Deep Learning Course Index – Dataplexa

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Deep Learning Course Index

Complete Deep Learning roadmap — foundations, neural networks, CNNs, RNNs, LSTMs, GRUs, attention, transformers, and modern AI architectures. Perfect for ML engineers, researchers, and real-world applications.

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I. Deep Learning Foundations (15 Lessons)

1. What is Deep Learning? 2. Biological vs Artificial Neurons 3. Neural Network Architecture 4. Activation Functions 5. Forward & Backpropagation 6. Loss Functions 7. Gradient Descent Variants 8. Weight Initialization 9. Bias & Variance 10. Overfitting & Underfitting 11. Regularization 12. Dropout 13. Batch Normalization 14. Optimization Algorithms 15. Training Pipeline

II. Neural Network Models (15 Lessons)

16. Perceptron 17. Multi-Layer Perceptron 18. Initialization Strategies 19. Hyperparameter Tuning 20. Epochs, Batches, LR 21. Early Stopping 22. Vanishing Gradients 23. Evaluation Metrics 24. Confusion Matrix 25. ROC & AUC 26. Keras Sequential API 27. PyTorch Basics 28. TensorFlow Fundamentals 29. Build NN from Scratch 30. Gradient Checking

III. Convolutional Neural Networks (15 Lessons)

31. Intro to CNNs 32. Convolution Operation 33. Pooling Layers 34. Padding & Stride 35. Feature Maps 36. Classic CNNs 37. LeNet 38. AlexNet 39. VGG 40. ResNet 41. Transfer Learning 42. Fine-Tuning Models 43. Image Augmentation 44. CNN Architectures 45. Build CNN in TF/Keras

IV. Sequence Models (15 Lessons)

46. Intro to RNNs 47. Vanishing Gradients 48. LSTM Networks 49. GRU Networks 50. Bidirectional RNNs 51. Encoder-Decoder 52. Seq2Seq 53. Attention Mechanism 54. Transformers Intro 55. Positional Encoding 56. BERT Overview 57. GPT Overview 58. Training Large Models 59. Beam Search 60. Applications