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68.955 resultaten

Eenvoudig Duits + 1 jaar onbeperkt leren cadeau

In deze cursus leer je de basis van de Duitse taal op een overzichtelijke en praktische manier, speciaal afgestemd op Nederlandstaligen. Je krijgt duidelijke uitleg, herkenbare voorbeelden en... Je bekijkt nu de online cursus ‘Eenvoudig Duits’ van Hobp. Goed om te weten: als je deze online cursus boekt, dan krijg je van Hobp tevens 1 jaar onbeperkt leren cadeau, via het leerplatform van Hobp. Dat betekent dat je – naast deze cursus – een jaar lang alle andere online cursussen kosteloos kunt volgen. Het leerplatform van Hobp is op elk moment beschikbaar via telefoon, tablet of laptop én na afronding van een cursus ontvang je een persoonlijk certificaat. Wil je meer weten over Hobp en welke (leer)mogelijkheden wij bieden voor jou of je bedrijf? Scroll dan naar beneden naar onze 'opleidersinformatie' of bekijk ons leerplatform via https://leren.hobp.nl. Ken je dat gevoel dat Duits bijna logisch lijkt, maar dat je toch steeds nét de verkeerde woorden of zinsvolgorde gebruikt? Veel Duitse woorden lijken sterk op het Nederlands, maar betekenen iets anders, en juist dat zorgt voor verwarring. In deze online cursus leer je stap voor stap hoe Duits echt werkt. Door inzicht te krijgen in grammatica, woordgebruik en zinsbouw wordt Duits een stuk overzichtelijker en gebruiksvriendelijker. Deze training (of dit cursuspakket) maakt deel uit van het duurzaamheidsplatform van Hobp. Dit is een platform dat medewerkers helpt om gezond, gelukkig en bekwaam aan het werk te blijven én bedrijven ondersteunt met een toegankelijke oplossing voor duurzame inzetbaarheid. Het platform biedt o.a. persoonlijk inzicht met de scan (DIX) van TNO, stimuleert bewustwording en bevat 600+ online tools en interventies om duurzame inzetbaarheid te vergroten. Meer weten? Ga dan naar www.hobp.nl. De online cursus Eenvoudig Duits ondersteunt bij het ontwikkelen van basisvaardigheden in de Duitse taal. De cursus is gericht op iedereen die Duits beter wil begrijpen en gebruiken, zowel privé als in werksituaties. De cursus Eenvoudig Duits bestaat uit: E-learning, Oefenvragen, Praktijkvoorbeelden en een Eindtoets. ⊛ Inzicht in veelvoorkomende Duitse grammaticale structuren ⊛ Herkennen en vermijden van typische Nederlandse fouten in het Duits ⊛ Correct gebruik van lidwoorden, naamvallen en werkwoordsvormen ⊛ Beter begrip van Duitse zinsopbouw en woordvolgorde ⊛ Meer zelfvertrouwen in het lezen, begrijpen en gebruiken van Duits
€225
E-Learning
5 uren

Revit Dynamo

Nijmegen vr 16 okt. 2026
De cursus Revit Dynamo leert u hoe u op eenvoudige wijze nieuwe functionaliteit toevoegt aan Revit. Dynamo biedt een zogenaamde "No Code, low code" ontwikkelomgeving. Door blokken in een stroomschema te schuiven bouwt u een programma. Dit is bij uitstek geschikt voor parametrische modellen of programma's voor kleine projecten. U kunt in een mum van tijd kleine aanpassingen aan Revit maken. Iedere verandering van uw Dynamo schema resulteert direct in een ander resultaat in Revit, zonder dat u Revit opnieuw start. Als u nieuwe functionaliteit wilt toevoegen voor meerdere gebruikers van Revit kunt u doorgroeien naar de cursus Revit programmeren.  U leert gevorderde en geavanceerde technieken. De volgende onderwerpen komen aan bod: Algemene werking Dynamo Modeleren in Revit met Dynamo Parametrisch modelleren Tekenwerk automatiseren Aanpassen Datamodel Parameteters lezen en schrijven Excel koppelen Modelstudie in Dynamo: massamodelleren Introductie programmeren
€1.490
Klassikaal
max 13
HBO

