Deep Learning (DL) is a sub-branch of Machine Learning that focuses on one thing – to design and develop smart machines that can learn through experience and examples via the trial and error method. Artificial Neural Networks that are inspired by the neural structure of the human brain form the core of Deep Learning.
Just like the neurons in the human brain can send and receive signals to process information and perform tasks, ANNs also function in the same way. The neural network consists of multiple layers that further contain numerous nodes that send and receive signals from one layer to another for data processing.
Although Deep Learning models are fed with data to train them, eventually, they can perform human-like tasks by learning through experience. The deep learning algorithms running these advanced models learn to perform classification tasks directly from images, text, or sound inputs.
When trained right, their performance and accuracy can often exceed that of humans. Autonomous cars, recommendation engines used by online platforms (Amazon and Netflix), virtual assistants (Siri, Alexa, Cortana), Virtual Recognition, are some of the greatest applications of Deep Learning. Learn more about deep learning applications in real world.
Who is a Deep Learning Engineer?
Deep Learning Engineers are experts in Machine learning and Deep Learning. Their primary responsibility is to use DL platforms and algorithms for performing specific tasks to further the bigger goal – Artificial Intelligence.
Deep Learning Engineers train DL models with large and complex datasets to develop intelligent systems that can mimic brain functions and perform tasks that require cognition (but with no or minimal human intervention). To accomplish this goal, they leverage the power of neural networks.
Deep Learning Engineers develop systems that can efficiently transfer data and also write/implement complex programming code to direct parts of the neural network to operate according to the task at hand. The Deep Learning models created by Deep Learning Engineers are used in many real-world applications, including natural language processing, image classification, image/speech recognition, Facial recognition, fraud detection, market forecasting, medical image analysis, and much more.
While Deep Learning Engineers must have a strong foundation in Machine Learning basics, including Mathematics and Statistics, they must also be well-versed in data mining, predictive analytics, and exploratory data analysis.
Responsibilities of a Deep Learning Engineer
- To have a deep understanding of Computer Science fundamentals – data structures, algorithms, computability and complexity, and computer architecture.
- To use Mathematical formulas and techniques to perform complex computations and design advanced algorithms.
- To be well-versed in Machine Learning and ML/DL algorithms.
- To extend existing ML/DL libraries and frameworks
- To develop algorithms based on statistical modeling procedures.
- To build and maintain scalable ML/DL solutions in production.
- To analyze large and complex datasets to extract insights.
- To train and retrain ML/DL systems as and when necessary.
- To be familiar with various ML/DL algorithms and libraries – they must know how and when to use them.
- To manage the infrastructure and data pipelines required to take the code from development through to production stage.
- To collaborate with Data Engineers to design and develop data and model pipelines.
- To demonstrate an end-to-end understanding of ML/DL applications (including, but not limited to, ML and DL algorithms, libraries, and frameworks).
- To collaborate with project stakeholders to identify and evaluate business problems, clarify the requirements, and define the scope of the resolution needed.
- To provide support to ML Engineers/Data Engineers in implementing correct ML and DL techniques in a product.
Skills required to become a Deep Learning Engineer
A Deep Learning Engineer should possess:
- Exceptional mathematical and statistical skills to perform complex computations.
- In-depth understanding of data structures, data modeling, and software architecture.
- The ability to work with various ML and DL frameworks and libraries like TensorFlow, Keras, Caffe, PyTorch, DeepLearning4J, Theano, etc.
- The ability to write precise and efficient code in Python, Java, and R.
- Excellent written and verbal communication skills.
- Excellent analytical and problem-solving skills.
- A creative bent of mind with attention to detail.
Also read: Data Scientist Salary in India
How to become a Deep Learning Engineer?
Since one cannot become a Deep Learning Engineer right away, you need to start your Deep Learning journey from the foundation – either by beginning as a Software Engineer, or Data Engineer, or an ML Engineer. All these job roles have a common foundation – Mathematics, Statistics, Probability, and of course, programming.
A career in Deep Learning demands that you are well-versed in the concepts of Machine Learning, including both supervised and unsupervised learning techniques. There are plenty of online learning resources for helping you master the theoretical part of ML and DL.
