Deep Learning Engineer is listed 2nd on the list of Top AI jobs by Indeed.com. The global market investment in Deep Learning grew from USD 3.5 trillion to USD 5.8 trillion last year, as per a report by McKinsey Global Institute.
You can find Deep Learning jobs everywhere across the globe. The average annual salary of a Deep Learning Engineer is around INR 7 to 12 LPA. The best thing about Deep learning is that you can enter into a career in Deep Learning even if you are a beginner.
This is why newbies are looking to take a deep learning course for beginners for a rewarding career in this domain.
We can find Deep Learning applications in our daily lives. Virtual assistants like Amazon’s Alexa/Apple’s Siri/Microsoft’s Cortana; Recommendation engines on social media, eCommerce, and OTT platforms; sentiment analysis; and many more. It is applied in the Healthcare sector where it is revolutionizing the ways of checking blood sugar levels, heartbeat count, blood pressure, and diagnosis of diseases at an early stage.
In addition, colorization of black and white images, automatic handwriting generation, sequence generation, automated essay grading tools are all innovative applications of Deep Learning.
Let us look at what Deep Learning is and its advantages and challenges.
What is Deep Learning?
Deep Learning is a function of Artificial Intelligence that mimics the functioning of the human brain in transforming the data and creating patterns to be used in making wise decisions. A subset of machine learning, deep learning, has networks capable of performing unsupervised learning from unlabeled or unstructured data. Deep Learning is also referred to as Deep Neural Learning or Deep Neural Network.
How Deep Learning Works?
With the advancements in digitization, deep learning is evolving consistently, resulting in an explosion of data in different formats and that too from every corner of the world. Big data is collected from different sources such as social media, eCommerce platforms, search engines, and other sources. This massive amount of data is made accessible and is shared via fintech applications such as cloud computing.
Generally, this huge amount of data generated is unstructured and is impossible for humans to comprehend and extract useful information. Deep Learning utilizes a hierarchical level of artificial neural networks to execute the processing of machine learning. These artificial neural networks are created as a human brain where neuron nodes are connected to each other like a web. Where traditional programs analyze the data in a linear manner, the hierarchical model of a deep learning system allows machines to work on the data in non-linear patterns.
Each layer of the neural network is built on its previous layer with the additional data. The final layer is responsible for giving out the desired result.
Advantages of Deep Learning
With the incredible applications of Deep learning, you would like to know more about deep learning. Let us have a look at the advantages of Deep Learning.
1. Maximal utilization of unstructured data
The massive data generation on the Internet is mostly unstructured. It is impossible for a human brain to comprehend this data in a short period of time. Machine learning algorithms are unable to transform this unstructured data into a useful form. Here deep learning algorithms come into play.
Different formats of data are used to train deep learning algorithms and gain insights relevant to serve the purpose. For example, deep learning algorithms can be used to uncover relations between social media chats, industry analysis, etc.
2. Delivers top-quality results
Once trained perfectly, deep learning algorithms can be used to perform iterative tasks thousands of times and that too within a very short time. The best part is that the results it delivers are flawless and high-quality ones.
3. No need for feature engineering
Deep learning algorithms can execute feature engineering by itself. This is advantageous for data scientists as it saves a lot of time. In this method, an algorithm starts with scanning the data to recognize features that correlate and then blends those features to enhance faster learning without being explicitly programmed to do so.
4. Deep learning models are able to detect defects that would have been difficult to identify otherwise, thereby saving significant costs.
5. Deep learning algorithms are capable of learning without guidelines, eliminating the need for labeling the data.
6. The deep learning architecture is flexible enough to get adapted to new issues easily.
7. It allows for massive parallel computations by utilizing GPUs and is scalable for huge volumes of data.
8. Deep learning architectures are robust to natural variations.
With the fact that there are two sides to a coin and all the positive things come with some negative features too, Deep learning also has some limitations. Let’s read them below.
Limitations of Deep Learning
- To deliver the best results, a deep learning algorithm requires massive amounts of data to get trained. If you are unable to feed them with enough data, the deep learning system is likely to fail.
- The term deep in deep learning doesn’t refer to the level of learning, but it refers to architecture. So, deep learning algorithms don’t really understand the context very well. With an increasing demand for real-time data analysis, it is required to quickly retrain deep learning models.
- There is no standardized theory to enable you to select the right deep learning tool as it requires you to have an understanding of the topology and other parameters.
- Deep learning is incredibly expert in providing cybersecurity. But the network of deep learning is itself prone to hacking.
- Deep learning requires numerous machines and expensive GPUs to work. So, it becomes really expensive to build deep learning architecture.
Conclusion
You have seen the advantages and disadvantages of the technology that is booming these days. Though it has some disadvantages, the global market size of deep learning is projected to grow from USD 20 million in 2018 to USD 930 million in 2025.
Do you wish to make a career in Deep learning?
If yes, then the smartest way to do so is to get yourself registered in an online training course from an accredited institute. Job assistance and doubt sessions are the best parts of these courses, apart from other features like self-paced learning, different learning modes, and exposure to real-life scenarios.