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- PyTorch Tutorial
Curriculum
- 21 Sections
- 103 Lessons
- 10 Weeks
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- PyTorch - Introduction4
- PyTorch - Installation3
- Mathematical Building Blocks of Neural Networks4
- PyTorch - Neural Network Basics3
- Universal Workflow of Machine Learning3
- PyTorch - Machine Learning vs. Deep Learning4
- PyTorch - Implementing First Neural Network8
- PyTorch - Neural Networks to Functional Blocks7
- PyTorch - Loading Data2
- PyTorch - Linear Regression5
- PyTorch - Convolutional Neural Network6
- PyTorch - Recurrent Neural Network7
- PyTorch - Datasets3
- PyTorch - Introduction to Convents2
- PyTorch - Training a Convent from Scratch4
- PyTorch - Feature Extraction in Convents3
- PyTorch - Visualization of Convents4
- PyTorch - Sequence Processing with Convents4
- PyTorch - Word Embedding4
- PyTorch - Recursive Neural Networks2
- PyTorch - Quick Guide21
- 21.1PyTorch – Introduction
- 21.2PyTorch – Installation
- 21.3Mathematical Building Blocks of Neural Networks
- 21.4PyTorch – Neural Network Basics
- 21.5Universal Workflow of Machine Learning
- 21.6PyTorch – Machine Learning vs. Deep Learning
- 21.7PyTorch – Implementing First Neural Network
- 21.8PyTorch – Neural Networks to Functional Blocks
- 21.9PyTorch – Terminologies
- 21.10PyTorch – Loading Data
- 21.11PyTorch – Linear Regression
- 21.12PyTorch – Convolutional Neural Network
- 21.13PyTorch – Recurrent Neural Network
- 21.14PyTorch – Datasets
- 21.15PyTorch – Introduction to Convents
- 21.16PyTorch – Training a Convent from Scratch
- 21.17PyTorch – Feature Extraction in Convents
- 21.18PyTorch – Visualization of Convents
- 21.19PyTorch – Sequence Processing with Convents
- 21.20PyTorch – Word Embedding
- 21.21PyTorch – Recursive Neural Networks
