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Course Outline
Introduction to Google Colab for Deep Learning
- Overview of Google Colab
- Setting up Google Colab
- Navigating the Google Colab interface
Introduction to Deep Learning
- Overview of deep learning
- Importance of deep learning
- Applications of deep learning
Understanding Neural Networks
- Introduction to neural networks
- Architecture of neural networks
- Activation functions and layers
Getting Started with TensorFlow
- Overview of TensorFlow
- Setting up TensorFlow in Google Colab
- Basic TensorFlow operations
Building Deep Learning Models with TensorFlow
- Creating neural network models
- Training neural networks
- Evaluating model performance
Advanced TensorFlow Techniques
- Implementing convolutional neural networks (CNNs)
- Implementing recurrent neural networks (RNNs)
- Transfer learning with TensorFlow
Data Preprocessing for Deep Learning
- Preparing datasets for training
- Data augmentation techniques
- Handling large datasets in Google Colab
Optimizing Deep Learning Models
- Hyperparameter tuning
- Regularization techniques
- Model optimization strategies
Collaborative Deep Learning Projects
- Sharing and collaborating on notebooks
- Real-time collaboration features
- Best practices for collaborative projects
Tips and Best Practices
- Effective deep learning techniques
- Avoiding common pitfalls
- Enhancing model performance
Summary and Next Steps
Requirements
- Basic knowledge of machine learning
- Experience with Python programming
Audience
- Data scientists
- Software developers
14 Hours
Testimonials (2)
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course - Natural Language Processing with TensorFlow
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
