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Artificial Intelligence and Machine Learning technologies are fueling and shaping next-generation computing. Although ‘smart’ machines surround us, they still need human expertise to run effectively. This is why software engineers, particularly those with advanced knowledge of deep learning and neural networks, are in high demand across industries. In this blog, we will uncover the fundamentals of these technologies and throw light on some lucrative career paths.
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If you already have a degree in Computer Science, IT Engineering, Mathematics, Statistics and related fields, read on to know how to pursue a successful career in deep learning. But before we begin, let us first take you through the definition and building blocks of deep learning.
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What is Deep Learning?
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning are tech buzzwords that are often used interchangeably. Here’s a summary of what they exactly mean:
- AI allows machines to reason, analyse, and act like humans when faced with a particular problem.
- ML is a part of AI that studies computer algorithms and uses data to enable machines to learn from experience.
- Deep learning is a subset of ML-based that works with artificial neural networks (designed to mimic human actions). It allows a computer model to classify data directly from text, image, or sound and improve on its own.
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Nowadays, deep learning and neural networks are so accurate that they can excel in human-level performance. Some examples include:
- Driverless car technology can distinguish pedestrians from lampposts and detect stop signs and traffic lights.
- Advanced microscopes yield high-dimensional data sets for training deep learning application in identifying cancer cells.
- Automatic detection mechanisms in factories ensure that people and objects are at a safe distance from the heavy equipment.
- Home-assistance devices powered by deep learning applications that are automated to hear, translate, and respond to your voice.
- AI-based mobile applications are capable of discerning images and generating intelligent captions for them.
As you can see, deep learning has a range of applications, from medical diagnosis to consumer devices. Let us now look at what goes behind the scenes.
How Does Deep Learning Work?
Just like the human brain has interconnected neurons to collect signals, smart machines have neural networks (layers of nodes) that receive external input. The network keeps getting deeper as the number of layers increases. And with the advent of fields like Big Data analytics, even larger and more sophisticated neural networks have come to the fore.
Deep learning techniques examine computer algorithms in the light of such advanced networks, helping computers observe, learn, and react faster than humans. This involves tasks like:
- Classifying images
- Translating language
- Recognising speech and
- Identifying patterns.
Deep learning systems need robust hardware to process large volumes of data and perform complex mathematical calculations. As the data processing kickstarts, artificial neural networks sift through the data, compute answers from a series of true or false questions, and produce and output.
For instance, a facial recognition program first detects the edges and lines, then identifies more striking features, and ultimately recognises the overall representations of the face placed in front of it. The accuracy of the program increases over time as it trains itself in a particular task.
Deep Learning Vs. Neural Networks
If you want to know ‘what is deep learning’, it’s crucial for you to understand the concept of neural networks. Any neural network with multiple layers can be described as deep learning.
But ‘what is deep neural network’? Neural networks transmit data through interconnected node layers to make it easier to comprehend observational data like photos or audio. When information goes through a layer, each node operates on it to pass selective results to the other nodes.
Therefore, every subsequent layer concentrates on a higher-level feature than the previous one. The process continues until a final output is received.
Between the output and the input layer, you will come across several hidden layers. That’s where the difference between deep learning and neural networks is evident. A basic neural network comes with one or two hidden layers. But a deep learning network can include dozens and hundreds of layers.
Maximizing the number of layers and nodes can improve the network’s accuracy. But more layers can also increase the model’s need for computational resources and models.
Deep learning can filter information through different layers of neural networks containing a set of inputs to receive raw data. For instance, you can teach a neural network to identify images of plants.
As you add more layers, you will be able to get more precise results. For instance, it will help the neural network to help differentiate between multiple species of plants.
Neural networks for deep learning models have various hidden layers between the output and input nodes. Therefore, they are capable of accomplishing more complicated data classifications.
But you need to train a deep learning algorithm with large data sets. It will become more accurate when it receives more data. You need to feed a deep learning model with multiple pictures of birds to precisely identify different species of birds.
In the case of neural networks, training a deep learning model is highly resource-intensive. When the neural network ingests inputs, they are processed in hidden layers with the help of weights. These weights or parameters can be adjusted according to the training inputs to make accurate predictions.
What Is a Recurrent Neural Network in Deep Learning?
While learning about deep learning, you might come across the concept of recurrent neural networks. But what is recurrent neural network in deep learning? It is an artificial neural network leveraging time series data or sequential data.
