Section outline

• Select section Day 1: Introduction to Deep Learning and TensorFlow

  Day 1: Introduction to Deep Learning and TensorFlow

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  Module 1: Overview of Deep Learning

  • Neural networks, activation functions, loss functions

  Module 2: Introduction to TensorFlow

  • TensorFlow architecture and basic operations

  Module 3: Neural Networks in TensorFlow

  • Building, training, and visualizing simple neural networks
• Select section Day 2: Advanced Neural Networks

  Day 2: Advanced Neural Networks

  Module 4: Convolutional Neural Networks (CNNs)

  • Implementing CNNs for image classification

  Module 5: Recurrent Neural Networks (RNNs)

  • LSTMs and their use cases in time-series data
• Select section Day 3: Deep Learning Techniques and Optimization

  Day 3: Deep Learning Techniques and Optimization

  Module 6: Techniques for Optimization

  • Dropout, batch normalization, data augmentation

  Module 7: Model Optimization

  • Hyperparameter tuning and advanced optimizers
• Select section Day 4: Specialized Models

  Day 4: Specialized Models

  Module 8: Generative Models

  • Building GANs in TensorFlow

  Module 9: Transfer Learning

  • Using pre-trained models for specific tasks

  Module 10: Natural Language Processing with RNNs

  • Text classification and sentiment analysis
• Select section Day 5: Deployment and Case Studies

  Day 5: Deployment and Case Studies

  Module 11: Model Deployment

  • Strategies with TensorFlow Serving

  Module 12: Case Studies and Real-World Applications

  • Applications in healthcare, finance, and retail

  Module 13: Capstone Project

  • Group project on deploying a deep learning model