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AI A-Z: Agentic AI, Gen AI, Prompt Engineering & RL
4.6842
(19)
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411 Estudiantes
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Certificación internacional
Acceso de por vida
Acceso multiplataforma
Info
USD 300.00
USD 300.00
USD 50.00
Descripcion
Recorrido de la A a la Z por la IA moderna: IA generativa, agentes (agentic AI), ingeniería de prompts y aprendizaje por refuerzo (RL). Contenido en inglés.
¿Qué aprenderás?
- Panorama de la IA: ML, deep learning y GenAI
- IA generativa y modelos de lenguaje (LLMs)
- Ingeniería de prompts y patrones efectivos
- Agentes de IA (agentic AI) y herramientas
- Fundamentos de aprendizaje por refuerzo (RL)
¿A quién está dirigido?
Personas que quieran entender y aplicar las últimas tendencias de IA, de la generativa a los agentes y el RL.
Al finalizar el curso
Tendrás una visión integral y práctica de la IA moderna, desde prompts hasta agentes y aprendizaje por refuerzo.
Contenido del Curso
Welcome to the course!
How to Build Your First AI Chatbot Using AWS PartyRock No Coding Required
--- Part 1 Prompt Engineering ---
Prompt Engineering & Prompt Templates
Prompt Engineering Techniques
Prompt Engineering [Hands-On] Part 1 The 4 Elements of a (good) prompt
Prompt Engineering [Hands-On] Part 2 Prompt Templates
Inference Parameters
Inference Parameters [Hands-On]
--- Part 2 Generative AI ---
Fundamentals of Generative AI
Generative AI for Image Generation
Foundation Models Overview
Foundation Models Lifecycle
Data Selection
Foundation Models Selection
Training vs. Inference
Context Window
Tokens and Embeddings
Transformers
Foundation Models Training
Foundation Models Fine-Tuning
Foundation Model Fine-Tuning [Hands-On] - Short Version
Foundation Models Evaluation
Retrieval-Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) [Hands-On] - Short Version
--- Part 3 Agentic AI ---
AI Agents
Q-Learning Intuition
Deep Learning Fundamentals Neural Networks & Activation Functions Explained
How Reinforcement Learning Works A Beginner's Guide to AI Training Methods
Bellman Equation in Reinforcement Learning A Step-by-Step Introduction
From State Values to Optimal Plans Bellman Equation in AI Decision Making
Markov Decision Processes in Reinforcement Learning A Complete Guide
RL Tutorial Optimal Policy vs Fixed Plans in AI Decision Making
Living Penalty in Reinforcement Learning Optimize AI Agent Decision Making
Q-Learning in Reinforcement Learning From V-Values to Q-Values Explained
Temporal Difference in Q-Learning A Complete Guide for Reinforcement Learning
Deep Q-Learning Intuition
Deep Learning Fundamentals Neural Networks & Activation Functions Explained
Deep Q-Learning vs Traditional Q-Learning Key Differences Explained
How Deep Q-Learning Works Neural Networks & Reinforcement Learning Explained
Experience Replay in Deep Q-Learning How it Works & Why it Matters
Q-Learning Guide to Epsilon-Greedy & Softmax Action Selection Algorithms
Deep Q-Learning Implementation
Step 1 - Deep Q-Learning Environment Setup From Gmail to Lunar Lander Training
Google Colab Setup Deep Q-Learning for Lunar Lander Tutorial
Step 3 - PyTorch DQN Architecture Building the AI Brain for OpenAI Lunar Lander
PyTorch Deep Q-Learning Implementing Forward Method for Neural Nets
Step 5 - Configure LunarLander-v2 Environment Parameters for DQN Training
DQN Hyperparameters Learning Rate & Replay Buffer Setup Guide (Step 6)
Step 7 Implementing Experience Replay Memory in DQN with Python
Step 8 DQN Push Method - Adding Experiences to Replay Memory Buffer
Step 9 Coding DQN Memory Sampling - PyTorch Experience Replay Tutorial
DQN Tutorial Initialize Q-Networks, Optimizer & Replay Memory Buffer
Step 11 DQN Step Method - Store & Learn from Experiences in Python
Step 12 DQN Action Selection - State Processing to Policy Implementation
Step 13 Deep Q-Network Training - Implementing Learn Method for RL
