In the Recent Trends in the Field of Artificial Intelligence (AI), DeepLearning has gained more popularity in Reasearch over the years compared to Traditional Machine Learning.
The Question is Why? If you would like to know continue Reading 🙂
Firstly, Deep learning is a subset of machine learning which provides the ability to machine to perform human-like tasks without human involvement. It provides the ability to an AI agent to mimic the human brain. Deep learning can use both supervised and unsupervised learning to train an AI agent. Deep learning is implemented through neural networks architecture hence also called a deep neural network. Deep learning is the primary technology behind self-driving cars, speech recognition, image recognition, automatic machine translation, etc. The main challenge for deep learning is that it requires lots of data with lots of computational power.
Getting an overview of DeepLearning, Lets go into the reasons
Exponential Growth in Data
In the recent years Data was generated more and more because of the use of gadgets and other IOT devices to connect to the internet as well as social media ( Facebook, Instagram , Youtube, Etc… ), all these and many more generated a lot of data, if you recall in the beginning, I said that one of the major challenges of DeepLearning is that it requires a lot of Data, so you can see the challenge it was facing before now turns to its projector 💪. With that amount of Data companies have utlilized the opportunity of potential growth in their business. Examples like Recommendation system ( Neflix, Youtube, Prime Video, etc…), Face Detection Systems and a lot more.
The Diagram above explains the explains it better, The graph shows us the relationship between performance of the Algorithm and Amount of Data. As you can see the traditional machine learning algorithms curve flattens when the data gets large, but the curves of te Deeplearning algorithms surpasses it in performance notwithstanding the amount of Data, which is very good.
Increase in Technology / Computing Power
Technology is increasing at a really high rate, at the moment we can get really amazing hardware ( GPU’s ) for a decent amount compared to previous years, this has made it possible for companies to setup Data Centers where they can train models on huge datasets, not to forget the Cloud Service Providers (Azure, AWS, GCP) which also help provide more access to high level computing power at really decent fees.
DeepLearning covers less steps than Traditional Machine Learning
In Traditional Machine Learning, we basically go through a life cycle of Data Collection -> Feature Extraction -> Model Training-> Output, but in the case of DeepLearning The whole Process of Feature Extraction and model training are Combined in the Deep Learning Algorithm.
This is the Reason that DeepLearning can really solve complex problems like; Natural Language Processing (NLP) , Object/Image Detection, Chatbots Systems Etc…. simply because it has a lot of Data and it uses Deep Neural Network ( The more Hidden Layers -> The more the Neural Network gets deep 👌).
If you made it till here, I Hope you found this Helpful. 🙂
Thanks for Reading ✌️…