Fusion Basis

Nijmegen ma 15 jun. 2026 en 2 andere data
Fusion is de fusie tussen de mooiste computer ondersteunde technieken (CA-technieken). Niet alleen modelleren (CAD), maar ook sterkteberekeningen (CAE), machine aansturingen (CAM en 3D printen), elektronisch ontwerpen (ECAD) en meer. Het is werkelijk een feest om met Fusion te mogen werken.  Deze cursus is bedoeld voor startende bedrijven, studenten of uitvinders met een goed idee die deze willen uitwerken tot een werkend prototype. Fusion is een onvervalst CAD CAM ontwikkelomgeving. De cursus is compact, praktisch en eindigt met een fysiek product dat tijdens de cursus ontworpen uitgewerkt en gefreesd is. Ervaren docenten en Nederlandstalig lesmateriaal. U leert de belangrijkste tekentechnieken van Fusion met de volgende cursusonderwerpen: User interface Modelleren Schetsen (eng: Sketch) Constructie-elementen (eng: features) Tekeningen afleiden van een model Samenstellingen Presentaties en fotorealistische plaatjes Samenwerken met meer man in een project Aansturen 3D-printer en freesmachine Vrijvorm modelleren. U kunt na de cursus: - Zelfstandig tekenen met Fusion. - Complexe 3D-varianten maken. - 2D-werktekeningen / CAM files / 3D print files aanmaken.
€990
Klassikaal
max 14
Mbo

Micro Frontends with React

Amsterdam ma 13 jul. 2026 en 9 andere data
The course Micro Frontends with React from SpiralTrain teaches you how to design modern, modular frontend solutions with React. Introduction The course Micro Frontends with React begins with an overview of micro frontend concepts and their advantages over monolithic user interfaces. Design principles are discussed, with attention to scalability and independent deployments. React Recap Next participants review the fundamentals of React, including JSX, components, props and state, React Hooks, and event handling. Core features such as conditional rendering, routing with React Router, and the Context API are also refreshed. Architecture This module covers architectural patterns for React micro frontends, including component composition, container and presenter patterns, shared versus isolated state, routing across applications, and the role of a shell architecture. Module Federation Participants learn how to set up Webpack Module Federation for React projects. Topics include host and remote applications, dynamic imports, shared dependencies, version handling, and best practices for runtime integration. Integration Integration strategies are then explored, with focus on UI composition, shared navigation, cross-application routing, authentication and authorization handling, and service integration within a micro frontend orchestration. State Management This module examines state management challenges and solutions. Topics include local state, prop drilling, Context API usage, and Redux with Redux Toolkit for sharing and synchronizing state across micro frontends. Deployment Deployment aspects include CI/CD pipelines, independent releases, environment configuration, and containerization with Docker. Hosting with Kubernetes, cloud providers, rollback strategies, and monitoring options are also discussed. Testing Essential testing practices are introduced, including unit and integration tests with Jest and React Testing Library, end-to-end tests with Cypress, contract testing, automation, and accessibility checks. Advanced Topics The course concludes with advanced subjects such as authentication and authorization patterns, error boundary handling, performance optimization, caching strategies, and future trends. A hands-on project brings all concepts together. Audience Course Micro Frontends with React This course is intended for React developers, frontend specialists, and solution architects who want to master building scalable applications based on a micro frontend architecture. Prerequisites Course Micro Frontends with React Participants should be comfortable with JavaScript, TypeScript, and React development. Experience with component-based design, build pipelines and state management is beneficial. Realization Training Micro Frontends with React The training consists of interactive lectures combined with practical labs under the guidance of an experienced trainer. Emphasis is placed on hands-on exercises and applying the concepts in realistic project scenarios. Micro Frontends with React Certificate After completing the course, participants receive a certificate of participation in Micro Frontends with React. Modules Module 1 : Introduction Micro Frontends Overview Why React Micro Frontends Monolith vs Distributed UI Benefits and Drawbacks Key Use Cases Design Principles Team Scalability Deployment Independence Real-World Examples Future Outlook Module 2 : React Recap React Fundamentals JSX Syntax Components Basics Props and State React Hooks Intro Event Handling Conditional Rendering List Rendering React Router Basics Context API Module 3 : Architecture Architecture Patterns Component Composition Container and Presenters Shared vs Isolated State Routing Across Apps Cross-App Communication Lazy Loading Routes Micro Frontend Shell Error Boundaries Resilience Patterns Module 4 : Module Federation Webpack Federation Setup Host and Remote Apps Dynamic Imports Exposing Components Shared Dependencies Version Handling Runtime Integration Error Handling Configuration Files Best Practices Module 5 : Integration UI Composition Shared Navigation Authentication Integration Authorization Handling Service Integration Shell Architecture Cross-App Routing Micro Frontend Orchestration Performance Monitoring Testing Integration Module 6 : State Management State Management Basics Local Component State Prop Drilling Issues Context API Usage Redux Fundamentals Redux Toolkit Cross-App State Sharing State Synchronization Async State Handling Debugging Tools Module 7 : Deployment Deployment Strategies CI/CD Pipelines Independent Deployments Version Management Docker with React Kubernetes Deployments Cloud Hosting Options Environment Configurations Rollback Strategies Monitoring and Alerts Module 8 : Testing Unit Testing React Jest Framework React Testing Library Integration Testing End-to-End Testing Cypress Framework Contract Testing Test Automation Performance Testing Accessibility Testing Module 9 : Advanced Topics Security Concerns Authentication Patterns Authorization Patterns Performance Optimization Error Boundary Handling Caching Strategies Accessibility Concerns Future Trends Case Studies Hands-On Project
€2.299
Klassikaal
max 12
3 dagen

