Deep Learning
Build and train neural networks from first principles to modern architectures — CNNs for vision, RNNs and LSTMs for sequences, and the Transformers behind today's AI.
https://github.com/waleedkhokar/python-ai-engineering-journey/tree/main/04%20Deep-Learning — so you always land in the right place, never on a guessed sub-folder.What You Will Learn
All Classes
Every class in this course, in order — with exactly what each class teaches. Notes, code and interview prep for each class live in the course folder on GitHub.
Setup & Environment
PyTorch, TensorFlow, GPU / CUDA, Jupyter, the libraries you need.
Deep Learning Fundamentals
AI vs ML vs DL, neural networks, tensors, computational graphs, the deep learning workflow.
Neural Networks Basics
Neurons, layers, weights, bias, forward propagation, nn.Module, nn.Linear.
Activation Functions
Sigmoid, Tanh, ReLU, Leaky ReLU, ELU, Softmax, vanishing gradients.
Loss Functions & Optimizers
MSE, MAE, cross-entropy, SGD, Momentum, Adam, learning-rate schedules.
Backpropagation
The forward pass, gradients, the chain rule, requires_grad, .backward(), the optimizer step.
Regularization
Dropout, weight decay, L1 / L2, early stopping, batch norm, layer norm.
CNNs
Convolution, filters, feature maps, stride, padding, pooling, image classification.
RNNs
Sequential data, hidden state, many-to-one and many-to-many, vanishing gradients.
LSTM & GRU
Forget, input and output gates, cell state, GRU, long-term dependencies.
Transformers
Attention, self-attention, multi-head attention, positional encoding, encoder / decoder.
Transfer Learning
Pretrained models, feature extraction, fine-tuning, ResNet, VGG, EfficientNet.
Autoencoders
Encoder, latent space, decoder, reconstruction, anomaly detection, denoising.
GANs
Generator, discriminator, adversarial training, mode collapse, DCGAN.
Computer Vision
Image preprocessing, augmentation, classification, detection, segmentation, OCR.
NLP with Deep Learning
Embeddings, RNN / LSTM for NLP, text classification, sentiment, seq2seq, attention.
Model Deployment
Save and load models, inference, FastAPI, Docker, batch and real-time inference.
Deep Learning Projects
ANN, CNN, transfer learning and NLP projects, end to end.
Projects in this course
Build them all — each project lives in the course folder on GitHub.
Interview Preparation
Every class in this course includes interview preparation. Open the course folder on GitHub for the full material.
Common Interview Questions
The questions interviewers actually ask on this course's topics, with clear, structured answers.
Open Interview PrepCoding Challenges
Hands-on problems for each class, so you can prove the skill, not just describe it.
Open Interview PrepCommon Mistakes to Avoid
The errors beginners make with these topics — and how professionals avoid them.
Open Interview PrepBest Practices
How working engineers write this code in production: clean, tested and maintainable.
Open Interview Prep