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📚 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
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What is Deep Learning & Neural Networks?Lesson
FREE
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The Perceptron & Neural Network ArchitectureLesson
FREE
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Backpropagation & Gradient DescentLesson
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TensorFlow & Keras — Setup and First ModelLesson
02Deep Learning Techniques
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Regularization — Dropout & Batch NormalizationLesson
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Convolutional Neural Networks (CNNs)Lesson
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Transfer Learning — Use Pre-trained ModelsLesson
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Recurrent Neural Networks & LSTMsLesson
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Project — Image Classifier with Transfer LearningProject
03Natural Language Processing with DL
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Word Embeddings — Word2Vec & GloVeLesson
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Text Classification with BERTLesson
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Building a Simple Chatbot with LSTMLesson
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Project — Urdu/English Sentiment AnalyzerProject
04Advanced DL & Real Projects
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Generative AI — GANs & Variational AutoencodersLesson
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Model Optimization — TensorRT, Quantization, PruningLesson
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MLOps — Experiment Tracking with MLflowLesson
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Capstone — Object Detection Web AppProject