Machine Learning vs. Deep Learning
Apa bedanya? Memahami Feature Extraction dan Representasi Data.
What Is a Neural Network
Anatomi Neuron, Hidden Layers, dan Forward Propagation.
Loss, Backprop, Optimization
Mekanisme belajar: Mencari kesalahan dan memperbaikinya.
How Training Works
Terminologi & Proses Pelatihan (Epoch, Batch, Iteration).
Performance Metrics
Rapor Kinerja AI: Confusion Matrix, Precision, Recall.
Overfitting & Regularization
Musuh Terbesar AI: "Menghafal" vs "Memahami".
Why Do We Need Convolution?
Masalah Flattening, Ledakan Parameter, dan Spatial Invariance.
How Does a CNN Work?
Convolution Layers, Filters, Max Pooling, dan Arsitektur Final.
Sequences and Time
RNN untuk Data Berurutan, Vanishing Gradient, dan Solusi LSTM/GRU.
Autoencoders
Dimensionality Reduction, Denoising, dan Latent Space.
Transformers
Self-Attention, Parallelization, dan Era GPT.
Normalization & Initialization
He Init, Xavier Init, dan Batch Normalization.
Data Augmentation
Memperkaya data Image, Text, dan Audio.
Advanced Optimization
Beyond SGD: Adam, RMSprop, dan Learning Rate Scheduling.
Explainability (XAI)
Membuka "Kotak Hitam" AI: Saliency Maps, SHAP, LIME.
TensorFlow vs PyTorch
Perbandingan Dua Raksasa Framework: Industri vs Riset.
Google Colab Guide
Akses GPU Gratis, Magic Commands, dan Integrasi Drive.
Mixed Precision Training
FP16 vs FP32: Training 3x Lebih Cepat.
Transfer Learning
Memakai "Otak" Model Lain (ResNet, BERT).
Model Management
Saving (.h5/.pt), Versioning, dan ONNX.
Deployment & Serving
FastAPI, Docker, dan TFLite.
Course Completed!
Anda telah mempelajari seluruh 21 Modul Deep Learning. Selamat belajar!