Growth Hub for Opportunities, Skills, Learning & AchievementGrow•Learn•AchieveDeep Learning Basics with PyTorch
Neural networks, training loops, CNNs and transfer learning in PyTorch, all runnable free on Google Colab
Ghosla Academy · AI Team
Deep learning powers image search, voice typing, recommendations and chat assistants, and PyTorch is one of the most widely used libraries for building such models. This course explains what a neural network really does using small numeric examples first, then moves to real PyTorch code: tensors, autograd, nn.Module, DataLoader and the training loop that every project uses. You then train networks on tabular and image data, learn to spot and fix overfitting, build a convolutional neural network for Fashion-MNIST and fine-tune a pretrained model on your own phone photos. Everything runs free in Google Colab, so a basic laptop is enough. Explanations are in simple English with occasional Hinglish.
What you will be able to do
- Explain neurons, activations, loss functions and gradient descent with simple numbers
- Create and reshape tensors and use autograd to compute gradients in PyTorch
- Write a complete training loop with nn.Module, DataLoader, an optimiser and validation
- Diagnose overfitting and apply dropout, weight decay and early stopping
- Build a CNN for image classification and fine-tune a pretrained model with transfer learning
- Save, load and run a trained model, and understand where embeddings, RNNs and transformers fit
What you build
- Neural network that predicts monthly flat rent from simple features
- Transfer-learning image classifier trained on photos you take with your phone
What you need to start
- Comfortable with Python basics; some NumPy helps
- Class 11–12 level maths idea of slope (no heavy calculus needed)
- A laptop with a browser and a free Google account for Google Colab
Curriculum · 5 sections, 20 lectures
Neural Network Foundations4 lectures
- What Deep Learning Is and Where It Is UsedFree preview12 min
- Neurons, Weights, Activations and LossFree preview14 min
- Gradient Descent Explained with Numbers12 min
- Quiz: Neural Network Foundations6 min
PyTorch Essentials4 lectures
- Tensors: Shapes, Operations and GPU in Colab15 min
- Autograd: How PyTorch Computes Gradients13 min
- Dataset and DataLoader12 min
- Quiz: PyTorch Essentials6 min
Training Your First Networks4 lectures
- nn.Module and the Training Loop16 min
- Overfitting, Validation and Regularisation12 min
- Project: Predict Monthly Flat Rent with a Neural Network20 min
- Quiz: Training Networks7 min
Convolutional Neural Networks4 lectures
- Convolutions, Pooling and Feature Maps15 min
- Building a CNN for Fashion-MNIST15 min
- Transfer Learning with Pretrained Models12 min
- Quiz: Convolutional Neural Networks7 min
Sequences, Deployment and Next Steps4 lectures
- Embeddings, RNNs and a First Look at Transformers15 min
- Saving, Loading and Serving a Model11 min
- Project: Classify Your Own Phone Photos with Transfer Learning20 min
- Quiz: Sequences and Deployment7 min
Who this is for
- Students and freshers who know basic machine learning and want to start deep learning
- Python developers curious about how neural networks are actually trained
- Engineering and science students preparing AI projects for internships or final year