Course Content
Introduction to PyTorch and Deep Learning
0/3
Overview of PyTorch
Deep Learning Fundamentals
PyTorch Basics
Data Handling and Preprocessing with PyTorch
0/3
Working with Tensors
Dataset Preparation using torch.utils.data
Data Augmentation and Normalization
Building Machine Learning Models in PyTorch
0/3
Implementing Linear and Logistic Regression
Neural Networks with torch.nn
Training and Evaluating Models
Convolutional Neural Networks (CNNs) for Computer Vision
0/3
Introduction to Convolutional Networks
Implementing CNNs in PyTorch
Data Augmentation and Image Processing
Recurrent Neural Networks (RNNs) and NLP
0/3
Understanding Sequence Models
Text Processing with PyTorch
Implementing Time Series Forecasting
Advanced Deep Learning Architectures
0/3
Generative Adversarial Networks (GANs)
Autoencoders and Anomaly Detection
Transformer Models for NLP
Model Deployment and Optimization
0/3
Model Optimization Techniques
Deploying PyTorch Models
Running PyTorch on Edge Devices
Capstone Project, Assessment & Certification
0/3
Final Deep Learning Project
Practical Assignments & Case Studies
Certification Exam
PyTorch for Deep Learning
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