Deep Learning, first choice of emerging companies

Deep Learning translates to a massive opportunity for businesses looking to leverage the technology to deliver high-performance outcomes. Research firms predict that the deep learning market could be worth nearly $100 billion by 2028 driven by data mining, sentiment analytics, recommendations, and personalization. So, there are big secrets behind this massive growth. That’s the reason Deep Learning has come up as the AI of choice for highly emerging companies. Here are some magical advantages:

1. Feature Generation Automation

Deep learning algorithms can generate new features from among a limited number located in the training dataset without additional human intervention. This means deep learning can perform complex tasks that often require extensive feature engineering. This gives faster application or technology rollouts for businesses that deliver superior accuracy.

2. Works Well with Unstructured Data

One of the biggest strength of deep learning is its ability to work with unstructured data. And most of the businesses data are unstructured. Text, images, and voice are some of the most common data formats that businesses use. Deep Learning promises to make the most impact.

3. Better Self-Learning Capabilities

The multiple layers in deep neural networks allow models to become more efficient at learning complex features and performing more intensive computational tasks, i.e., execute many complex operations simultaneously. It outshines machine learning in machine perception tasks (aka the ability to make sense of inputs like images, sounds, and video like a human would) that involve unstructured datasets.

This is due to deep learning algorithms' ability to eventually learn from its own errors. It can verify the accuracy of its predictions/outputs and make necessary adjustments. On the other hand, classical machine learning models require varying degrees of human intervention to determine the accuracy of output. What’s more? Deep learning’s performance is directly proportional to the volume of training datasets. So, the larger the datasets, the more accuracy.

4. Supports Parallel and Distributed Algorithms

A typical neural network or deep learning model takes days to learn the parameters that define the model. Parallel and distributed algorithms address this pain point by allowing deep learning models to be trained much faster. Models can be trained using local training (use one machine to train the model), with GPUs, or a combination of both.

However, the sheer volume of the training datasets involved could mean that storing it in a single machine becomes impossible. And that’s where data parallelism comes in. With data or the model, itself being distributed across multiple machines, training is more effective.

Parallel and distributed algorithms allow deep learning models to be trained at scale. For instance, if you were to train a model on a single computer, it could take up to 10 days to run through all the data. On the other hand, parallel algorithms can be distributed across multiple systems/computers to complete the training in less than a day.

5. Cost Effectiveness

While training deep learning models can be cost-intensive, once trained, it can help businesses cut down on unnecessary expenditure. In industries such as manufacturing, consulting, or even retail, the cost of an inaccurate prediction or product defect is massive. It often outweighs the costs of training deep learning models.

Deep learning algorithms can factor in variation across learning features to reduce error margins dramatically across industries and verticals. This is particularly true when you compare the limitations of the classical machine learning model to deep learning algorithms.

6. Advanced Analytics

Deep learning applied to data science offers better and more effective processing models. Its ability to learn unsupervised drives continuous improvement in accuracy and outcomes. It also offers data scientists with more reliable and concise analysis results.

The technology powers most prediction software today with applications ranging from marketing to sales, HR, finance, and more. If you use a financial forecasting tool, chances are that it uses a deep neural network. Similarly, intelligent sales and marketing automation suites also leverage deep learning algorithms to make predictions based on historical data.

7. Scalability

Deep learning is highly scalable due to its ability to process massive amounts of data and perform a lot of computations in a cost- and time-effective manner. This directly impacts productivity (faster deployment/rollouts) and modularity and portability (trained models can be used across a range of problems).

For instance, Google Cloud’s AI platform prediction allows you to run your deep neural network at scale on the cloud. So, in addition to better model organization and versioning, you can also leverage Google’s cloud infrastructure to scale batch prediction. This then improves efficiency by automatically scaling the number of nodes in use based on request traffic.

8. Ability to deliver high-quality results

Humans get hungry or tired and sometimes make careless mistakes. When it comes to neural networks, this isn’t the case. Once trained properly, a deep learning model becomes able to perform thousands of routine, repetitive tasks within a relatively shorter period of time compared to what it would take for a human being. In addition, the quality of the work never degrades, unless the training data contains raw data which doesn’t represent the problem you’re trying to solve.

9. Better, faster and Cheaper predictions: Which business wouldn’t want to be able to call just the customers who are ready to buy or keep just the right amount of stock? All of these decisions can be improved with better predictions. Deep learning, and machine learning in general, automates a company’s decision making increasing its execution speed. Consider customers that leave their contact info to get more details about a tech solution for their company. Maybe it is obvious from the contact info that this is a very high potential and needs to be contacted. Thanks to the model in place, no one needs to manually check that data, the potential customer will be immediately prioritized. Speed is especially important in this example because customers contacted sooner are more likely to convert.

Companies that do not implement operational decision making models, rely on analysts to make decisions which are orders of magnitude costlier than running deep-learning models. However, deep learning models also have setup time and costs. Therefore, the business case for models need to be investigated before rolling out models.

Final Thoughts

Keeping in mind the above and more advantages of using deep learning approach, it can be said that it’s obvious to experience the impact of deep learning in different high-end technologies like Advanced System Architecture or Internet of Things in the future. We can expect to see more valuable contributions to the larger business realm of connected and smart products and services.

These days, deep learning has come a long way from being just a trend and it’s quickly becoming a critical technology being adopted steadily by an array of businesses, across multiple industries.