Deep Learning with PyTorch: From Zero to Production
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AI & Machine Learning
intermediate

Deep Learning with PyTorch: From Zero to Production

4.9
8,432 students
6h 15m
25 lessons

About this course

Master deep learning from the ground up using PyTorch. This course takes you from understanding neural network fundamentals through building CNNs, RNNs, Transformers, and deploying models to production. You will work on real-world projects including image classification, sentiment analysis, and generative AI. Each lesson includes hands-on coding exercises with GPU-accelerated notebooks.

Course Content

Foundations of Neural Networks

  • What is Deep Learning? History and Key Concepts15min
  • Setting Up PyTorch and GPU Environment15min
  • Tensors, Autograd, and Computational Graphs15min
  • Building Your First Neural Network from Scratch15min
  • Loss Functions and Optimization Explained15min

Training and Debugging Models

  • Gradient Descent, Learning Rates, and Schedulers15min
  • Overfitting, Regularization, and Dropout15min
  • Batch Normalization and Weight Initialization15min
  • Debugging Neural Networks: Common Pitfalls15min
  • Hyperparameter Tuning Strategies15min

Convolutional Neural Networks

  • How Convolutions Work: Filters, Stride, Padding15min
  • Classic Architectures: LeNet, AlexNet, VGG15min
  • ResNet, EfficientNet, and Transfer Learning15min
  • Project: Image Classification on CIFAR-1015min
  • Data Augmentation and Mixed Precision Training15min

Sequence Models and NLP

  • RNNs, LSTMs, and GRUs Explained15min
  • Attention Mechanisms and Self-Attention15min
  • Transformers Architecture Deep Dive15min
  • Fine-Tuning BERT for Text Classification15min
  • Project: Sentiment Analysis Pipeline15min

Generative AI and Advanced Topics

  • Variational Autoencoders and Diffusion Models15min
  • Building a Simple GPT from Scratch15min
  • Model Optimization: Quantization, Pruning, ONNX15min
  • Deploying Models with TorchServe and FastAPI15min
  • Generative Adversarial Networks (GANs)15min