Deep Learning Career Path: Top 4 Fascinating Job Roles

Although at its nascent stage, Deep Learning (DL) is a fast-evolving to become one of the mainstream technologies alongside Artificial Intelligence and Machine Learning. Thanks to the rapid advancements in Deep Learning, it has become possible to implement the technology in real-world applications.

Today, Deep Learning tech is being leveraged in natural language processing, speech-to-text processing, speech/audio recognition, object detection, image recognition, and even in mastering video games like Dota.

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The inclusion and adoption of Deep Learning in the industry have given rise to many promising Deep Learning career paths. More and more aspirants are becoming attracted to this emerging field, hoping to build a stable career in Deep Learning.

In this post, we’ll shed light on some of the most promising Deep Learning career paths. However, we will start from ground zero and first talk about the prerequisites of building a career in Deep Learning.

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Deep Learning Prerequisites

Any aspirant who wishes to land a job in Deep Learning must first master the core concepts of Deep Learning.

1. Mathematics and Statistics

Since maths forms the foundation of Deep Learning, it is crucial to building a strong base in Mathematics and Statistics, including calculus, linear algebra, and probability, among other things. You cannot grasp Deep Learning unless you are familiar with the core concepts of linear algebra and calculus. So make sure to be well-versed with matrices, vectors and derivatives.

2. Machine Learning

The next step is to learn the basics of Machine Learning, including concepts like classification, linear regression, logistic regression, continuous and discrete distributions, probability estimation, and regularization methods.

3. Learning the fundamentals of CNNs

Convolutional Neural Networks (CNNs) is one of the most common applications of Deep Learning in real-world use cases. Every Deep Learning professional is expected to know the nitty-gritty of CNNs.

4. Getting handsy with GANs

GAN or the Generative Adversarial Networks is yet another crucial Deep Learning concept that you must know. GANs are generative models, as in, they can create new data instances that bear striking semblance to the training dataset. GANs can create images of human faces that don’t even exist in the real world!

5. Introduction to Deep Learning with Keras

While the top two Python platforms for Deep Learning are Theano and TensorFlow, it can be challenging for a beginner to use these directly for building Deep Learning models. This is where Keras enters. Keras is a minimalist Python library specially designed for Deep Learning. Its novelty feature is that it can run seamlessly on top of Theano or TensorFlow by creating a clean and accessible environment for model development. Make sure to get handsy with Keras.

6. Fine-tuning neural networks

Once you’ve understood the basics of model building and practised the same, it is time to learn how to fine-tune and perfect your neural network models. Usually, models don’t deliver the best results in the first iteration itself. So, you must learn how to improve and enhance your models to obtain the best possible results.

For this, you have to be proficient in handling and pre-processing image data, understanding hyperparameter tuning, and transfer learning to improve the performance of your Deep Learning model.

7. Debugging Deep Learning models

This is a crucial part of model building. If the input data isn’t reliable and relevant, no machine learning tool/platform/framework can work perfectly, no matter how good your algorithm is. Thus, it is essential to know how to debug your DL model by validating the input data, testing it, and ensuring that the data is correctly split into a training set and test set.

8. Sequence Models

Sequences are data structures wherein each example can be considered as a series of data points. Sequence models include Deep Learning techniques like Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTMs), and Gated Recurrent Unit (GRU).

9. Deep Learning for NLP

Deep Learning has revolutionized NLP. Thanks to the advancements in Deep Learning, neural-based models can now deliver superior results with greater accuracy. If NLP interests you, you should learn how the various Deep Learning concepts and techniques can be used to perform and perfect NLP tasks.

Deep Learning Career Paths

Now that you know what you must learn to kickstart a career in Deep Learning, here are the top four Deep Learning career paths that you can consider:

1. Deep Learning Engineer

Deep Learning Engineers are experts in Deep Learning platforms. Their primary responsibility is to develop advanced neural models that can mimic brain functions. Also, they have to run ML tests and experiments and implement appropriate ML algorithms for the optimal functioning of neural networks. Deep Learning Engineers must have experience with best practices in software engineering, including unit testing, continuous integration, and source control.

2. Deep Learning Scientist

Deep Learning Scientists are essentially researchers in the domain who are tasked with the job of continuously identifying and finding ways to optimize the performance of ML/DL models. Much like the Deep Learning Engineer, Deep Learning Scientists have to design, develop, configure, and optimize deep neural networks for performing various tasks like detection, classification., etc.

3. NLP Scientist

NLP Scientists design and developing intuitive DL models and applications that can both learn the patterns of speech of natural human languages and also translate words spoken in one language into other languages. Their primary focus is to create systems that can understand human languages just like a human would.

4. Image Processing Engineer

Image Processing Engineers are DL experts who develop and test image processing and data classification algorithms. They analyze data and develop methods by using ML techniques for feature extraction and classification. Visualizing the findings and communicating the results is one of the most crucial aspects of an Image Processing Engineer’s job. Hence, they must know how to create visual interpretations of data, write detailed reports, and create presentations.

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Conclusion

Since the deep learning technology is emerging, and with time, as Deep Learning tech advances further, more such exciting Deep Learning career paths will emerge.

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