Micro Frontends with Vue

Amsterdam wo 24 jun. 2026 en 9 andere data
The course Micro Frontends with Vue from SpiralTrain shows you how to build flexible and scalable frontend architectures using Vue. Introduction The course Micro Frontends with Vue begins with an introduction to the concept of micro frontends and their differences from monolithic applications. Use cases, and core principles are explained with examples from industry practice. Vue Recap Next, participants review essential Vue concepts including components, directives, reactivity, props and events, routing with Vue Router, and the Composition API. This recap ensures a solid basis for building micro frontends. Architecture This module explores architecture patterns and design approaches for Vue micro frontends. Topics include component composition, event bus communication, routing strategies, shell applications, lazy loading, and error boundaries. Module Federation Participants learn how to use Webpack Module Federation with Vue. Covered are host and remote apps, dynamic imports, shared libraries, version handling, runtime integration, and best practices for setup and error handling. Integration Integration is discussed with focus on UI composition, shared navigation, cross-app routing, and service integration. Special attention is given to authentication, authorization, and performance monitoring in real-world scenarios. State Management This part addresses state management in Vue. Subjects include local state, props and events, Vuex with modules, and newer alternatives like Pinia. Cross-app state sharing and debugging tools are also reviewed. Deployment Deployment strategies cover CI/CD pipelines, independent releases, and environment configurations. The module also includes containerization with Docker, deployment on Kubernetes, cloud hosting options, and rollback planning. Testing Participants learn about testing micro frontends with Vue. Unit testing with Vue Test Utils and Jest, integration testing, and end-to-end testing with Cypress are explained, alongside automation and accessibility checks. Advanced Topics The course concludes with advanced subjects such as security, authentication and authorization patterns, performance optimization, error handling, and caching strategies. Future trends are discussed, and a hands-on project wraps up the training. Audience Course Micro Frontends with Vue This course is designed for Vue.js developers, UI engineers, and technical leads who want to explore how micro frontend patterns can be applied to modern web applications. Prerequisites Course Micro Frontends with Vue Participants should have solid knowledge of JavaScript and Vue development. Understanding of modular design, web components and tooling like Vue CLI or Vite is recommended. Realization Training Micro Frontends with Vue The course combines conceptual explanations with guided exercises and coding workshops. Participants work on cases that simulate challenges encountered in real-world projects. Micro Frontends with Vue Certificate Upon completion, participants receive a certificate of participation in Micro Frontends with Vue. Modules Module 1 : Introduction Micro Frontends Overview Monolith vs Micro Frontends Advantages and Limitations Use Cases Core Principles Team Autonomy Deployment Flexibility Integration Styles Scaling Applications Industry Examples Module 2 : Vue Recap Vue Fundamentals Vue Components Directives Basics Reactivity System Props and Events Vue Router Intro Vue CLI Data Binding Lifecycle Hooks Composition API Module 3 : Architecture Architecture Patterns Component Composition Shared vs Isolated State Event Bus Pattern Routing Strategies Shell Application Cross-App Communication Lazy Loading Modules Error Boundaries Resilience Patterns Module 4 : Module Federation Webpack Federation Setup Host and Remote Apps Dynamic Imports Shared Libraries Exposing Components Version Handling Runtime Integration Error Handling Configuration Files Best Practices Module 5 : Integration UI Composition Shared Navigation Authentication Integration Authorization Handling Micro Frontend Shell Cross-App Routing Service Integration Performance Monitoring Testing Integration Real-World Scenarios Module 6 : State Management State Basics in Vue Local Component State Props and Events Vuex Overview Vuex Store Setup Modules in Vuex Cross-App State Sharing Async State Handling Pinia Introduction Debugging Tools Module 7 : Deployment Deployment Strategies Independent Deployments CI/CD Pipelines Environment Configurations Docker with Vue Kubernetes Deployments Cloud Hosting Options Version Control Rollback Strategies Monitoring Solutions Module 8 : Testing Unit Testing Vue Vue Test Utils Jest Framework Integration Testing End-to-End Testing Cypress Framework Contract Testing Test Automation Performance Testing Accessibility Testing Module 9 : Advanced Topics Security Concerns Authentication Patterns Authorization Patterns Performance Optimization Error Handling Accessibility Concerns Caching Strategies Future Trends Case Studies Hands-On Project
€2.299
Klassikaal
max 12
3 dagen

