Packt
PyTorch Ultimate 2024 - From Basics to Cutting-Edge Specialization
Packt

PyTorch Ultimate 2024 - From Basics to Cutting-Edge Specialization

Master Deep Learning with PyTorch. Master deep learning using PyTorch with the help of this comprehensive and practical course. Develop, deploy, and innovate with models in Regression, CNNs, GANs, NLP, Recommender Systems, Transformers, and more.

Packt

Instructor: Packt

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Get in-depth knowledge of a subject
Intermediate level

Recommended experience

1 month
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

1 month
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Identify and describe the fundamental concepts of PyTorch, machine learning, and deep learning, including neural networks.

  • Implement PyTorch models for tasks such as classification and regression, incorporating principles of deep learning.

  • Evaluate and optimize model performance using metrics and techniques for underfitting, overfitting, and hyperparameter tuning.

  • Develop advanced AI applications using GANs, CNNs, and RNNs, and deploy models effectively.

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Taught in English
Recently updated!

September 2024

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Specialization - 3 course series

What you'll learn

  • Set up and configure a PyTorch environment.

  • Understand fundamental AI and machine learning concepts.

  • Build, train, and evaluate neural networks from scratch, utilizing various optimization techniques

  • Apply PyTorch to real-world deep learning tasks.

Skills you'll gain

Category: Deep Learning
Category: Machine Learning
Category: PyTorch (Machine Learning Library)
Category: neural network

What you'll learn

  • Build and train neural networks using PyTorch for various tasks.

  • Implement classification models with multi-class, multi-label datasets, and CNNs for image and audio classification.

  • Apply object detection techniques using the YOLO algorithm.

  • Explore neural style transfer, transfer learning, and implement RNNs and LSTM networks.

Skills you'll gain

Category: Recurrent Neural Network (RNN)
Category: PyTorch (Machine Learning Library)
Category: CNN
Category: YOLO
Category: Classification Models

What you'll learn

  • Create and assess ML models for specific datasets, evaluating performance with proper metrics.

  • Design autoencoders for dimensionality reduction and build GANs for data simulation, analyzing quality.

  • Develop Graph Neural Networks for graph data and implement Transformers, including Vision Transformers.

  • Enhance models with semi-supervised learning using limited data, and deploy them with Flask on Google Cloud.

Skills you'll gain

Category: Transformers
Category: Autoencoders
Category: Recommender Systems
Category: PyTorch Lightning
Category: GANs

Instructor

Packt
Packt
231 Courses3,858 learners

Offered by

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