Top 10 Deep Learning concepts explained in very simple terms .

Here are the top ten concepts in Deep Learning explained in very simple terms for a common man to understand. I have got the help of Perplexity.ai in this regard.

Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. Some of the terms used in Deep Learning are explained here in simple terms. (Thanks to ZDnet for the above pic..)

• Neural Networks: Imagine your brain has millions of tiny little cells called neurons that work together to help you think and learn. Deep learning uses artificial neural networks that are inspired by the human brain to help computers learn and understand information.

  

• Layers: In a neural network, the information flows through different layers, just like how your brain processes information in different parts. The first layer takes in the information, the middle layers process it, and the final layer gives the output.
• Activation Functions: These are like the "switches" in the neural network that decide whether a piece of information should be passed on to the next layer or not. They help the network learn complex patterns in the data.
• Backpropagation: This is the process where the neural network learns from its mistakes. It goes back through the layers, adjusting the connections between the neurons to improve its performance.
• Optimization Algorithms: These are the "rules" that the neural network follows to keep improving itself and getting better at the task it's trying to learn.
• Convolutional Neural Networks (CNNs): These are special types of neural networks that are great at recognizing and understanding images. They can spot patterns and features in images that a human might not even notice.
• Recurrent Neural Networks (RNNs): These are good at working with sequential data, like text or speech. They can remember information from earlier parts of the sequence to better understand the whole thing.
• Long Short-Term Memory (LSTMs): These are a special type of RNN that can remember information for a really long time, which is useful for tasks like language translation or speech recognition.
• Generative Adversarial Networks (GANs): These are like two neural networks that compete against each other. One tries to generate new, realistic-looking data, while the other tries to spot the fake stuff. This can be used to create really cool, lifelike images and videos.
• Transfer Learning: This is when you take a neural network that's already been trained on a lot of data and use it as a starting point for a new task. It's like using what you've already learned to help you learn something new, which can be really efficient. 
  

 

                Ref - Deep Learning, John D Kelleher, MIT Press, Cambridge, Mass. ,2019,