PyTorch Essentials: An Applications-First Approach (LFD273)

Course code: LFD273

Start prototyping AI applications powered by PyTorch by leveraging popular pretrained models in the fields of Computer Vision and Natural Language Processing covering an extensive span of practical applications.

This course provides hands-on experience to train and fine-tune deep learning models using the rich PyTorch and Hugging Face ecosystems of pre-trained models for Computer Vision and Natural Language Processing tasks. Additionally, you will be able to deploy prototype applications using TorchServe, allowing you to quickly validate and demo your application.

Course dates

Starting date: Upon request

Type: Self-paced

Course duration: 365 days

Language: en

Price without VAT: 285 EUR

	Starting date	Place	Type	Course duration	Language	Price without VAT
	Upon request		Self-paced	365 days	en	285 EUR	Register
G	Guaranteed course

Starting
date

Place

Type

Course
duration

Language

Price without VAT

Upon request

Self-paced

365 days

285 EUR

Guaranteed course

Course description

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.

This course includes

Online, Self Paced
40 Hours of Course Material
Hands-on Labs & Assignments1
12 Months of Access to Online Course
Digital Badge
Discussion forums

Course structure

Chapter 1. Course Introduction
Chapter 2. PyTorch, Datasets, and Models
Chapter 3. Building Your First Dataset
Chapter 4. Training Your First Model
Chapter 5. Building Your First DataPipe
Chapter 6. Transfer Learning and Pretrained Models
Chapter 7. Pretrained Models for Computer Vision
Chapter 8. Pretrained Models for Natural Language Processing
Chapter 9. Image Classification with Torchvision
Chapter 10. Fine-Tuning Pretrained Models for Computer Vision
Chapter 11. Serving Models with TorchServe
Chapter 12. Datasets and Transformations for Object Detection and Image Segmentation
Chapter 13. Models for Object Detection and Image Segmentation
Chapter 14. Object Detection Evaluation
Chapter 15. Word Embeddings and Text Classification
Chapter 16. Contextual Word Embeddings with Transformers
Chapter 17. Hugging Face Pipelines for NLP Tasks
Chapter 18. Q&A, Summarization, and LLMs

Prerequisites

To get the most possible value from this course, you should be familiar with the following:

Python (notions of Object-Oriented Programming (OOP))

PyData Stack (Numpy – arrays, slicing, vectorized operations – , Pandas – series, slicing, indexing, transformations – , Matplotlib – basic plotting only – , Scikit-Learn – linear regression, pipelines, one-hot encoding, normalization/scaling, grid search, hyper-parameter optimization)

Machine Learning Concepts (supervised learning: regression and classification; loss functions: RMSE, cross-entropy; train-validation-test split; evaluation metrics (R-squared, precision, recall, accuracy, confusion matrix)

To do the lab exercises in this course, you’ll need the following:

Google account (for Google Colab, free tier)

The date:
Takes place:
Form:
Language:
Price without VAT:

PyTorch Essentials: An Applications-First Approach (LFD273)

Course dates

Didn't find a suitable date?

Course description

Target group

Course structure

Prerequisites

Do you need advice or a tailor-made course?