Frequently Asked Questions (FAQs)
1. What is the difference between AI, ML, and DL?
AI is the broader concept. Machine Learning (ML) is a subset of AI. Deep Learning (DL) is a subset of ML that uses neural networks with many layers.
2. What are neural networks?
Neural networks are algorithms modeled after the human brain that are designed to recognize patterns and solve complex problems.
3. What is a deep neural network?
A deep neural network is a neural network with multiple hidden layers, allowing it to learn complex features from data.
4. Which languages are used in Deep Learning?
Primarily Python is used, along with libraries like TensorFlow, PyTorch, and Keras.
5. What are common deep learning frameworks?
Popular frameworks include TensorFlow, PyTorch, Keras, and Theano.
6. What hardware is needed for deep learning?
GPUs (Graphics Processing Units) are highly recommended for training deep learning models faster.
7. Can I learn deep learning without coding?
Basic coding is necessary, especially Python. However, platforms like Teachable Machine can help beginners experiment without coding.
8. What is the role of activation functions?
Activation functions introduce non-linearity into neural networks, helping them learn complex patterns.
9. What is overfitting in deep learning?
Overfitting occurs when a model learns the training data too well, including its noise, and performs poorly on new data.
10. Is deep learning used in ChatGPT?
Yes. ChatGPT is based on large-scale deep learning models known as transformer architectures (like GPT-4).