Lilly Tech Systems
Learn Deep Learning
Master the foundations of deep learning — from neural networks and CNNs to Transformers and modern frameworks. Build, train, and deploy deep learning models with hands-on examples.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is deep learning? Its relationship to ML and AI, history, and why it works now.
2. Neural Networks
Artificial neurons, activation functions, layers, forward propagation, and backpropagation.
3. CNNs
Convolutional Neural Networks for image processing, filters, pooling, and transfer learning.
4. RNNs & LSTMs
Recurrent networks for sequential data, LSTMs, GRUs, and handling time series.
5. Transformers
Self-attention, multi-head attention, BERT, GPT, and the transformer revolution.
6. Training & Optimization
Hyperparameters, optimizers, regularization, data augmentation, and GPU training.
7. Frameworks
PyTorch, TensorFlow, JAX, Hugging Face, and model deployment tools.
8. Best Practices
Architecture selection, debugging, reproducibility, ethics, and common pitfalls.
What You'll Learn
By the end of this course, you'll be able to:
Understand Neural Networks
Know how neurons, layers, and backpropagation work together to learn from data.
Build Deep Learning Models
Implement CNNs, RNNs, and Transformers using PyTorch and TensorFlow.
Train and Optimize
Apply best practices for training: learning rate schedules, regularization, and GPU acceleration.
Deploy Models
Export and serve models using ONNX, TorchScript, and cloud platforms.