They are often used for temporal or ordinal problems like speech recognition, language translation, image captioning, and natural language processing. Popular applications like Google Translate, voice search, and Siri incorporate recurrent neural networks.
Similar to convolutional neural networks, recurrent neural networks also focus on training data for learning purposes. Moreover, recurrent neural networks share parameters across different layers. In every layer of the network, recurrent neural networks share the same weight parameter.
Recurrent neural networks also use the BPTT algorithm to identify different gradients. The principles of the BPTT algorithm are similar to traditional backpropagation. But it also has certain variations from the traditional backpropagation method due to its approach toward data sequencing.
Career Opportunities in Deep Learning
Deep learning is on the rise in 21st-century workplaces. Companies are increasingly deploying this disruptive digital technology to create new business models and stay ahead of the curve. Here are some up and coming areas in which the skills of deep learning engineers are proving useful:
- Medical image analysis
- Natural language processing
- Computer vision
- Automated translations
- Material inspection
- Drug design
- Bioinformatics
- Fraud detection
- Entertainment (pixel restoration, adding sound to silent movies, etc.)
Apart from the domains mentioned above, ML practitioners also have transferable skills for sectors like e-commerce, digital marketing, and social media. From powering recommendation engines and offering purchase suggestions to detecting spam and flagging inappropriate content without human intervention, there are numerous use cases. Read how machine learning transforming industries.
As a deep learning engineer or ML professional, you would be required to:
- Run experiments with the help of programming languages and ML libraries.
- Deploy ML solutions into production.
- Optimise deployed solutions to achieve better performance and scalability.
- Implement custom codes depending on business needs.
- Analyse data to come up with novel applications.
- Ensure a good flow between backend systems and databases.
- Collaborate with stakeholders, including data scientists, researchers, product managers, and software engineers.
Deep Learning Salary in India
According to Payscale, deep learning engineers currently earn an average salary of Rs. 9,05,105 in India. The remuneration for senior roles can go up to Rs. 20,00,000 depending on candidates’ educational qualifications and years of work experience. Factors like job location and industry sector also influence the pay offered by an employer.
Professionals adept in deep learning skills are hired by several top companies in India, such as:
- Accenture
- Genpact
- Adobe
- Myntra
- American Express
- Standard Chartered
- PayTM
- MobiKwik
- Razorpay
- Practo
- Make My Trip
Deep learning is expected to gain even wider acceptance and drive business innovation in the coming years. According to a NASSCOM study, the composite AI stack can boost India’s GDP by 2025. Additionally, LinkedIn’s Emerging Jobs ranking list featured ML engineers at the top. The job role witnessed a growth of almost ten times in five years. This surge is sure to pick up in the future! Read more on deep learning engineer salary.
However, AI & ML jobs in India largely remain understaffed due to the mismatch between industry requirements and professionals’ skills. This gap is likely to expand with rapid technological advancements. Some unique training avenues in deep learning and neural networks offer a way of addressing this issue. Let’s explore.
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How to Become a Deep Learning Engineer?
An academic background in data science and software engineering can give you a headstart in deep learning careers. In terms of skills, you would need working knowledge of programming languages (Python, SQL, Java, C++, etc.) and a good grasp of tools like TensorFlow, R Programming, Spark and Hadoop, Apache Kafka, and MATLAB. Proficiency with system design and data structures can also give you an edge over your competitors.
Besides developing your technical know-how, it is crucial to apply theoretical knowledge to real-life scenarios. Assignments and projects offer excellent avenues for such learning while helping you build your portfolio. In addition, having an end-to-end implementation experience would immediately set you apart in the eyes of the hiring manager.
That said, sometimes employers are looking for people with specialised knowledge in deep learning and neural networks. In such cases, online courses like Advanced Certificate Programme in Machine Learning & Deep Learning can prove immensely helpful. The six-month programme is offered by IIIT-Bangalore and upGrad and gives you industry-oriented training in deep learning and neural networks.
Moreover, you get the opportunity to connect with experts and fellow learners, benefitting from personalised career assistance and networking sessions. The holistic experience helps you earn the proper credentials for high-paying job roles while also preparing you for the future. Therefore, enrolling in an advanced course is a logical step for those looking to pursue a career in deep learning.
Before selecting a study option and diving in further, it is best to have a clear idea of the foundational concepts and how they fit together under an umbrella. We hope this blog helped you do that. In summary, we discussed what is deep learning and informed you about some emerging career paths in the field. These insights to gain a foothold in the AI & ML industry!
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