Laying the Foundation for Deep Learning

Overview

Explore the fundamentals of deep learning and its potential impact on technology with insights from Airbnb's Mike Curtis at Collision Conference.

Syllabus

• Introduction to Deep Learning
Definition and Key Concepts
Historical Background and Evolution
Overview of Deep Learning Architectures
• The Mathematics of Deep Learning
Linear Algebra Essentials
Calculus and Optimization Techniques
Probability and Statistics for Deep Learning
• Neural Networks
Structure and Function of Artificial Neurons
Understanding Layers and Activation Functions
Training Neural Networks: Forward and Backpropagation
• Deep Learning Frameworks
Introduction to Popular Frameworks (TensorFlow, PyTorch, etc.)
Setting Up and Configuring a Deep Learning Environment
Building Your First Neural Network with a Framework
• Convolutional Neural Networks (CNNs)
Architecture and Applications of CNNs
Image Processing and Recognition
Case Study: Applications in Industry
• Recurrent Neural Networks (RNNs)
Understanding Sequence Modeling with RNNs
Applications in Natural Language Processing
Long Short-Term Memory (LSTM) Networks
• Ethical and Social Implications of Deep Learning
Bias and Fairness in AI
Deep Learning and Privacy Concerns
The Role of Deep Learning in Society
• Deep Learning for Industry
Insights from Mike Curtis: Airbnb's Use of Deep Learning
Potential Impact on Various Sectors
The Future of Deep Learning in Technology
• Capstone Project
Designing, Training, and Deploying a Deep Learning Model
Real-world Applications and Case Studies
Presentation and Evaluation of Projects
• Conclusion and Future Directions
Recap of Key Concepts and Skills
Emerging Trends in Deep Learning
Resources for Further Learning and Exploration

Subjects

Conference Talks