Skip to content

Contact sales

By filling out this form and clicking submit, you acknowledge our privacy policy.


Course Summary

The PyTorch training course is designed to demonstrate the basics of data science and machine learning.

The course begins with a review of the key mathematical concepts used by the hands-on data science and machine learning labs in this course. Next, it examines Jupyter Notebooks to implement low level PyTorch code for commonly used tensor operations such as stacking, slicing, reshaping, and squeezing. The course concludes with best practices and a case study for how to build a mature data science and machine learning practice on a project or at an organization.

Learn how to implement statistical and machine learning models using PyTorch and how to improve their performance based on an understanding of underlying mathematical principles.
Students who have either taken an introduction to Machine Learning course or have equivalent knowledge/experience.
Data Engineer - Data Scientist - Software Developer
Skill Level
3 Days
Related Technologies
Python | PyTorch


Productivity Objectives
  • Grasp a focus on applied math.
  • Determine an operational approach.
  • Perceive and identify Machine Learning.

What You'll Learn:

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
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”


Dive in and learn more

When transforming your workforce, it's important to have expert advice and tailored solutions. We can help. Tell us your unique needs and we'll explore ways to address them.

Let's chat

By filling out this form and clicking submit, you acknowledge our privacy policy.