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POLICY UPDATE: Senior leaders from the world’s leading research..
PyTorch for Deep Learning
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Course Content
Introduction to PyTorch and Deep Learning
Overview of PyTorch
Deep Learning Fundamentals
PyTorch Basics
Data Handling and Preprocessing with PyTorch
Working with Tensors
Dataset Preparation using torch.utils.data
Data Augmentation and Normalization
Building Machine Learning Models in PyTorch
Implementing Linear and Logistic Regression
Neural Networks with torch.nn
Training and Evaluating Models
Convolutional Neural Networks (CNNs) for Computer Vision
Introduction to Convolutional Networks
Implementing CNNs in PyTorch
Data Augmentation and Image Processing
Recurrent Neural Networks (RNNs) and NLP
Understanding Sequence Models
Text Processing with PyTorch
Implementing Time Series Forecasting
Advanced Deep Learning Architectures
Generative Adversarial Networks (GANs)
Autoencoders and Anomaly Detection
Transformer Models for NLP
Model Deployment and Optimization
Model Optimization Techniques
Deploying PyTorch Models
Running PyTorch on Edge Devices
Capstone Project, Assessment & Certification
Final Deep Learning Project
Practical Assignments & Case Studies
Certification Exam
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