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Deep Learning
Master deep learning fundamentals through hands-on projects with neural networks, CNNs, RNNs, and transformers.
12 weeks
14 Modules
Certificate Included
Learning Outcomes
What You'll Learn
By the end of this course, you'll be able to:
Build and train neural networks from scratch
Implement CNNs for image analysis
Create RNNs and LSTMs for sequence data
Use transformer architectures
Apply transfer learning effectively
Debug and optimize deep learning models
Curriculum
Course Modules
A comprehensive curriculum designed for practical application.
1
Neural Network Basics
- Perceptrons
- Activation functions
- Forward propagation
- Backpropagation
2
Deep Learning Frameworks
- PyTorch fundamentals
- Tensor operations
- Automatic differentiation
- GPU acceleration
3
Convolutional Neural Networks
- Convolution layers
- Pooling
- CNN architectures
- Image classification
4
CNN Applications
- Object detection
- Semantic segmentation
- Transfer learning
- Fine-tuning
5
Recurrent Neural Networks
- Sequence modeling
- Vanishing gradients
- LSTMs and GRUs
- Time series forecasting
6
Natural Language Processing
- Text preprocessing
- Word embeddings
- Sequence-to-sequence
- Attention mechanism
7
Transformers Architecture
- Self-attention
- Multi-head attention
- BERT and GPT
- Position encoding
8
Generative Models
- VAEs
- GANs
- DCGANs
- Style transfer
9
Optimization Techniques
- Adam, SGD variants
- Learning rate schedules
- Batch normalization
- Dropout
10
Regularization
- L1/L2 regularization
- Data augmentation
- Early stopping
- Label smoothing
11
Model Debugging
- Gradient analysis
- Loss landscape
- Interpretability
- Common pitfalls
12
Distributed Training
- Multi-GPU training
- Model parallelism
- Mixed precision
- Efficient data loading
13
Reinforcement Learning
- MDPs
- Q-learning
- Policy gradients
- Deep Q-networks
14
Production DL Systems
- Model serving
- ONNX
- Inference optimization
- Monitoring
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