What is Deep Learning?
Deep learning is a subset of Machine learning, which in turn is a subset of artificial intelligence, artificial intelligence is a fashion that enables a machine to mimic mortal gets machine learning is a fashion to achieve AI through algorithms trained with data and eventually. deep learning is a type of machine learning inspired by the structure of the mortal brain in terms of deep learning this structure is called an artificial neural network.
let’s understand deep learning more and how it’s different from machine learning, say we produce a machine that could separate between tomatoes and cherries.
if done using machine learning we would have to tell the Machine the features grounded on which the two can be discerned these features could be the size and therefore the sort of stem on them with deep learning, on the other hand the features are picked out by the neural network without the mortal intervention of the course that kind of independence comes at the cost of having a much advanced the volume of knowledge to coach our machine.
When most people converse with customer support agents, the discussion seems so real they don’t even realize it’s a bot on the other side.
In healthcare, neural networks detect cancer cells and analyze MRI images to provide detailed results.
Self-driving cars, what feels like science fiction, is now a reality. Apple, Tesla, and Nissan are just a few of the companies working on self-driving cars.
Deep learning has a vast scope, but it also faces some limitations. The first, as we discussed earlier, is data. While deep learning is most effective at processing unstructured data, a neural network requires a huge volume of data to train.
Let’s assume we always have access to the necessary amount of data. Processing this isn’t within the capability of every machine, and that brings us to our second limitation: computational power.
Training a neural network requires graphical processing units (GPUs), which have thousands of cores compared to CPUs. GPUs are also more expensive.
Finally, we come to training time. Deep neural networks take hours or even months to train. The time increases with the quantity of data and number of layers within the network.
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Take hours or indeed months to train the time increases with the quantum of data and number of layers within the network future is indeed full of surprises and that is deep learning.
FAQs
1 – What is deep learning and how does it relate to AI?
Deep learning is a type of machine learning, which is itself a part of artificial intelligence. It uses structures called artificial neural networks that are inspired by the human brain.
2 – How is deep learning different from regular machine learning?
In machine learning, humans must specify the features for the computer to look at (like size or color), but in deep learning, the neural network identifies important features on its own without human guidance.
3 – What are some real-world applications of deep learning?
Deep learning powers customer service chatbots, cancer detection in healthcare, MRI image analysis, and self-driving cars from companies like Apple, Tesla, and Nissan.
4 – What are the main limitations of deep learning?
The main limitations are: needing huge amounts of data to train properly, requiring expensive and powerful hardware (GPUs), and taking very long training times (hours or months).
5 – Why do deep learning systems need so much computational power?
Deep learning uses neural networks with many layers that process massive amounts of data. These complex calculations require specialized graphics processing units (GPUs) with thousands of cores, which are more powerful but also more expensive than regular computer processors.