Introduction
Deep Learning has revolutionized modern AI by enabling breakthroughs in image recognition, natural language, robotics, and more. This course explores the architecture and training principles behind neural networks. Participants will learn how deep learning models extract hierarchical features and why they outperform traditional ML in many domains. With practical examples, attendees will understand how to design and evaluate networks effectively. By the final day, participants will have built and trained simple deep learning models.
Course Objectives
- Understand neural networks and their components
- Learn optimization techniques and loss functions
- Explore common deep learning architectures
- Practice building models using modern frameworks
- Gain insight into training challenges and solutions
Target Audience
- Developers and analysts with basic ML knowledge
- Students studying AI or data science
- Engineers transitioning into deep learning
- Technical professionals who need hands-on experience
- Anyone interested in neural networks
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
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- Day 1: Neural Network Basics• Perceptron model
• Layers, weights, biases
• Activation functions
• Forward and backward propagation
• Building a simple network0 - Day 2: Training Neural Networks• Loss functions
• Gradient descent variants
• Learning rate strategies
• Regularization techniques
• Hands-on: Tuning a small model0 - Day 3: Convolutional Neural Networks (CNNs)• Convolutional layers
• Kernels and feature maps
• Pooling layers
• Image classification basics
• Hands-on: Build a CNN0 - Day 4: Recurrent Neural Networks (RNNs)• Sequence modeling
• LSTM and GRU networks
• Use cases in NLP and time-series
• Common challenges
• Hands-on: Build an RNN0 - Day 5: Model Optimization & Deployment• Transfer learning
• Monitoring model training
• Deployment basics
• Using GPUs for training
• Capstone project0
Instructor
Lessons:
