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PyTorch Essentials: An Applications-First Approach (LFD273) Course Overview

PyTorch Essentials: An Applications-First Approach (LFD273) Course Overview

The course begins with an overview of PyTorch, including model classes, datasets, data loaders and the training loop. Next, it covers the role and power of transfer learning, along with how to use it with pretrained models. Practical lab exercises cover multiple topics including: image classification, object detection, sentiment analysis, text classification, and text generation/completion. Learners also will use their data to fine-tune existing models and leverage third-party APIs.

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Course Prerequisites

Certainly! Here are the minimum required prerequisites for successfully undertaking the PyTorch Essentials: An Applications-First Approach (LFD273) course:


  • Basic understanding of Python programming: Familiarity with Python syntax and data structures is essential for writing and understanding code in this course.
  • Fundamental knowledge of linear algebra: A basic grasp of concepts such as vectors, matrices, and operations involving them will be beneficial.
  • Introduction to machine learning: Familiarity with basic machine learning concepts, such as supervised and unsupervised learning, will help in comprehending PyTorch's application in building models.
  • Experience with scientific computing libraries: Exposure to libraries like NumPy or Pandas can assist in data manipulation and analysis tasks associated with PyTorch.

With these prerequisites, learners will be well-prepared to dive into the practical applications of PyTorch in this course!


PyTorch Essentials: An Applications-First Approach (LFD273)
PyTorch and Deep Learning for Decision Makers (LFS116)

Target Audience for PyTorch Essentials: An Applications-First Approach (LFD273)

PyTorch Essentials: An Applications-First Approach (LFD273) equips learners with practical skills in PyTorch for developing AI applications, targeting individuals eager to enhance their machine learning expertise.


  • Data Scientists
  • Machine Learning Engineers
  • Software Developers
  • AI Researchers
  • Data Analysts
  • DevOps Engineers
  • AI Product Managers
  • Computer Vision Specialists
  • Academic Researchers
  • IT Professionals looking to pivot into AI
  • Enthusiasts and Hobbyists in AI/ML


Learning Objectives - What you will Learn in this PyTorch Essentials: An Applications-First Approach (LFD273)?

Introduction:
The PyTorch Essentials: An Applications-First Approach (LFD273) course provides a hands-on introduction to PyTorch, focusing on practical applications in deep learning and helping students master essential concepts for developing AI models.

Learning Objectives and Outcomes:

  • Understand the fundamental principles of PyTorch and its architecture.
  • Gain proficiency in building and training deep learning models.
  • Explore key components such as tensors, gradients, and automatic differentiation.
  • Learn to implement neural networks for various applications.
  • Develop skills in optimizing model performance using techniques like regularization.
  • Gain experience with data preprocessing and augmentation strategies.
  • Understand how to evaluate model performance and interpret results.
  • Get familiar with deploying PyTorch models in real-world scenarios.
  • Explore advanced topics such as transfer learning and neural network tuning.
  • Build a portfolio of projects showcasing practical applications of PyTorch.

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