Top Deep Learning Companies List

The Nuances of Deep Learning 

Deep learning is a domain that falls under the umbrella of AI technology. It is an evolving technology in which artificial neural networks and human brain-inspired algorithms learn using huge amounts of data. Mimicking the human brain, a deep learning algorithm executes a task a number of times and tweaks it repeatedly to figure out the best way. In other words, deep learning teaches machines to do things that humans do instinctively- learn by example. Through the deployment of Deep learning, machines can resolve complex problems. Deep learning consulting companies develop customized solutions for addressing their client needs. 

Some Real-World Examples of Deep Learning Tech

While Deep Learning is a subset of AI and it is still an evolving technology, the early adopters are already there. Technology giants and some MNCs are already dabbling with this futuristic technology, and the outcomes are awe-inspiring. 

Listed here are some real-world examples of this tech developed by the top ai deep learning companies at work:

1. Virtual assistants- Have you ever thought about how the virtual/digital assistants in your smartphone work! Cortana and Alexa can perform a lot of tasks, driven by voice commands. These assistants utilize deep learning to understand and analyze speech content. 
2. Chatbots- Chatbots are already being used by plenty of companies to handle customer care operations online. The upcoming generation of chatbots will be smarter, and they will be capable of dealing with complex and diverse customer queries. They are going to be powered by deep learning tech. 
3. Autonomous vehicles- The prototypes of driverless and autonomous vehicles are not restricted to the concept stage any longer! Yes, there are detractors of that tech, but the future will be bright for such cars. These cars are driven using deep learning algorithms. 
4. Image colorization- Making a black and white picture into a colorized one was done manually earlier. Deep learning algorithms are being used nowadays to colorize objects and people in black and white images. 
5. Facial recognition- Facial recognition may not seem a cutting-edge tech for those who use it to unlock their phones! However, the limitations of existing facial recognition tech are being addressed by the deployment of deep learning algorithms. In the near future, your smartphone screen will work regardless of your spec style, hair, and facial hair, etc. It will work even if the lighting is bad. 
6. Personalized shopping suggestions- You may have seen how Netflix and Amazon make recommendations for your purchase and entertainment content! These services are being made more refined and suitable by using Deep learning technology. 

Is Deep Learning The Same as Machine Learning?

Deep learning is actually a subset of machine learning, and the former is more autonomous and advanced in nature. In ML, humans still have some amount of control over the operations of machines and processes. The data is uploaded and rectified by human staff. In the Deep Learning model, human intervention is not required much. The problem solving is done mostly by machines, based on data analysis. 

The Advantages of Deep Learning

It is not without reason that so many businesses are seeking the services of the top deep learning companies. There are some major benefits of deploying deep learning methodologies for businesses. These are:

1. Maximum data utilization- In deep learning, huge amounts of data sets are analyzed a number of times; this makes it easier for the machines to deal with all types of data -including unstructured data. So, every bit of collected data is utilized.
2. Less human intervention- Compared to machine learning methodology, Deep learning requires less human intervention. 
3. The deep learning algorithms scan the data continuously and so identifying relevant features in the data set is done without human intervention. 
4. More accurate results- In deep learning, neural networks work tirelessly on patterns and find solutions. The efficacy is way better than human efficacy.
5. Cost reduction- when deep learning is deployed, problems in the workflow can be detected early. From tracing risks of online frauds to early detection of clinical issues, the benefits are manifold—these help in eliminating factors that lead to extra expenditure. 

The Hurdles of Working with Deep Learning

Technology does not usually come without pitfalls, and the same can be said about the Deep learning model. There are some limitations of deploying Deep learning, and you should be aware of these.

1. Data Availability- Deep learning models need gargantuan amounts of data, as it is. Along with the quantity of data, quality also matters. If erroneous or irrelevant data is fed into the system- the output can be dissatisfactory! Gathering huge amounts of accurate data can be tough at times for businesses. 
2. Security of data- You also need to think of data security while deploying deep learning solutions. As it is, businesses deal with confidential customer data. Feeding such data into the application is a risky job, and so security protocols should be in place. 
3. Heavy-duty hardware setup- To execute deep learning models, it is often necessary to use high-performance hardware. This can be heavy on the wallet. 
4. Time intensive- Deep learning models yield results, but you can’t expect results quickly. Based on the domain and type of model used, it may take a long time before the system returns the desired output. This is natural given the fact the models need to deal with and analyze a massive amount of data in sequences.

Summing Up

Now, you know the basics of deep learning and the benefits it can bring. To hire the best deep learning companies, you may count on us. Companies List offers a database of reputed and reliable contenders in this segment.