Visão Geral

O Curso Python with TensorFlow for Deep Learning, foi projetado para capacitar os alunos a desenvolver e implementar modelos de aprendizado profundo utilizando a biblioteca TensorFlow. Com a crescente demanda por aplicações de inteligência artificial e aprendizado de máquina, este Curso Python with TensorFlow for Deep Learning oferece uma abordagem prática e teórica para o entendimento e aplicação das técnicas de deep learning em problemas reais.

Objetivo

Após realizar o Curso Python with TensorFlow for Deep Learning, você será capaz de:

• Compreender os fundamentos do aprendizado profundo e suas aplicações.
• Construir e treinar modelos de deep learning utilizando TensorFlow.
• Implementar redes neurais convolucionais (CNNs) e redes neurais recorrentes (RNNs).
• Avaliar e otimizar o desempenho de modelos de deep learning.
• Aplicar técnicas de transfer learning e fine-tuning.

Publico Alvo

• Profissionais de tecnologia que desejam aprofundar seus conhecimentos em aprendizado profundo.
• Desenvolvedores Python que buscam aplicar técnicas de deep learning em seus projetos.
• Estudantes e pesquisadores de ciência da computação, engenharia e áreas afins interessados em IA.

Pre-Requisitos

• Conhecimento básico de Python.
• Familiaridade com conceitos de aprendizado de máquina e estatística.
• Interesse em aprender sobre algoritmos e técnicas de deep learning.

Materiais

Conteúdo Programatico

Module 1: Introduction to Deep Learning

1. Understanding the basics of deep learning
2. Comparing deep learning with traditional machine learning
3. Overview of neural networks and their components
4. Applications of deep learning in various fields

Module 2: Getting Started with TensorFlow

1. Installing TensorFlow and setting up the environment
2. Introduction to TensorFlow architecture
3. Understanding tensors and operations in TensorFlow
4. Building your first simple neural network with TensorFlow

Module 3: Fundamentals of Neural Networks

1. Exploring the architecture of neural networks
2. Understanding activation functions and their roles
3. Implementing forward and backward propagation
4. Training neural networks with optimization algorithms

Module 4: Convolutional Neural Networks (CNNs)

1. Understanding CNNs and their applications in image processing
2. Implementing convolutional layers and pooling layers
3. Building a CNN model for image classification
4. Data augmentation and regularization techniques

Module 5: Recurrent Neural Networks (RNNs)

1. Understanding RNNs and their applications in sequence data
2. Implementing LSTM and GRU architectures
3. Building models for time series prediction and natural language processing
4. Handling vanishing and exploding gradients in RNNs

Module 6: Transfer Learning

1. Understanding the concept of transfer learning
2. Utilizing pre-trained models for new tasks
3. Fine-tuning models for improved performance
4. Case studies of transfer learning in practice

Module 7: Model Evaluation and Optimization

1. Evaluating model performance using metrics and validation techniques
2. Implementing techniques for hyperparameter tuning
3. Understanding overfitting and underfitting
4. Best practices for model optimization and deployment

Module 8: Building a Complete Deep Learning Application

1. Planning and designing a deep learning project
2. Implementing features learned throughout the course
3. Testing and troubleshooting deep learning models
4. Presenting the final project and demonstrating its functionality

Module 9: Final Project - Deep Learning Application

1. Developing a complete deep learning application using TensorFlow
2. Applying CNNs, RNNs, or transfer learning as needed
3. Presenting the project to showcase skills and knowledge
4. Discussing potential improvements and future enhancements