Python for Deep Learning

Foundations of Neural Networks:

• Introduction to neural networks and their historical context.
• Basic concepts of perceptrons and activation functions.
• Understanding the structure and functioning of a neural network.

Deep Neural Networks:

• Delving into the architecture of deep neural networks (DNNs).
• Exploring the role of hidden layers and the vanishing/exploding gradient problem.
• Activation functions and their impact on model performance.

Convolutional Neural Networks (CNNs):

• Understanding CNN architecture and its applications in image processing.
• Feature extraction and hierarchical representation in CNNs.
• Hands-on exercises on image classification tasks using CNNs.

Recurrent Neural Networks (RNNs):

• Introduction to sequential data processing with RNNs.
• Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks.
• Applications of RNNs in natural language processing and time-series analysis.

Optimization Techniques:

• Gradient descent and its variants (e.g., stochastic gradient descent).
• Exploring optimization challenges in deep learning.
• Regularization techniques to prevent overfitting.

Transfer Learning and Fine-Tuning:

• Leveraging pre-trained models for specific tasks.
• Fine-tuning models to adapt to new datasets or tasks.
• Practical applications and case studies.

Generative Models:

• Introduction to generative adversarial networks (GANs) and variational autoencoders (VAEs).
• Creating artificial data using GANs.
• Applications in image generation, style transfer, and data augmentation.

Deep Reinforcement Learning:

• Basics of reinforcement learning and its integration with deep learning.
• Q-learning, policy gradients, and deep Q-networks.
• Applications in game playing, robotics, and decision-making.

Ethical Considerations and Future Trends:

• Discussing ethical considerations in deep learning applications.
• Exploring emerging trends in the field.
• The societal impact and responsible use of deep learning.