Step 14 - Deep Q-Network Implementation Soft Update Method for Stable Training
Step 15 Creating Your First AI Agent - Deep Q-Network (DQN) Tutorial
Step 16 - Epsilon-Greedy Strategy Initializing AI Training Hyperparameters
Step 17 Deep Q-Learning Training Loop - Complete Lunar Lander Guide
Step 18 DQN Training Visualization - Dynamic Score Tracking Implementation
Step 19 Visualizing Deep Q-Learning - AI Perfects Lunar Lander Landing
ChatGPT vs Custom DQN Comparing Deep RL Implementations
Deep Convolutional Q-Learning Intuition
Deep Learning Fundamentals Neural Networks & Activation Functions Explained
Deep Convolutional Q-Learning Build AI Agents for Game Environments
Deep Q-Learning vs Eligibility Trace AI Algorithm Comparison & Guide
Deep Convolutional Q-Learning Implementation
Deep Q-Learning Setting Up Pac-Man in OpenAI Gym - Step 1
Step 2 - Implementing DCQN Architecture in Python Setup & Neural Network Design
Step 3 Building CNNs - Creating AI's Visual Processing System
CNN Architecture Adding Fully Connected Layers After Convolutional Layers
Step 5 Deep Q-Learning - Building AI's Visual Processing System
Step 6 Configuring Miss Pacman for Deep Q-Learning Training
Step 7 Deep Q-Learning Hyperparameters - Learning Rate & Batch Size Setup
Image Preprocessing for Deep Q-Learning PIL & Torchvision Implementation
Step 9 Deep Q-Learning to DCQN - Experience Replay & Memory Updates
Step 10 Implementing DCQN Agent - Deep Q-Learning Adaptation & Methods
Step 11 Optimizing DQN Training on V100 GPU - Setup to Solved Environment
Step 12 Visualizing Deep Q-Learning - Watch AI Play Pac-Man Like a Human
Step 13 Deep Q-Learning - Optimizing Neural Networks with GPT-4
A3C Intuition
Deep Learning Fundamentals Neural Networks & Activation Functions Explained
A3C Algorithm Tutorial Understanding Asynchronous Advantage Actor-Critic in AI
Actor-Critic Algorithm From Deep Q-Learning to A3C Implementation
Asynchronous Learning in A3C Shared Critics and Neural Networks Explained
How Does Advantage Work in Actor-Critic Methods A3C Algorithm Explained
LSTM in A3C Algorithm How Memory Enhances Reinforcement Learning Performance
A3C Implementation
A3C Deep RL Setting Up Kung Fu Master Environment in Atari
A3C Algorithm Implementation Neural Network & Environment Setup
Step 3 - A3C Deep Reinforcement Learning Network Class & Architecture Design
A3C Algorithm Building Action & State Value Outputs - Step 4
A3C Algorithm PreprocessAtari Class & Hyperparameter Tuning Setup
Step 6 A3C Agent Class Init Method for Deep Reinforcement Learning
A3C Agent Converting States to Actions with PyTorch Neural Networks
Step 8 Coding A3C Step Method in PyTorch - Complete Implementation Guide
Step 9 - How to Initialize an A3C Agent in Python Creating the Agent Instance
Implementing A3C Agent Evaluation in Python Deep RL Tutorial Step 10
Step 11 EnvBatch Class for A3C Multi-Environment Training in Python
Step 12 Multi-Environment A3C - EnvBatch Class Implementation Tutorial
Step 13 A3C Training - Multi-Environment Batch Setup Implementation
Step 14 A3C Training Loop with Progress Bar for Kung Fu AI Model
ChatGPT A3C Model PyTorch for KungFuMaster AI Optimization (Step 15)
--- Part 8 PPO and SAC ---
AWS DeepRacer Training Autonomous Vehicles with PPO and SAC Algorithms
LLMs Intuition
Introduction to Large Language Models (LLMs) Transformers Explained
Building Large Language Models Essential Ingredients for LLM Development
How Were Large Language Models Invented Origins of Transformer AI
Understanding Next Word Prediction How LLMs Process Text One Word at a Time
How Do Large Language Models Work A Deep Dive into LLM Architecture
What Are LLM Parameters Understanding Large Language Model Size Explained
Context Windows Explained How LLMs Remember Conversation History
Fine-Tuning Large Language Models Real-World Applications and Use Cases
LLMs Implementation
Fine-Tuning LLMs for Medical Chatbots A Practical Guide with Hugging Face
Step 1 - Installing & Importing Key Libraries for Fine-Tuning Llama 2 Models
