Visão Geral

Este curso apresenta os fundamentos de Deep Learning, área da Inteligência Artificial baseada em redes neurais artificiais capazes de aprender padrões complexos a partir de grandes volumes de dados. O participante compreenderá a arquitetura das redes neurais, os processos de treinamento e inferência, além das principais aplicações de Deep Learning em visão computacional, processamento de linguagem natural, reconhecimento de voz e sistemas inteligentes.

Objetivo

Após realizar este curso, você será capaz de:

• Compreender os fundamentos e conceitos centrais de Deep Learning
• Entender a estrutura e o funcionamento das redes neurais artificiais
• Identificar os principais tipos de arquiteturas de Deep Learning
• Compreender os processos de treinamento, validação e inferência de modelos
• Reconhecer aplicações práticas de Deep Learning em diferentes setores
• Avaliar desafios, limitações e oportunidades dessa tecnologia

Publico Alvo

• Cientistas de dados iniciantes
• Analistas de dados
• Desenvolvedores de software
• Engenheiros de Machine Learning
• Profissionais de tecnologia interessados em IA
• Estudantes que desejam aprofundar conhecimentos em Inteligência Artificial

Pre-Requisitos

• Conhecimentos básicos de lógica de programação
• Noções de estatística e matemática básica
• Conhecimentos introdutórios de Machine Learning são recomendados

Conteúdo Programatico

Module 1: Introduction to Deep Learning

1. Evolution from Artificial Intelligence to Deep Learning
2. Machine Learning versus Deep Learning
3. History and milestones of Deep Learning
4. Deep Learning ecosystem overview
5. Business applications and use cases
6. Current trends in AI systems

Module 2: Foundations of Neural Networks

1. Biological inspiration of neural networks
2. Artificial neuron concepts
3. Network architecture fundamentals
4. Input, hidden and output layers
5. Weights, biases and activation functions
6. Feedforward neural networks

Module 3: Mathematics for Deep Learning

1. Linear algebra fundamentals
2. Vectors and matrices concepts
3. Functions and transformations
4. Probability and statistics basics
5. Gradient concepts
6. Optimization fundamentals

Module 4: Training Neural Networks

1. Training process overview
2. Forward propagation
3. Backpropagation concepts
4. Loss functions
5. Gradient descent optimization
6. Hyperparameter fundamentals

Module 5: Deep Neural Networks

1. Multi-layer neural networks
2. Deep architecture concepts
3. Vanishing and exploding gradients
4. Weight initialization strategies
5. Batch normalization overview
6. Regularization techniques

Module 6: Convolutional Neural Networks (CNNs)

1. Introduction to computer vision
2. CNN architecture fundamentals
3. Convolution operations
4. Pooling techniques
5. Image classification concepts
6. CNN applications and use cases

Module 7: Recurrent Neural Networks (RNNs)

1. Sequential data concepts
2. Recurrent neural network architecture
3. Long Short-Term Memory (LSTM)
4. Gated Recurrent Units (GRU)
5. Time-series applications
6. Sequence modeling fundamentals

Module 8: Transformers and Modern Architectures

1. Limitations of traditional architectures
2. Attention mechanism concepts
3. Transformer architecture overview
4. Large Language Models (LLMs)
5. Foundation models introduction
6. Modern AI applications

Module 9: Deep Learning Development Lifecycle

1. Data preparation for Deep Learning
2. Model development workflow
3. Training and validation strategies
4. Model evaluation techniques
5. Deployment concepts
6. Monitoring and maintenance fundamentals

Module 10: Ethics, Challenges and Future Trends

1. Ethical considerations in Deep Learning
2. Bias and fairness concepts
3. Explainability and interpretability
4. Privacy and security concerns
5. Emerging Deep Learning technologies
6. Future directions and career pathways