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8 Deep Learning Skills You Need to Master to Land the Next Cool Job

The machine learning (ML) market is valued at nearly $21 billion in 2022 and is expected to reach $209.91 billion by 2029 at a whopping CAGR of 38.8%. Deep learning is the sub-segment or subset of ML — more so, an evolved branch of ML that is immensely high in demand. From healthcare to entertainment, the use cases of deep learning across sectors are manifold.

And that begs the question – what are the best skills to master deep learning? To answer that, we have rounded up the top and most valuable skills to climb the ladder of deep learning and master it from head to toe.

Top 8 Deep Learning Skills to Master for an Exponential Growth Trajectory

Every deep learning engineer has to work through various domains. These include but are not limited to data engineering, modeling, framework, analysis, and more. To succeed in these domains, various skills need to be mastered.

1.    Programming/Coding Fundamentals

Deep learning engineers are supposed to write both prototype and production codes, which need a better understanding of programming fundamentals. Companies are ideally looking for someone compatible with writing and understanding AI and ML-specific languages. Thus, coding skills are something that needs to be there on your resume. For starters, you can learn Spark, Hadoop, or Python.

2.    Machine Learning and Deep Learning Algorithms

A variety of machine learning algorithms are employed to run the simplest to the most sophisticated of tasks across industries. While it’s not necessary to learn ML algorithms for advancing to deep learning, it doesn’t harm to get versed with them to have a head start. After all, deep learning is technically a subfield of ML.

Notably, a profound understanding of top deep learning algorithms like CNNs, RNNs, GANs, MLPs, SOMs, etc., is critical for progress in the field.

3.    Data Structures and Algorithms

Since deep learning concentrates heavily on Mathematics, proficiency in data structures and algorithms is a must-have skill. The ability to map out different problems and their solutions is what makes software engineers, data analysts, and data scientists successful in their respective fields.

4.    Deep Learning Framework

To build the deep learning framework, the candidates are required to have a working knowledge of PyTorch and TensorFlow libraries, among others. These are ideal for deep learning research. Companies usually use these to test the framework or model in the research phase before it is ready for deployment or use. These two are ideal sources to find how seamless the framework is. Thus, understanding framework creation and checking these sources is essential.

5.    Neural Network Architecture

In-depth knowledge of neural network architecture is an essential skill that most organizations are on the lookout for. The ability to create a network that mimics how the brain works is the responsibility of a deep learning engineer. This is done with the help of neural networks, which are nothing but statistical models. Any input crosses several layers of architecture to produce a desirable output without human intervention.

6.    Natural Language Processing

Natural language processing (NLP) is a combination of machine learning and deep learning models. It allows the computer to understand the textual and verbal instructions to carry out specific actions. Speech recognition, speech deciphering, finding ambiguity, and understanding sentiments are some tasks that fall under this area. Therefore, mastering NLP makes a lot of sense.

7.    Communication and Soft Skills

Besides the technical skills, a candidate must have excellent communication skills. The ability to foster a better relationship with the stakeholders or clients is what makes them more effective in their work.

For deep learning engineers, data scientists, analysts, and software engineers, soft skills are incredibly important to present their work in the best light. Their contributions to the product, marketing, design, and development teams are crucial for the company’s growth — something that necessitates them to possess strong verbal and non-verbal communication skills.

At the end of the day, deep learning engineers are supposed to be adaptable and team players, as their projects require a cumulative joint effort.

8.    Logical Thinking and Problem-Solving Capability

Today, deep learning is being used to solve the most practical issues in different business spaces. Therefore, every technical expert is required to have logical thinking and problem-solving capability. When working on high-end algorithms, codes, and networks, it is vital to think out of the box to solve unseen issues and bugs.

Technology-heavy jobs require aspirants to always be on their toes. So, if you are looking to grab the next cool job in the space of deep learning, then start upskilling yourself with the skills and competencies mentioned above.

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