Neural Nets
- Introduction to Machine Learning
- Introduction to Neural Networks
- Machine Learning Foundations
- Deep Learning Overview
- Understanding Large Language Models (LLMs)
- Implementing Machine Learning and Deep Learning Concepts
- Underlying Technology behind LLMs
- Training LLMs
- Advanced Topics in LLMs
- Case Studies of LLM Applications
- Future Trends in Machine Learning and LLMs
Deep Learning Overview
What is Deep Learning?
Branch of machine learning.
Deep Learning is a subset of machine learning, which in turn is a subset of artificial intelligence (AI). It is a field that is based on learning and improving on its own by examining computer algorithms. While machine learning uses complex inductive and deductive rules, deep learning learns through an artificial neural network that mimics the workings of a human brain.
Deep learning models are built using neural networks that consist of several layers. These layers are the 'deep' in deep learning. Each layer learns to transform its input data into a slightly more abstract and composite representation.
Hierarchical Feature Learning
One of the key aspects of deep learning is hierarchical feature learning. In traditional machine learning, most of the applied features need to be identified and coded by hand. On the other hand, deep learning algorithms try to learn high-level features from data in an incremental manner. This is a more efficient way of learning and can lead to better performance and functionality.
For example, in image processing, a deep learning model could learn to identify edges from raw pixels in the first layer, then use the edges to learn more complex shapes in the second layer, and so on. This layered approach allows the model to learn complex functions that map the input data to the output data, without any need for manual feature extraction.
The Importance of Deep Learning
Deep learning is becoming increasingly important in today's world. It is driving AI innovation in industries like healthcare, where it's used to make more accurate diagnoses, and in the automotive industry, where it's used in the development of self-driving cars.
Deep learning also plays a crucial role in voice control in consumer devices like phones, tablets, TVs, and hands-free speakers, which are becoming more prevalent and are expected to drive future consumer behavior.
In conclusion, deep learning is a powerful tool for solving complex problems. It's a field that has a lot to offer and is worth understanding due to its vast potential applications.