▶
📚 What you'll learn
✓Neural network architecture and backpropagation
✓Build CNNs for image classification
✓Transfer learning with EfficientNet and ResNet
✓RNNs and LSTMs for sequence data
✓BERT and transformers for NLP tasks
✓GANs and generative models
✓Model optimization: quantization, pruning
✓Deploy models as web apps with Gradio
Course Curriculum
01Introduction to Deep Learning
▾
🎬
What is Deep Learning & Neural Networks?Video · 20:00
FREE
🎬
The Perceptron & Neural Network ArchitectureVideo · 28:00
FREE
🎬
Backpropagation & Gradient DescentVideo · 35:00
🎬
TensorFlow & Keras — Setup and First ModelVideo · 30:00
02Deep Learning Techniques
▾
🎬
Regularization — Dropout & Batch NormalizationVideo · 28:00
🎬
Convolutional Neural Networks (CNNs)Video · 40:00
🎬
Transfer Learning — Use Pre-trained ModelsVideo · 35:00
🎬
Recurrent Neural Networks & LSTMsVideo · 35:00
🛠️
Project — Image Classifier with Transfer LearningProject · 90:00
03Natural Language Processing with DL
▾
🎬
Word Embeddings — Word2Vec & GloVeVideo · 30:00
🎬
Text Classification with BERTVideo · 38:00
🎬
Building a Simple Chatbot with LSTMVideo · 32:00
🛠️
Project — Urdu/English Sentiment AnalyzerProject · 90:00
04Advanced DL & Real Projects
▾
🎬
Generative AI — GANs & Variational AutoencodersVideo · 38:00
🎬
Model Optimization — TensorRT, Quantization, PruningVideo · 28:00
🎬
MLOps — Experiment Tracking with MLflowVideo · 25:00
🛠️
Capstone — Object Detection Web AppProject · 120:00