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Deep Learning with PyTorch

Master deep learning from first principles using PyTorch 2.x. You will build and train neural networks from scratch, implement convolutional and recurrent architectures, apply attention mechanisms, and fine-tune pretrained models for real-world tasks. Every concept is grounded in working Python code and backed by the theory from leading university curricula.

11 hours
0 enrolled
Updated Apr 2026
Course Content ↓
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By CloudaQube Engineering

What You'll Learn

Understand tensors, autograd, and PyTorch computation graphs
Build custom neural networks with nn.Module
Implement full training and validation pipelines with DataLoader
Design and train CNNs for image classification including ResNet
Understand RNNs, LSTMs, vanishing gradients, and self-attention
Fine-tune pretrained torchvision models for custom datasets
Save, load, and deploy PyTorch models

Prerequisites

  • Python proficiency (functions, classes, list comprehensions)
  • NumPy familiarity
  • Basic calculus (derivatives, chain rule)
  • Basic linear algebra (matrices, dot products)
  • Completion of an introductory machine learning course recommended

About the Instructor

C

CloudaQube Engineering

Expert instructor with hands-on industry experience in Ai Ml.

Included in paid plans

LevelAdvanced
Duration11 hours
Lessons
Students0

This course includes

  • Hands-on practice labs
  • AI-powered explanations
  • Progress tracking
  • Certificate of completion
  • Lifetime access
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