and the process can be repeated for the next task. Practical focus of the courseParticular attention is paid to situations where the AI begins to deviate from requirements, makes unfounded architectural decisions, or creates code that works now but causes problems as the project progresses. Using practical examples, the author demonstrates the differences between standard vibe coding and system-level AI development. He also compares the costs of creating, operating, and scaling the same application using different approaches to AI. Who is this course suitable for?This course is designed for developers who already know how to program and use artificial intelligence in their daily work, but want to make this process more predictable, efficient, and professional. For frontend, backend, and full-stack developers who want to integrate AI into their workflow. For engineers tired of chaotic vibe coding and unstable code generation results. For developers who want to better manage the architecture and quality of projects created with AI. Team leads and technical specialists who need to understand how to safely use AI in team development. Knowledge of TypeScript or experience as a Senior Developer is not required, but basic programming skills are a must. What will you get as a result?After completing this course, you'll be able to not only generate working code from a neural network, but also manage the entire engineering development process with AI: from problem setting and architecture selection to quality assurance and final product release. The main goal of this course is to teach you how to use agent engineering as a professional approach to software development, where artificial intelligence accelerates work, but key engineering decisions remain under your control. File Information Submitter S A N Submitted 10/27/2026 Category Paid Coding Courses Sale page https://jsmastery.com/course/agentic-engineering-course View File

   • 4 hours ago4 hr
   • 0 replies
3. Tle Prime
   

   Tle Prime

   giganigga posted a topic in Request course

   TLE launched a new course TLE PRIME if possible please upload that!

   • 23 hours ago23 hr
   • 0 replies
   • • .net
4. Code with mosh complete python mastery
   

   Code with mosh complete python mastery

   princesp3 replied to S A N's topic in Courses For Free

   folders are all empty on Mega

   • Saturday at 06:51 PM2 days
   • 1 reply
   • • python
     • complete python
     • python for beginners
     • english
     • +2 more

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       • code with mosh complete python mastery
       • code with mosh
5. Arcanum Sec - Hackbots (using Agent Frameworks)
   

   Arcanum Sec - Hackbots (using Agent Frameworks)

   S A N posted a topic in Courses For Developers

   Arcanum Sec - Hackbots (using Agent Frameworks) File Name: Arcanum Sec - Hackbots (using Agent Frameworks) Content Source: https://arcanum-sec.com/training/hackbots/ Genre / Category: Courses for Developers Language: ENGLISH Original Price: $1,000 What you'll learn: Build, control, and ship real hack bots.Decide where AI adds real leverage in offensive security and where it does not Design hack bots with human-in-the-loop as a core requirement Drive Agent Frameworks as the reasoning and execution engine for offensive workflows Architect single-purpose bots and multi-stage workflows using agents, skills, and commands Engineer context and prompt patterns for recon, analysis, and triage tasks Execute local tools through Agent Frameworks and normalize their output Feed structured tool results back into reasoning loops Build a recon bot for asset discovery and prioritization Build endpoint, API, and client-side JavaScript analysis bots Build bug triage, write-up, and content discovery workflow bots Build mutation bots for XSS and SSRF Spot failure modes like hallucinations and over-trust, then operationalize bots in real engagements File Information Submitter S A N Submitted 10/19/2026 Category Paid Coding Courses Sale page https://arcanum-sec.com/training/hackbots/ View File

   • October 19Oct 19
   • 0 replies

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