DATA HARVEST DAILY DEEP LEARNING
Deep learning can be very powerful; it requires data, computing power and careful checking. It can also make mistakes if its training data is incomplete or inaccurate
Deep Learning (DL) is a way of teaching a computer to make decisions by learning from examples instead of following only fixed rules
If the shape has one loop, maybe it is 3.
Artificial Intelligence: computers doing tasks that seem intelligent
Machine learning: computers learning from data
Deep Learning: Machine learning with multi-layer neural networks
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Types of deep learning networks
Convolutional Neural Networks (CNNs)
CNNs are mainly used for images and videos. They can detect objects, faces, and medical conditions in scans and handwritten text.
Recurrent Neural Networks (RNNs)
RNNs are designed for sequences, such as sentences and speech, or stock-price history. They remember some earlier information while processing the next item.
Transformers
Transformers are widely used for languages and modern AI tools such as chatbots. They can understand relationships between words and handle long pieces of text more effectively. ChatGPT is based on a transformer; some earlier
Generative Adversarial Networks (GAANs)
GANs can create new content, such as realistic-looking faces, artwork, or product images. One network generates content while another checks whether it looks real.
Real-life Applications
Deep Learning is used in many everyday systems
