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Intermediate
Deep Learning
Dive into deep neural networks, backpropagation, optimization techniques, and modern architectures including CNNs, RNNs, Transformers, and GANs.
20-30 hours total18 modulesCertificate included
Course Modules
1
Introduction to Deep Learning
What makes deep learning special
30 min
2
Neural Network Fundamentals
Neurons, layers, and activations
45 min
3
Backpropagation
The math behind learning
60 min
4
Optimization Algorithms
SGD, Adam, RMSprop explained
50 min
5
Regularization Techniques
Dropout, batch norm, weight decay
40 min
6
Convolutional Neural Networks
Architecture and applications
90 min
7
CNN Architectures
VGG, ResNet, EfficientNet
60 min
8
Recurrent Neural Networks
Sequential data processing
75 min
9
LSTM and GRU
Long-term dependencies
60 min
10
Attention Mechanisms
The foundation of transformers
70 min
11
Transformer Architecture
Self-attention and positional encoding
90 min
12
BERT and GPT
Pre-trained language models
80 min
13
Generative Adversarial Networks
Generator vs discriminator
75 min
14
Variational Autoencoders
Latent space representations
60 min
15
Transfer Learning
Leveraging pre-trained models
45 min
16
Model Interpretability
Understanding DL decisions
50 min
17
Deep Learning at Scale
Distributed training strategies
55 min
18
Capstone Project
Build an end-to-end DL system
120 min