PyTorch

In the PyTorch training course, you'll learn:

• Getting started with PyTorch
  • PyTorch Models and Frameworks
  • Distributed Training Support
  • Core Python API
• Jupyter Notebooks
• Intermediate Machine Learning
  • Tensors as Data Structures
  • Machine Learning for Data Science
  • Regression vs. Classification Use Cases
  • Reproducibility in Machine Learning
• Math Foundations Recap
  • MSE and Cross-Entropy Loss
  • Troubleshooting Gradient Descent
  • Choosing the Right Regularization
  • Type I and II Errors in Classification
• Regression with Structured Data
  • Reproducible Datasets with Hashing
  • Regression Loss vs. Metric
  • Benchmark Loss and Metric for a Dataset
• Classification with Structured Data
  • Deep Neural Network Models
  • Activation Functions
• PyTorch APIs
  • Use Cases Regression and Classification
  • Processing Shared Datasets
  • Fault-Tolerant Distributed Training
  • TensorBoard for Monitoring and Analysis
• Classification with Unstructured Image Data
  • Fashion-MNIST and Flowers Image Datasets
  • Cross-Entropy Loss and Precision, Recall, ROC, AUC Metrics
  • Deep Neural Networks for Image Classification
  • Convolutional Neural Networks for Image Classification
  • Convolutional and Maxpooling Layers
• Training Convolutional Neural Networks
  • L1, L2, and Dropout Regularization
  • Batch Normalization
  • Data Augmentation
  • Transfer Learning
• Advanced PyTorch
  • Storing / loading models
  • Debugging
  • Torch JIT
  • Hooks for forward / backward custom processing
  • PyTorch & GPU
• Feature Engineering
  • Five Criteria for Effective Features
  • Case Studies and Best Practices
  • Feature Crosses, Quantization, Hot-one Encoding with PyTorch
  • Feature Pre-processing and Engineering in a Machine Learning Pipeline
  • Features for Wide-and-Deep Machine Learning Models
• Model Training and Evaluation
  • Gradient Descent vs. Alternatives for Training
  • Netflix Prize Model Evaluation Case Study
  • Best Practices for Model Evaluation