Welcome to the repository for the Modern Artificial Intelligence course. This course, available on the Udemy platform, offers a comprehensive and practical overview of the fundamentals and applications of artificial intelligence (AI) and deep learning. Here, you will find all the supporting materials, including code notebooks, images, and additional resources needed to follow the course.
The course is divided into several sections, each focusing on a crucial aspect of AI and deep learning:
- Welcome Video
- Introduction to Modern AI and Deep Learning
- Topics Covered in the Course
- Inspiration from Modern AI: The Human Brain
- Basic Processes in Neural Networks
- Backpropagation and Loss Function
- Activation Functions and Non-linearity
- Optimization and Gradient Descent
- Convolutional Neural Networks (CNNs)
- Convolution, Pooling, and ReLU
- Practical Applications of CNNs
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM)
- Financial Forecasting Models
- Transformers
- Positional Encoding and Attention Mechanisms
- Practical Applications in Natural Language Processing (NLP)
- Generative Transformers
- Style Transfer
- Generating Images from Text ("Prompts")
By completing this course, students will be able to:
- Understand the basic concepts of deep neural networks and language generation.
- Evaluate and apply different language models in practical tasks.
- Adapt language models to different languages and contexts.
This course and the provided resources are expected to be valuable in learning about artificial intelligence and inspire continued exploration in this dynamic field.
The code in this repository is licensed under the terms of the MIT license. Refer to the LICENSE file for more details.