Micro Frontends with Angular

Amsterdam wo 10 jun. 2026 en 9 andere data
The course Micro Frontends with Angular from SpiralTrain teaches you how to design and implement scalable frontend architectures using Angular. Introduction The course Micro Frontends with Angular starts with an overview of the differences between monolithic applications and micro frontends. Benefits, key principles, and real-world examples of micro frontend architectures are discussed. Angular Recap Next a short recap of core Angular concepts is given, including components and templates, dependency injection, modules, routing, and change detection, as a foundation for building micro frontends. Architecture This module covers architectural patterns and design choices such as domain-driven design, component communication, the use of an event bus, lazy loading, and versioning strategies. Module Federation Here participants learn to work with Webpack Module Federation, including host and remote applications, dynamic imports, shared libraries, and best practices for runtime integration. Integration This part focuses on integrating micro frontends into a complete application. Topics include routing integration, UI composition, cross-app communication, the role of a shell application, and handling authentication and authorization. State Management State management is addressed with an emphasis on challenges of shared state and the use of NgRx. Key topics are store setup, selectors, and reducers for consistent and maintainable state handling. Deployment Deployment strategies are discussed with attention to CI/CD pipelines, independent deployments, containerization with Docker, and hosting options in Kubernetes and the cloud. Testing This module introduces essential testing approaches such as unit and integration testing, end-to-end testing with Cypress, contract testing, and automation with performance checks. Advanced Topics The course concludes with advanced subjects such as security and access control, error handling with error boundaries, performance optimization, and a hands-on project that ties everything together. Audience Course Micro Frontends with Angular This course is intended for Angular developers, frontend engineers, and software architects who want to learn how to design applications using a micro frontend architecture. Prerequisites Course Micro Frontends with Angular Participants should have a good understanding of JavaScript, TypeScript, and Angular development. Familiarity with web components, modular architectures, and build tools is helpful. Realization Training Micro Frontends with Angular The course combines theoretical sessions with hands-on labs guided by an expert trainer. Real-world case studies are central to the training experience. Micro Frontends with Angular Certificate After completion, participants receive a certificate of participation in Micro Frontends with Angular. Modules Module 1 : Introduction Micro Frontends Overview Monolith vs Micro Frontends Benefits and Challenges Use Cases Key Principles Architecture Styles Integration Approaches Deployment Strategies Scaling Teams Real-World Examples Module 2 : Angular Recap Angular Fundamentals TypeScript Essentials Components and Templates Services and Dependency Angular CLI Modules and Imports Data Binding Directives Basics Routing Essentials Angular Change Detection Module 3 : Architecture Micro Frontend Concepts Architecture Patterns Domain-Driven Design Shared vs Isolated State Component Communication Event Bus Pattern Routing Strategies Lazy Loading Versioning Strategies Resilience Patterns Module 4 : Module Federation Webpack Module Federation Host and Remote Apps Dynamic Imports Shared Libraries Version Compatibility Exposing Components Runtime Integration Configuration Files Error Handling Best Practices Module 5 : Integration Routing Integration UI Composition Shared Navigation Shared Services Cross-App Communication Micro Frontend Shell Authentication Integration Authorization Handling Performance Monitoring Testing Integration Module 6 : State Management State Management Intro Local Component State Shared State Issues NgRx Overview NgRx Store Setup Selectors and Actions Reducers and Effects Cross-App State Sharing State Synchronization State Debugging Tools Module 7 : Deployment Deployment Strategies CI/CD Pipelines Independent Deployments Version Management Environment Configurations Containerization Basics Docker with Angular Kubernetes Deployments Cloud Hosting Options Rollback Strategies Module 8 : Testing Unit Testing Angular Integration Testing E2E Testing Basics Jest with Angular Cypress Framework Contract Testing Test Automation Performance Testing Accessibility Testing Testing Best Practices Module 9 : Advanced Topics Micro Frontend Security Authentication Patterns Authorization Patterns Error Boundary Handling Accessibility Concerns Performance Optimization Caching Strategies Future Trends Case Studies Hands-On Project
€2.299
Klassikaal
max 12
3 dagen

