Python Pytorch Programming with Coding Exercises

Course Overview

• Course Title: Python Pytorch Programming with Coding Exercises
• Instructor: Dr Python
• Target Audience:
  • Individuals with a basic understanding of Python programming
  • Those familiar with fundamental machine learning concepts
• Prerequisites:
  • Basic understanding of Python programming
  • Familiarity with fundamental machine learning concepts

Curriculum Highlights

• Key Topics Covered:
  • Introduction to PyTorch and its significance in the deep learning ecosystem
  • Building and training neural networks from scratch using PyTorch
  • Implementing various layers and activation functions for customized model architectures
  • Training, validation, and testing of deep learning models
  • Handling overfitting with regularization techniques and optimizing model performance
  • Understanding and implementing convolutional neural networks (CNNs) and recurrent neural networks (RNNs)
  • Working with datasets and data loaders for efficient training
  • Transfer learning and fine-tuning pre-trained models for specific tasks
• Key Skills Learned:
  • Building, training, and evaluating neural networks using PyTorch
  • Techniques for optimizing deep learning models, including regularization and transfer learning
  • Implementation of CNNs and RNNs for complex tasks in image and sequence data
  • Practical skills in applying PyTorch to real-world deep learning projects

Course Format

• Duration:
  • 1 hour on-demand video
• Format:
  • Self-paced online course
• Resources:
  • 2 practice tests
  • Assignments
  • 10 articles
  • Access on mobile and TV
  • Certificate of completion