PyTorch Essentials: An Applications-First Approach (LFD273)

Kód kurzu: 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.

Termíny kurzov

Počiatočný dátum: Na vyžiadanie

Forma: Self-Paced

Dĺžka kurzu: 365 dní

Jazyk: en

Cena bez DPH: 285 EUR

Registrovať

	Počiatočný dátum	Miesto konania	Forma	Dĺžka kurzu	Jazyk	Cena bez DPH
	Na vyžiadanie		Self-Paced	365 dní	en	285 EUR	Registrovať
G	Garantovaný kurz

Počiatočný
dátum

Miesto
konania

Forma

Dĺžka
kurzu

Jazyk

Cena bez DPH

Na vyžiadanie

Self-Paced

365 dní

285 EUR

Registrovať

Garantovaný kurz

Popis kurzu

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

Štruktúra kurzu

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

Predpokladané znalosti

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)

PyTorch Essentials: An Applications-First Approach (LFD273)

Termíny kurzov

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Popis kurzu

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Štruktúra kurzu

Predpokladané znalosti

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