Building Large Language Models

Amsterdam ma 8 jun. 2026 en 9 andere data
The course Building Large Language Models from SpiralTrain teaches you how to design, train, fine-tune, and deploy transformer-based LLMs using PyTorch and modern tooling. LLM Intro The course starts by explaining what LLMs are, where they’re used, and the lifecycle of building vs. using them. We introduce the Transformer/GPT architecture, how models learn from large datasets, and when to use classic QA versus RAG. Working with Text Data You’ll move from raw text to model-ready tensors: tokenization (e.g., BPE), token→ID mapping, special/context tokens, and sliding-window sampling. We cover embeddings and positional encodings while handling unknown words and basic sentence structure. Attentions Mechanism This module demystifies self-attention for long-sequence modeling: queries, keys, values, and causal masking to hide future tokens. We add positional encoding, multi-head attention, and stacked layers to capture dependencies across different parts of the input. Pytorch Deep Learning This module explains PyTorch fundamentals—tensors, core operations, and training loops—with the tooling to measure model quality. We cover feature scaling/normalization (including categorical features), activation and loss functions, and backpropagation. Neural Networks Next the course proceeds with building MLPs and CNNs in PyTorch while choosing appropriate activations and losses and implementing backprop. We touch NLP-specific preprocessing and walk through end-to-end binary and multi-class classification. GPT from scratch Next you will implement a minimal GPT with layer normalization, residual connections, and attention + feed-forward (GELU) blocks. Pretraining Then pretrain the LLM on unlabeled text with next-token prediction, tracking training vs. validation losses. You will explore decoding strategies (e.g., temperature, top-k), control randomness for reproducibility, and save/load PyTorch weights. Tuning for Classification Then the course covers preparing datasets and dataloaders, initializing from pretrained weights, and add a classification head with softmax. Train and evaluate with loss/accuracy, culminating in an LLM-based spam-classification example. Fine-Tuning Finally you will practice supervised instruction tuning: format datasets, batch efficiently, and fine-tune a pretrained LLM. Also evaluate outputs, export responses/checkpoints, and apply parameter-efficient methods such as LoRA. Audience Course Building Large Language Models The course Building Large Language Models is intended for engineers who want to design transformer-based LLMs. Prerequisites Course Building Large Language Models Participants should be comfortable with Python. Prior exposure to PyTorch or a similar Deep Learning framework is helpful. Realization Training Building Large Language Models The training blends concise theory with guided, hands-on labs. Through code-alongs you’ll build a mini-GPT, prepare datasets, run pretraining and fine-tuning and deploy models. Building Large Language Models Certificate After completion, participants receive a certificate of participation for the course Building Large Language Models. Modules Module 1 : LLM Intro What is an LLM? Applications of LLMs Stages of Building LLMs Stages of Using LLMs Transformer Architecture Utilizing Large Datasets GPT Architecture Internals Learn Language Patterns Retrieval Augmented Generation Question and Answer Systems QA versus RAG Building an LLM Module 2 : Working with Text Data Word Embeddings Decoders and Encoders Decoder Only Transformer Tokenizing text Convert Tokens into IDs Special Context Tokens Understand Sentence Structure Byte Pair Encoding Unknown Words Sampling with Sliding Window Creating Token Embeddings Encoding Word Positions Module 3 : Attentions Mechanism Modeling Long Sequences Capturing Data Dependencies Attention Mechanisms Attending Different Input Parts Using Self-Attention Trainable Weights Hiding Future Words Positional Encoding Causal Attention Masking Weights with Dropout Multihead Attention Stacking Attentions Layers Module 4 : Pytorch Deep Learning Deep Learning Intro Overview of PyTorch PyTorch Tensors Tensor Operations Model Evaluation Metrics Feature Scaling Feature Normalization Categorical Features Activation Functions Loss Functions Backpropagation Module 5 : Neural Networks Neural Networks Intro Building NN with PyTorch Multiple Layers of Arrays Convolutional Neural Networks Activation Functions Loss Functions Backpropagation Natural Language Processing Stopword Removal Binary Classification Multi-class Classification Module 6 : GPT from scratch Coding an LLM Architecture Layer Normalization Normalizing Activations Feed Forward Network GELU Activations Adding Shortcut Connections Connecting Attention Weight Tying Linear Layers in Transformer Block Coding the GPT Model Generating Text Module 7 : Pretraining Pretraining on Unlabeled Data Calculating Text Generation Loss Training Losses Validation Set Losses Training an LLM Decoding Strategies Control Randomness Temperature Scaling Saving Model Weights in PyTorch Loading Pretrained Weights Module 8 : Tuning for Classification Categories of Fine-Tuning Preparing the Dataset Creating Data Loaders Top-k Sampling Soft-Max Function Initializing with Pretrained Weights Adding Classification Head Classification Loss and Accuracy Fine-tuning on Supervised Data Using LLM as Spam Classifier Module 9 : Fine-Tuning Instruction Fine-tuning Supervised Instruction Preparing a Dataset Organizing Training Batches Creating Data Loaders Loading a pretrained LLM Fine-tuning the LLM Extracting and Saving Responses Evaluating Fine-tuned LLM Fine Tuning with LoRA
€3.200
Klassikaal
max 12
4 dagen