Also, as we mentioned before, it is crucial to get familiar and handsy with various ML/DL libraries and frameworks for model building. And since most of the popular libraries and frameworks are Python-based, you must be proficient in Python programming language.
Once you’ve got these basics right, you should start implementing your theoretical knowledge into practical experimentation. You can do this by taking up small ML/DL projects – Kaggle is one of the best platforms to find fun and challenging projects. Try to work on ML models that include logistic regression, K-means clustering, support vector machines, and other such advanced algorithms.
Deep Learning is a mix of different things such as model training, coding business logic, design functionality, unit testing, model optimization, and much more. Hence, before you can master Deep Learning, you must learn the other elements, including programming, data mining, predictive analysis, ML libraries/frameworks, and so on.
Thus, as you can see, the pathway to becoming a Deep Learning Engineer is not a direct one. However, the journey to getting there can be pretty exciting, given that Machine Learning and Deep Learning are both exciting fields of study. Both ML and DL are evolving technologies, and hence, upskilling and keeping yourself updated with the latest advancements in the fields are the mark of a true Deep Learning Engineer.
Deep Learning Engineer Salary
The Deep Learning Engineer salary in India is determined by several factors such as a candidate’s educational qualifications, skill set, work experience, and also the company size and reputation, location, and job role offered. Typically, the salaries of Machine Learning job roles in India remains well above the market average.
The starting pay for Deep Learning Engineers in India can range anywhere between Rs. 3 – 15 LPA. Of course, individuals who fall on the higher end of the salary scale possess advanced qualifications, or have prior work experience, or may work for top players in the industry.
For instance, individuals with the following skills can demand a higher yearly compensation:
- Python/C++ Programming Language
- Natural Language Processing
- Software Development
- Big Data Analytics
- Image Processing
- Computer Vision
- Data Modeling
- Deep Learning
- Data Analysis
Then again, educational qualifications play a pivotal role in determining the salary scale. For instance, graduates with a Bachelor’s degree in Computer Engineering/Software Engineering can earn around Rs. 3.5 – 6 LPA, whereas those having a postgraduate degree in the same specialization or related fields (Electronic Engineering/Computer Science/Information Science), can make about Rs. 5 – 7.3 LPA. However, MBA graduates can earn a high starting salary (owing to their extensive knowledge of both the technical and business domains) of around Rs. 6 – 8.5 LPA or more.
Mid-level Deep Learning Engineers having more than eight years of work experience can earn an average annual salary of Rs. 7 – 12 LPA, whereas senior-level professionals having over 15 years of field experience can command salaries ranging between Rs. 25 – 48 LPA and more.
According to the latest stats, the global ML market size that stood at US$ 6.9 billion in 2018, is projected to grow at a CAGR of 43.8% between 2019 to 2025. Naturally, this will lead to the creation of many more job openings in ML and related fields, including Deep Learning. So, this is the time to gain the requisite skills and become a Deep Learning Engineer!
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What are the job responsibilities of a deep learning engineer?
A deep learning engineer is responsible for completing subtasks such as deployment, data engineering, and modeling. Data requirements are anticipated to be shown and defined by deep learning engineers. Data is also collected, transferred, supplemented, inspected, and cleaned by them. They should also know how to build up a cloud environment for deploying the specified models and converting prototyped code into production code. Deep learning models are also trained by a deep learning engineer.
What knowledge do I need to have before I become a deep learning engineer?
You do not need to be a math whiz, but you should have a basic understanding of algebra, calculus, statistics, and other related subjects. You should also be familiar with a variety of programming languages, such as Java, C, C++, and Python, because you'll be converting codes. Deep learning requires more than just building a prediction model. You must assess the model's quality and continue to improve it until you get the best model possible. You should be well-versed in evaluation metrics in order to do so.
How is deep learning different from machine learning?
Machine learning is a branch of deep learning, but it is a more specialized one. Machine learning needs a great deal of human interaction, whereas deep learning necessitates very little. Traditional methods are used in machine learning, and it requires structured data to function properly. While deep learning is good at working with unstructured data and employs neural networks instead of standard techniques, it is not as good at working with structured data. Machine learning systems are simple to set up and use, but their outcomes may be restricted. Deep learning systems take longer to set up but provide results quickly.