HomeBlogArtificial IntelligenceDeep Learning Career Path: Top 4 Fascinating Job Roles

Deep Learning Career Path: Top 4 Fascinating Job Roles

Read it in 5 Mins

Last updated:
12th Dec, 2019
Views
1,501
In this article
View All
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. 

Best Machine Learning Courses & AI Courses Online

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. 

Ads of upGrad blog

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. 

In-demand Machine Learning Skills

Get Machine Learning Certification from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.

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.

Ads of upGrad blog

Popular AI and ML Blogs & Free Courses

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.

If you are interested to know more about deep learning and artificial intelligence, check out our PG Diploma in Machine Learning and AI program which is designed for working professionals and more than 450 hours of rigorous training.

Profile

Kechit Goyal

Blog Author
Experienced Developer, Team Player and a Leader with a demonstrated history of working in startups. Strong engineering professional with a Bachelor of Technology (BTech) focused in Computer Science from Indian Institute of Technology, Delhi.
Get Free Consultation

Select Course
Select
By tapping submit, you agree to  UpGrad's Terms & Conditions

Our Popular Machine Learning Course

Suggested Blogs

Introduction to Natural Language Processing
1500
We’re officially a part of a digitally dominated world where our lives revolve around technology and its innovations. Each second the world produces a
Read More

by Abhinav Rai

01 Apr 2023

What is an Algorithm? Simple & Easy Explanation for Beginners [2023]
1500
It is a standard protocol to use maps and blueprints for executing various processes smoothly. Just like an architect uses detailed blueprints to esta
Read More

by Pavan Vadapalli

01 Apr 2023

Recursive Feature Elimination: What It Is and Why It Matters?
1500
Data is the backbone of modern decision-making, and businesses are always looking for ways to extract valuable insights from it. Machine learning is o
Read More

by Pavan Vadapalli

27 Mar 2023

Why AI Is The Future & How It Will Change The Future?
1500
The advent and advancements in Artificial Intelligence (AI) has indeed changed our lives for the better. It refers to software robots’ capabilit
Read More

by Pavan Vadapalli

27 Mar 2023

A Brilliant Future Scope of Machine Learning
1500
A constant form of silent evolution is machine learning. We thought computers were the big all-that that would allow us to work more efficiently; soon
Read More

by Thulasiram Gunipati

26 Mar 2023

What is Supervised Machine Learning? Algorithm, Example
1500
Machine learning is everywhere – from government agencies, retail services, and financial institutions to the healthcare, entertainment, and tra
Read More

by Pavan Vadapalli

23 Mar 2023

All about Informed Search in Artificial Intelligence
1500
Informed search is a type of search algorithm that uses domain-specific knowledge to guide its search through a problem space. From navigation systems
Read More

by Pavan Vadapalli

22 Mar 2023

Future of Retail in the Metaverse
1500
The metaverse is the successor to the mobile internet and the next progression in social connection. Similar to the internet, the metaverse will let y
Read More

by Pavan Vadapalli

17 Mar 2023

5 Significant Benefits of Artificial Intelligence [Deep Analysis]
1500
Artificial Intelligence (AI) has come a long way from being the subject matter of science fiction to being the living and breathing reality of the 21s
Read More

by Kechit Goyal

16 Mar 2023