How to Become a Deep Learning Engineer in 2024?

A Learning Roadmap to Becoming a Deep Learning Engineer in 2024.

How to Become a Deep Learning Engineer in 2024?
 |  BY ProjectPro

Deep learning was developed in the early 1940s to mimic the neural networks of the human brain. However, it did not garner enough interest due to limited computation power and storage options. However, in the last few decades, deep learning has unleashed itself into the world. Its massive evolution is also the result of substantial research labs and industry players like Facebook, Google, Apple, Netflix, Microsoft, Baidu, and IBM investing in its research. 85% of data science platform vendors have the first version of deep learning in products

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Deep Learning Engineer Jobs Growth 

Deep learning is the driving force of artificial intelligence that is helping us build applications with high accuracy levels. Advances in technology have pushed deep learning to a point where it can now outperform humans in tasks like object classification in images. Today, deep learning is present in our everyday Google's voice and image recognition, Netflix and Amazon's recommendation engines, Apple's Siri, automatic email and text replies, chatbots, Tesla's self-driving cars, and many more.

According to the 2020 O'Reilly survey report, Deep learning (55%) is also the most popular technique used among organizations still in the evaluation stage of Artificial intelligence. The shortage of machine learning modelers and data scientists topped the list, cited by close to 58% of respondents in the O'Reilly survey making machine learning and deep learning a highly sought-after skill in the industry.

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Who is a Deep Learning Engineer?

Deep learning is a sub-field of the machine learning domain which deals with artificial neural networks. Neural networks are used t0 provide solutions using different types of image text and audio datasets. The Neural networks are designed to imitate the working of a human brain and similarly produce results. Deep learning involves building and training an extensive artificial neural network or ANN, with multiple hidden layers between the input layer of the network and the output layer. Because of these numerous hidden layers, we call this neural network "deep." Deep neural networks have at least three hidden layers, but some neural networks have hundreds. 

A deep learning engineer uses the algorithms and techniques developed by the researchers and applies them to real-world problems, which help create solutions. Deep learning engineers are highly versatile individuals. Their mix of engineering and scientific skills allows them to perform multiple AI project development and deployment tasks. They work well with data analysts and statisticians who focus on translating data statistics into relevant business information and software engineers who build the infrastructure and tools that increase the effectiveness of the tasks.

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What does a Deep Learning Engineer do?

The design and development of an Artificial Intelligence project have multiple lifecycle phases. A deep learning engineer is involved in the project's data engineering and modeling phase in the beginning. He is also an essential part of the deployment and infrastructure of the project. Deep learning engineers carry out data engineering tasks like defining data requirements for a project, collecting, labeling, inspecting, and cleaning data. They are also involved in modeling tasks that include training deep learning models, defining evaluation metrics, searching hyperparameters for the models. Deployment tasks such as converting prototyped code into production code and setting up a cloud environment to deploy the production model are part of a deep learning engineer's role.

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Deep Learning Engineer Salary - How much do they Earn?

The average salary for Deep Learning Engineer is ₹8,02,902 per year in India.  In contrast, the national average salary for Deep Learning Engineer is US$1,30,123 annually in (USA). An entry-level deep learning engineer ranges from ₹5,00,000 to ₹8,00,000. A senior-level deep learning engineer can earn an average of ₹13,00,000 annually and go up to a higher limit of ₹17,00,000 in India. The average salary for Deep Learning Engineer is £55,985 per year in the United Kingdom. Their work is comparable to the creation of computer and information research scientists, who also develop new forms of technology. 

According to O'Reilly's salary survey in 2020, the average salary for AI and data professionals who participated in the survey was $146,000. The average change in compensation in the last three years was $9,252. This increase in salary corresponds to an annual increase of 2.25%. The US Bureau of Labor Statistics states that computer and information research scientists would experience a job growth rate of 15% from 2019 to 2029.

Skills Required to Become a Deep Learning Engineer

Deep learning engineers require outstanding engineering and scientific skills since they are involved in production and prototyping phases. Deep learning engineers write and review lines of programming code for neural networks using a range of programming languages. They also need analytical skills to review code to identify issues or areas for improvement and debugging. Data scientists are only involved in the prototyping and software engineers in the production phase. Hence deep learning engineers must be equipped with machine learning, data science, mathematics, algorithmic coding, and software engineering. Communication skills also play a big part as deep learning engineers interact with multiple teams to develop AI products. Deep learning engineers need to be good team players and have strong communication skills to be influential team members. They also need good written communication skills, documentation of the product, and preparing reports. Deep learning engineers develop applications in speech recognition, NLP (natural language processing), and computer vision

Deep learning engineers should be familiar with the most common neural network architectures like Autoencoders, Deep Belief Networks (DBNs), Generative Adversarial Networks (GANs), Convolutional neural networks (CNNs), Recurrent Neural Networks, LSTM Networks so they can choose the most appropriate deep learning architecture for any deep learning project. 

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Deep learning engineers also need strong mathematical and analytical skills and benefit from applied mathematics and statistics courses. They are also required to be handy with a wide range of computer programming languages and platforms, including Python, Matlab, Linux, and C++. Tensorflow, sci-kit learn Keras, and PyTorch are the most used tools in deep learning applications. According to the 2020 O'Reilly survey, TensorFlow is the single most popular tool for use in AI-related work for deep learning engineers. TensorFlow was cited by almost 55% of respondents in both 2019 and 2020 surveys.

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Deep Learning Engineer Toolkit

The tools used by a deep learning engineer may vary from one organization to another. However, here are some of the tools commonly used by deep learning engineers:

  • Cloud technologies such as AWS, GCP, and Azure.

  • Object-oriented programming (OOP) languages such as Python, Java, and C++, used to deploy the AI product.

  • Collaboration and workflow management tools like Git for version control

  • Integrated development environment (IDE) like Jupyter Notebook. for workflow management

  • Querying languages like Python and SQL for data engineering.

  • Python packages like sci-kit learn, pandas, NumPy, TensorFlow, matplotlib, Keras, Theano, and PyTorch for modeling the solution.

  • Caffe, an open-source, deep learning tool whose framework built with expression, speed, and modularity in consideration for application development

Becoming a Deep Learning Engineer - Next Steps

The O'Reilly survey identified ML and AI-specific skills gaps in organizations. The shortage of skilled data scientists and deep learning engineers tops the list at 58%. This shortage is slightly more than the 58% votes in 2019, exposing us to the gaping need for critical skills shortage in this area. The role of a data scientist and deep learning engineer is multi-faceted, requiring theoretical and practical skills, domain-specific business expertise, and good communication skills.

We have listed a few projects in deep learning which will help get you started on the journey as a deep learning engineer. If you are an entry-level engineer, here is a link to a project introducing deep neural networks and teaching you how to implement them in Python. You can then try this project on Image Segmentation Tensorflow using CNN. You can develop a project to Build CNN for Image Colorization using Deep Transfer Learning. You will Train a model for colorization to make grayscale images colorful using convolutional autoencoders. This project on NLP and Deep Learning For Fake News Classification in Python will help you use Python to implement RNN's and other Machine learning techniques for fake news classification. You can also try the Time Series Forecasting with LSTM Neural Network Python Project to learn to apply a deep learning paradigm to forecast univariate time series data.

We hope that this takes you on an exciting journey of deep learning neural networks which will help you solve and build solutions to the world's most prominent business problems. After all, deep learning is all about accomplishing tasks beyond the realm of human perception.

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