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+--- Thread: PyTorch Ultimate 2023: From Basics to Cutting-Edge (/Thread-PyTorch-Ultimate-2023-From-Basics-to-Cutting-Edge)
PyTorch Ultimate 2023: From Basics to Cutting-Edge - BaDshaH - 10-30-2023
![[Image: 913f7ed2cac070735b16ba01d3271e44.jpg]](https://i122.fastpic.org/big/2023/1030/44/913f7ed2cac070735b16ba01d3271e44.jpg)
Last updated 10/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 18h 8m | Size: 5.7 GB
Become an expert applying the most popular Deep Learning framework PyTorch
[b]What you'll learn[/b]
learn all relevant aspects of PyTorch from simple models to state-of-the-art models
deploy your model on-premise and to Cloud
Transformers
Natural Language Processing (NLP), e.g. Word Embeddings, Zero-Shot Classification, Similarity Scores
CNNs (Image-, Audio-Classification; Object Detection)
Style Transfer
Recurrent Neural Networks
Autoencoders
Generative Adversarial Networks
Recommender Systems
adapt top-notch algorithms like Transformers to custom datasets
develop CNN models for image classification, object detection, Style Transfer
develop RNN models, Autoencoders, Generative Adversarial Networks
learn about new frameworks (e.g. PyTorch Lightning) and new models like OpenAI ChatGPT
use transfer learning
[b]Requirements[/b]
basic Python knowledge
[b]Description[/b]
learn all relevant aspects of PyTorch from simple models to state-of-the-art models
deploy your model on-premise and to Cloud
Transformers
Natural Language Processing (NLP), e.g. Word Embeddings, Zero-Shot Classification, Similarity Scores
CNNs (Image-, Audio-Classification; Object Detection)
Style Transfer
Recurrent Neural Networks
Autoencoders
Generative Adversarial Networks
Recommender Systems
adapt top-notch algorithms like Transformers to custom datasets
develop CNN models for image classification, object detection, Style Transfer
develop RNN models, Autoencoders, Generative Adversarial Networks
learn about new frameworks (e.g. PyTorch Lightning) and new models like OpenAI ChatGPT
use transfer learning
Who this course is for
Python developers willing to learn one of the most interesting and in-demand techniques
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