Agentic AI with LangChain

Amsterdam ma 20 jul. 2026 en 9 andere data
The course Agentic AI with LangChain from SpiralTrain teaches you how to build intelligent, autonomous AI agents that can reason, plan, and execute complex tasks. Intro Agentic AI The course Agentic AI with LangChain begins with a comprehensive introduction to agentic AI systems, exploring how they differ from traditional chatbots and what makes an agent truly autonomous. The architecture patterns, core components, and the role of LLMs as reasoning engines are discussed, along with common challenges and real-world use cases. LangChain Fundamentals This module provides a thorough foundation in the LangChain framework, covering its architecture, the distinction between chains and agents, and essential components like prompt templates, memory modules, and document loaders. Building First Agent Here participants create their first functional AI agent from scratch. The module covers choosing appropriate LLMs, defining clear agent goals, writing effective prompts, integrating tools, managing state, and implementing robust error handling. Agent Tools and Actions This part focuses on expanding agent capabilities through tools and actions. Participants learn to create custom tools, integrate APIs, connect to databases, implement search functionality, enable web scraping, and handle tool execution errors properly. Memory and Context Memory management is explored in depth, covering different memory types including short-term, long-term, conversation buffers, and vector stores. The module addresses entity memory, knowledge graphs, and techniques for optimizing memory. Multi-Agent Systems This module introduces collaborative multi-agent systems using frameworks like LangGraph. Topics include agent collaboration patterns, message passing between agents, task decomposition, workflow orchestration, and evaluating multi-agent performance. RAG and Knowledge Retrieval Augmented Generation is covered comprehensively, including document processing, embeddings, vector databases, and semantic search. Participants learn chunking strategies, and methods for evaluating RAG system performance. Production Deployment Deployment considerations are addressed with attention to API development, scalability, performance optimization, caching, rate limiting, and security best practices. The module also covers monitoring, observability, cost management, and testing strategies. Audience Course Agentic AI with LangChain This course is intended for software developers, data scientists and AI engineers, who want to build autonomous AI systems using LangChain. Prerequisites Course Agentic AI with LangChain Participants should know Python programming and a basic understanding of machine learning. Realization Training Agentic AI with LangChain The training combines theoretical instruction with hands-on exercises guided by an experienced trainer. Participants build working agents throughout the course. Agentic AI with LangChain Certificate After successful completion, participants receive a certificate of participation in Agentic AI with LangChain. Modules Module 1: Introduction to Agentic AI What is Agentic AI Agents vs Chatbots Agent Architecture Patterns LLMs as Reasoning Engines Agent Core Components Autonomy and Decision-Making Agent Frameworks Overview LangChain Introduction Use Cases and Applications Common Challenges Module 2: LangChain Fundamentals LangChain Architecture Models and Prompts Chains vs Agents Prompt Templates Memory Modules Document Loaders Output Parsers Streaming Responses Tool Integration Basics LangSmith Debugging Module 3: Building First Agent Choosing an LLM Defining Agent Goals Writing Effective Prompts Tool Selection and Integration Managing Agent State Error Handling Strategies Multi-Step Task Planning Agent Personality Design Logging and Monitoring Sandbox Environments Module 4: Agent Tools and Actions Tool Abstractions Custom Tool Creation API Integration Search Tools Calculator and Math Tools Database Connections File System Access Web Scraping Tools Code Execution Tools Tool Error Handling Module 5: Memory and Context Memory Types Overview Short-Term Memory Long-Term Memory Conversation Buffer Vector Store Memory Entity Memory Knowledge Graphs Memory Retrieval Strategies Context Window Management Memory Optimization Module 6: Multi-Agent Systems Multi-Agent Concepts Agent Collaboration Patterns LangGraph Framework Agent Roles and Responsibilities Message Passing Task Decomposition Goal Refinement Workflow Orchestration Conflict Resolution Evaluation Strategies Module 7: RAG and Knowledge Retrieval Augmented Generation Document Processing Embeddings and Vectors Vector Databases Semantic Search Chunking Strategies Hybrid Search Reranking Techniques Citation and Sources RAG Evaluation Module 8: Production Deployment Agent Deployment Patterns API Development Scalability Considerations Performance Optimization Caching Strategies Rate Limiting Security Best Practices Monitoring and Observability Cost Management Testing Strategies Module 9: Advanced Applications Coding Assistants Research Agents Customer Service Bots Finance and Analytics Agents Enterprise Automation Real-Time Agent Systems Guardrails and Safety Ethical Considerations Future of Agentic AI Capstone Project
€2.250
Klassikaal
max 12
3 dagen

