Content of the course

Python Fundamentals :

• Introduction to Python
• Installing Python and the necessary libraries
• Basic Programming Concepts
  • Variables and Data Types
  • Operators
  • Classes and Objects
  • Functions
  • Control Structures (if/else statements, loops)
• NumPy and Pandas
  • Introduction to NumPy and Pandas
  • Arrays and Matrices in NumPy
  • DataFrames and Series in Pandas
• Matplotlib
  • Introduction to data visualization
  • Using Matplotlib for data visualization
  • Matplotlib Plots
• Working with Files and Data
  • Reading and writing files in Python
  • Reading and writing files using Pandas
• Parsing and cleaning data
• Preparing data for machine learning algorithms

Mathematics Fundamentals

• Linear Algebra

  • Scalars, Vectors, Matrices, and Tensors
  • Matrix operations
  • Vector operations
  • Eigenvalues and Eigenvectors

• Calculus

  • Limits and Continuity
  • Derivatives
  • Gradient and Partial Derivatives
  • Optimization using Gradient Descent

• Probability and Statistics

  • Probability Theory
  • Distributions (Gaussian, Poisson, etc.)
  • Hypothesis Testing and Confidence Intervals
  • Bayesian Statistics

Deep Learning Fundamentals

• Introduction to Deep Learning
  • What is deep learning?
  • Why is deep learning important?
  • History of deep learning
  • Applications of deep learning
  • Deep learning vs machine learning
• Fundamentals of Neural Networks
  • Artificial neurons
  • Activation functions
  • Forward propagation
  • Backpropagation
  • Loss functions
  • Optimizers

Deep Learning Architectures

• Deep Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• Autoencoders
• Generative Adversarial Networks
• Deep Reinforcement Learning

Practical Deep Learning

1. Data preparation and preprocessing
2. Model selection and evaluation
3. Hyperparameter tuning
4. Transfer learning
5. GPU acceleration

Advanced Topics in Deep Learning :

1. Deep Learning for Natural Language Processing
2. Deep Learning for Computer Vision
3. Deep Learning for Time Series Analysis
4. Explainable AI
5. Ethical considerations in deep learning

Project-Based Learning

1. Applying deep learning to real-world problems
2. Project-based learning on a dataset of choice
3. Discussion and feedback on projects
4. Conclusion and Future Directions
5. Recap of key concepts
6. Future of deep learning
7. Challenges and opportunities in deep learning