Pytorch Deep Learning

This PyTorch course is very hands-on and project based. You won’t just be staring at your screen. We’ll leave that for other PyTorch tutorials and courses. In this course you’ll actually be: Running experiments Completing exercises to test your skills Building real-world deep learning models and projects to mimic real life scenarios By the end of it all, you’ll have the skillset needed to identify and develop modern deep learning solutions that Big Tech companies encounter. Enroll Here

Skills You Will Gain

Tensor Operations


Paper Replication

Model Training

Model Qunatization

Vision Transformation

This course includes

Syllabus Overview

  • Introduction to PyTorch: Explore the fundamentals, advantages, and applications of PyTorch in industry and academia.
  • Environment Setup: Instructions on installing and setting up PyTorch on various platforms (Linux, Windows, Mac).
  • PyTorch Tensors: In-depth exploration of tensors, the core component of PyTorch, including creation, manipulation, and operations.



This revised syllabus provides a structured and detailed pathway through learning PyTorch, ensuring that learners not only gain theoretical knowledge but also practical skills in building, optimizing, and deploying neural networks using PyTorch.

  • Tensor Operations: Detailed guide on tensor operations including addition, subtraction, multiplication, division, and more complex mathematical operations.
  • Matrix Operations: Focus on matrix multiplication, understanding its role in neural networks, and troubleshooting common tensor shape errors.
  • Advanced Tensor Manipulation: Techniques for reshaping, squeezing, unsqueezing, and indexing tensors to prepare data for models.
  • Linear Regression Models: Constructing linear regression models from scratch, understanding the underlying mathematics and implementation in PyTorch.
  • Classification Models: Building and training neural networks for classification tasks, understanding network architectures, and performance metrics.
  • Computer Vision with CNNs: Application of convolutional neural networks to image data for classification, detection, and segmentation tasks.
  • Custom Datasets and DataLoaders: Handling custom datasets, transforming them into tensors, and utilizing PyTorch’s DataLoader for efficient model training.
  • Transfer Learning: Leveraging pre-trained models to boost performance and efficiency on new tasks with a focus on enhancing FoodVision Mini.
  • Model Optimization: Exploring techniques like quantization and pruning to enhance model performance and efficiency.
  • Experiment Tracking with TensorBoard: Implementing TensorBoard to track experiments, visualize model performance, and select the best models.
  • Deployment Strategies: Techniques and tools for deploying PyTorch models into production, including Docker, Kubernetes, and cloud platforms.
  •  Replicating Machine Learning Research: Practical experience in replicating results from cutting-edge research papers, focusing on architectures like Vision Transformers.
  • Model Debugging and Evaluation: Strategies for debugging PyTorch models, understanding outputs, and employing various evaluation metrics.
  • Capstone Project: A comprehensive project that synthesizes all the learned skills to design, build, train, and deploy a model following MLOps principles.
  • Real-World Application Scenarios*: Applying learned models to solve real-world problems across different domains such as healthcare, autonomous driving, or financial forecasting.
  • Scaling PyTorch Applications: Techniques for scaling PyTorch applications horizontally and vertically to handle larger datasets and more complex models.
  • Advanced Deployment Techniques: Deep dive into advanced model deployment strategies including continuous integration and delivery pipelines for machine learning models.
  • Review and Final Assessment: Comprehensive review of all topics covered with a final assessment to test knowledge and skills.
  • Future Learning Paths: Guidance on continuing education and research opportunities in machine learning and deep learning post-course.

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