Deep Learning

What is Deep Learning?

Image Credits

Deep learning is a part of machine learning and AI. It uses neural networks with lots of layers. These layers copy how the human brain works. This lets computers learn from big data sets. Deep learning models especially deep neural networks (DNNs), learn and get better on their own. They don't need specific programming for each task.

Deep learning models have different designs. Some common types are:

• Convolutional Neural Networks, or CNNs handle images and videos. They use special layers to spot patterns in data at different levels. LeCun, Bengio, and Hinton talked about this in 2015. CNNs are pretty good at figuring out what's in a picture or video.
• Recurrent Neural Networks also called RNNs, work well with stuff that happens in order. They're great for guessing what comes next in a series or understanding language. RNNs use loops to remember things from before. Mikolov and some other folks wrote about this back in 2010. These networks have an impact on tasks like predicting stock prices or translating languages.
• Generative Adversarial Networks, or GANs, use two neural networks. They have a generator and a discriminator. GANs create realistic fake data. Goodfellow and others introduced them in 2014. These networks work together to produce synthetic info that looks real.

‍

Why is Deep Learning important?

Deep learning matters a lot these days. It can handle huge amounts of messy data that old-school machine learning can't deal with. Here's why deep learning is such a big deal: Deep learning models rock at handling massive amounts of data. As data grows like crazy messy stuff like pictures, sound, and text deep learning gives us the tools to make sense of it all. It's like having a super-smart friend who can sift through mountains of information and tell you what's important (Deng & Yu, 2014).

Deep learning has an impact on AI big time. It's pushing the boundaries in things like computer vision, speech recognition, and understanding human language. It's pretty wild - some deep learning methods are now as good as humans, or even better, at spotting what's in a picture (He et al. 2016). It's like teaching computers to see and understand the world the way we do, but sometimes they're even sharper than us!

Deep learning models often beat traditional machine learning models in accuracy and efficiency for complex tasks. They do this because they can learn layered representations and tricky patterns in data. This gives them an edge over older methods. Unlike old-school models that need humans to pick out important features deep learning models can do this on their own from raw data. This makes building models easier and helps them work better. It's like the models can figure out what's important without being told. These improvements come from the way deep learning works. It's not just about being more accurate - it's about being smarter in how it handles data. This automation in feature extraction is a big deal. It saves time and often leads to better results. Researchers have been studying this for years. They've found that deep learning's ability to learn on its own is key to its success. It's changing how we approach many problems in AI and machine learning.

‍

What are benefits of using Deep Learning?

Deep learning brings lots of good stuff when you use it in different areas.

Deep learning models boost prediction accuracy. This leads to smarter choices in health, money, and self-driving cars. Esteva and others showed this in 2017. Deep learning can handle big data and lots of computer power. This makes it great for huge tasks and quick processing. Krizhevsky, Sutskever, and Hinton proved this back in 2012. You can use deep learning for many different jobs. It's good at spotting images and voices. It also gets language and can play games. Silver and his team wrote about this in 2016.  Deep learning models can keep getting better as they get more data. They adapt to new info and changing trends. This happens all the time (LeCun et al. 2015). Deep learning has an impact on creating new apps. These include personal suggestions, AI helpers, and smart robots. It's causing big tech changes in many fields (Goodfellow et al. 2016).

‍

How Alltius AI Enables Organizations to use Deep Learning?

Alltius' provides leading enterprise AI technology for enterprises and governments to harness and extract value from their current data using variety of technologies. ‍

Alltius' Gen AI platform enables companies to create, train, deploy and maintain AI assistants for sales, support agents and customers in a matter of a day. Alltius platform is based on 20+ years of experience at leading researchers at Wharton, Carnegie Mellon and University of California and excels in improving customer experience at scale using Gen AI assistants catered to customer's needs. Alltius' successful projects included but are not limited to Insurance(Assurance IQ), SaaS (Matchbook), Banks, Digital Lenders, Financial Services (AngelOne) and Industrial sector(Tacit).

If you're looking to implement Gen AI projects and check out Alltius - schedule a demo or start a free trial.

Schedule a demo to get a free consultation with our AI experts on your Gen AI projects and usecases.

‍