Step 2 - Loading LLaMA 2 Model Hugging Face Transformers & 4-bit Precision
Step 3 - Loading & Configuring HuggingFace Tokenizer for LLaMA 2 Implementation
Step 4 - Setting Training Arguments for LLM Fine-Tuning in Transformers Library
Step 5 - Implementing SFTTrainer Memory-Efficient LLM Training with PEFT & LoRA
Step 6 LoRA & Quantization for Efficient LLM Training in Medical Terms
Step 7 Chatting with Your Fine-Tuned Medical LLM via Text Generation
--- Part 10 Responsible AI ---
Features of Responsible AI
Guardrails in Generative AI
Amazon Bedrock Guardrails [Hands-On]
Legal Risks of Generative AI
AWS Tools for Responsible AI
Amazon SageMaker Clarify and Monitor [Hands-On]
Amazon Augmented AI [Amazon A2I] [Hands-On]
Interpretability vs. Explainability
SageMaker Model Cards
Amazon SageMaker Model Cards [Hands-On]
THANK YOU
THANK YOU Video
Annex 1 Artificial Neural Networks
What is Deep Learning A Beginner's Guide to Artificial Neural Networks
Deep Learning Fundamentals Neural Networks & Activation Functions Explained
How Do Artificial Neurons Work A Complete Guide to Neural Network Basics
Neural Network Activation Functions ReLU, Sigmoid, Tanh & Threshold Explained
How Do Neural Networks Work A Step-by-Step Property Valuation Example
How Do Neural Networks Learn Understanding Backpropagation & Cost Functions
Understanding Gradient Descent Optimize Neural Network Weights Efficiently
Stochastic Gradient Descent vs Batch Gradient Descent What's the Difference
Backpropagation in Neural Networks Step-by-Step Training Guide
Annex 2 Convolutional Neural Networks
Deep Learning Fundamentals Neural Networks & Activation Functions Explained
CNN vs Human Vision How Convolutional Neural Networks Process Images
CNN Convolution Feature Detection & Feature Maps in Deep Learning
ReLU in CNN Understanding Non-Linearity for Deep Learning
Step 2 - Max Pooling in CNN How to Reduce Feature Maps & Prevent Overfitting
Step 3 - CNN Feature Map Flattening From Pooling Layer to Neural Network Input
Step 4 CNN Classification - How FC Layers Process Features
CNN Architecture Explained Feature Detection to Neural Network Classification
Understanding Softmax and Cross-Entropy Loss Functions in Neural Networks
Requisitos
Requisitos
- Interés en IA (no requiere nivel avanzado)
- Computadora con conexión a Internet estable
- Inglés básico (el contenido está en inglés)
Reseñas generales
4.6842 de 5
19 Puntuaciones
5 star
79%
4 star
11%
3 star
11%
2 star
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1 star
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Reseñas destacadas
D
daniela sady castro
Muy buen ritmo y explicaciones claras.
D
Demo SAPTotal
Los ejemplos practicos con Python son geniales.
M
Mildred Mejicanos
La calidad del material es excelente.
G
GONZALO SEGUNDO AGUILERA BASTIAS
Aprendi a aplicar IA a problemas reales.
J
Jose Gabriel Fernandez Toro
Lo aplique de inmediato en mi trabajo.
J
Jean Pierre Espinoza Marambio
Excelente para meterse en datos e IA desde cero.
J
Juan carlos zuleta corcho
Muy didactico, ideal para autoestudio.
E
Edward sucari carrasco
Una gran inversion para mi carrera.
Y
YENUEN GARCÍA
Aprendi mucho mas de lo que esperaba.
R
Roberto Aguilar Soto
Ideal para analistas que quieren subir de nivel.
J
Jose Hernandez Reyes
Excelente curso, muy completo y bien explicado.
Y
YUEH MOLANO
Lo recomiendo totalmente.
V
Visa
Material actualizado y facil de seguir.
R
Ricardo Torres Silva
Explicaciones simples para temas complejos.
L
Laura Sanchez Morales
Los ejercicios y casos reales son muy utiles.
D
Diego Perez Alarcon
Contenido practico y bien estructurado.
A
Adriana Jazmín Lozada Mendez
Muy claro el paso de los datos al modelo.
M
Miguel Venegas Yañez
Lo recomiendo a cualquier profesional del area.
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Parte de una ruta de especialización
Especialización en Data & Inteligencia Artificial
Inversion
USD 50.00
Duracion17 Horas
EvaluacionSi
CertificadoSi
WebinarsSi
IdiomaEspañol
Aprobacion93%