Multi Agents with LangGraph

Amsterdam wo 10 jun. 2026 en 9 andere data
The course Multi Agents with LangGraph from SpiralTrain teaches you how to design and build sophisticated multi-agent AI systems using LangGraph. Introduction LangGraph The course Multi Agents with LangGraph begins with a comprehensive introduction to LangGraph, exploring how it differs from traditional agent frameworks. Graph-based architectures, StateGraph concepts, nodes, edges, and conditional routing are discussed. Graph Fundamentals This module covers essential graph theory concepts including directed graphs, state machines, and different node and edge types. Participants learn about entry points, conditional edges, cyclic graphs, and techniques for graph compilation and visualization. State Management State management in LangGraph is explored in depth, covering state schema definition using TypedDict, state updates, reducers, and immutability. The module addresses checkpointing, state persistence, restoration, and debugging techniques. Building Agents Here participants learn to build agent nodes with tool-calling capabilities using the ReAct pattern. Topics include custom agent logic, agent state management, error handling, monitoring, testing, and established best practices for robust agent development. Multi-Agent Patterns This part focuses on architectural patterns for multi-agent systems including hierarchical structures, supervisor patterns, and manager-worker configurations. Sequential, parallel, and collaborative agent patterns are explored along with orchestration strategies. Agent Communication Communication between agents is addressed through message passing, shared state, and handoff mechanisms. The module covers communication protocols, event systems, inter-agent messaging, state broadcasting, and synchronization techniques. Advanced Workflows Complex workflow patterns are introduced including human-in-the-loop systems, approval workflows, and branching logic. Topics include loop detection, retry mechanisms, fallback strategies, subgraphs, and workflow composition for sophisticated multi-agent applications. Production Deployment Deployment considerations are covered with focus on the LangGraph API, scaling strategies, and streaming responses. The module addresses persistence backends, checkpoint storage, cloud deployment options, and cost optimization for production environments. Audience Course Multi Agents with LangGraph This course is intended for AI engineers, software developers, and data scientists who want to build multi-agent systems using LangGraph and orchestrate AI workflows. Prerequisites Course Multi Agents with LangGraph Participants should have Python skills and understanding of LLMs and AI agents. Familiarity with LangChain, graph theory, and asynchronous programming is beneficial. Realization Training Multi Agents with LangGraph The training combines theoretical instruction with hands-on exercises guided by an expert trainer. Participants build real multi-agent systems throughout the course. Multi Agents with LangGraph Certificate After successful completion, participants receive a certificate of participation in Multi Agents with LangGraph. Modules Module 1: Introduction LangGraph LangGraph Overview Agents vs Workflows Graph-Based Architecture StateGraph Concepts Nodes and Edges Conditional Routing LangGraph vs LangChain Use Cases Installation and Setup Development Environment Module 2: Graph Fundamentals Graph Theory Basics Directed Graphs State Machines Node Types Edge Types Entry Points Conditional Edges Cyclic Graphs Graph Compilation Graph Visualization Module 3: State Management State in LangGraph State Schema Definition TypedDict States State Updates State Reducers Immutable State State Persistence Checkpointing State Restoration State Debugging Module 4: Building Agents Agent Nodes Tool-Calling Agents ReAct Pattern Agent Executors Custom Agent Logic Agent State Error Handling Agent Monitoring Agent Testing Agent Best Practices Module 5: Multi-Agent Patterns Hierarchical Agents Supervisor Pattern Manager-Worker Pattern Sequential Agents Parallel Agents Collaborative Agents Competitive Agents Specialized Agents Agent Orchestration Design Patterns Module 6: Agent Communication Message Passing Shared State Agent Handoffs Communication Protocols Event Systems Inter-Agent Messages State Broadcasting Conflict Resolution Synchronization Communication Debugging Module 7: Advanced Workflows Complex Workflows Human-in-the-Loop Approval Workflows Branching Logic Loop Detection Retry Mechanisms Fallback Strategies Subgraphs Workflow Composition Performance Optimization Module 8: Production Deployment LangGraph API Deployment Strategies Scaling Considerations Streaming Responses Persistence Backends Checkpoint Storage Cloud Deployment Monitoring Solutions Cost Optimization Production Best Practices Module 9: Real-World Applications Customer Support Systems Research Automation Code Review Agents Data Analysis Workflows Content Generation Pipelines Decision Support Systems Process Automation Testing Frameworks Case Studies Capstone Project
€2.250
Klassikaal
max 12
3 dagen

N8N Workflow Automation

Amsterdam ma 6 jul. 2026 en 9 andere data
The course N8N Workflow Automation from SpiralTrain teaches you how to design and build powerful automation workflows using the n8n platform. Introduction to N8N The course N8N Workflow Automation begins with an introduction to n8n and workflow automation concepts. Self-hosted versus cloud options, installation, the n8n interface, nodes, triggers, and practical use cases are explored. Building Workflows This module covers creating workflows from scratch including trigger and action nodes, configuration, data mapping, testing, error handling basics, workflow organization, and using templates for rapid development. Advanced Nodes Here participants learn to work with advanced nodes including HTTP requests, webhooks, code execution, functions, conditional logic with IF and Switch nodes, data merging, splitting, and loop operations. Integration and APIs This part focuses on integrating external services through API authentication methods, OAuth, connecting third-party applications like Slack and Google Workspace, database connections, and building custom integrations. Data Transformation Data manipulation techniques are addressed including JSON processing, the expression language, filtering, aggregation, date-time functions, string and array operations, data validation, and error recovery strategies. Production Deployment The course concludes with production considerations including deployment strategies, environment management, credentials security, workflow monitoring, error notifications, performance optimization, scaling, and backup procedures. Audience Course N8N Workflow Automation This course is intended for business analysts, IT professionals, developers, and automation specialists who want to streamline processes and automate workflows using n8n. Prerequisites Course N8N Workflow Automation Participants should have basic technical understanding and familiarity with web applications and APIs. Programming experience and knowledge of JSON is beneficial. Realization Training N8N Workflow Automation The training combines theoretical instruction with extensive hands-on exercises guided by an expert trainer. Participants build real automation workflows throughout the course using practical business scenarios. N8N Workflow Automation Certificate After successful completion, participants receive a certificate of participation in N8N Workflow Automation. Modules Module 1: Introduction to N8N N8N Overview Workflow Automation Concepts Self-Hosted vs Cloud Installation and Setup N8N Interface Nodes and Connections Triggers and Actions Workflow Execution Use Cases Best Practices Module 2: Building Workflows Creating First Workflow Trigger Nodes Action Nodes Node Configuration Data Mapping Testing Workflows Error Handling Basics Workflow Organization Saving and Versioning Workflow Templates Module 3: Advanced Nodes HTTP Request Node Webhook Node Code Node Function Node Set Node IF Node Switch Node Merge Node Split in Batches Loop Nodes Module 4: Integration and APIs API Authentication OAuth Integration API Keys Third-Party Apps Database Connections Email Integration Slack Integration Google Workspace CRM Systems Custom Integrations Module 5: Data Transformation Data Manipulation JSON Processing Expression Language Data Filtering Data Aggregation Date and Time Functions String Operations Array Operations Data Validation Error Recovery Module 6: Production Deployment Deployment Strategies Environment Variables Credentials Management Monitoring Workflows Error Notifications Performance Optimization Scaling Workflows Backup and Recovery Security Best Practices Production Checklist
€1.699
Klassikaal
max 12
2 dagen