Want to Become a Deep Learning Engineer? You Need to Read This First

Introduction:

We live in the period of the Big Data revolution where almost all industries produce mountainous amounts of data. This leads to unfamiliar challenges concerning their analysis and interpretation. For this reason, there is a critical need for novel machine learning and artificial intelligence methods that can assist in utilizing these data.

Definition:

Deep learning is an Artificial Intelligence function that imitates the functioning of the human cerebrum in maneuvering information and creating designs for use in judgment. Deep learning is a variant of Machine Learning in AI that has networks to encompass unsupervised learning from information that is unlabelled or unstructured.

Deep learning is a variant of machine learning, which is simply a neural network with three or more layers. These neural networks make efforts to instigate the behavior of the human brain allowing. While a neural network with a single layer can formulate estimated predictions, supplementary hidden layers can alleviate optimization and improve accuracy.

Deep learning, also referred to as deep neural learning and deep neural network, is an offshoot of machine learning that has progressed and transformed with the advancements in technology.

Real-World Applications

Deep Learning has become a slice of our daily life. Haven’t you noticed that for every photo you upload on Facebook, the app lets us tag people automatically? Why is that? Indeed it’s the deep learning that’s minimizing your tasks right there. Aren’t we all familiar with digital assistants like Alexa, Siri, Cortana, and Google Assistant? How do they instantaneously function as per our commands? Moreover, don’t they use our own mother tongue for us or every other language in the whole wide world?

This is the technique behind Deep Learning, the mechanism enriches machines to acquire human behavior and simplify our tasks to a large extent. Other applications of Deep learning include:

• Automatic Handwriting generation
• Self-driving cars
• Language translation
• Virtual assistants
• Image and fingerprint recognition functions
• Open source platforms with customer recommendations
• Banking apps
• Medical research tool
• Business trends and outcomes

Even Netflix and Amazon are upgrading their deep learning potential to dispense a personalized experience to their viewers by showing preferences, time of access, history, and recommend shows that the viewers could be interested in.

According to GlobeNewswire news, in 2019 the value of the global machine learning market was $8 billion and is predicted to hit USD 117 billion by the end of 2027.

Scope:

Today, the scope of deep knowledge for its flexibility and adjustability is incalculable. Deep learning techniques aid processes and examine the accessible Big Data via systematic programming, disciplines, and codes in order to dispatch judgments and inferences, to develop patterns and trends.

How to learn deep learning courses?

Professionals with a minimum bachelor’s degree in technical areas like computer science, statistics, physics, or electrical engineering can pursue deep learning courses. Anyone whose work is related to data analysis and wishes to study critical concepts, formulations, algorithms on which it is based. Anyone interested in deeper understanding, in-depth knowledge, and experience in the industry.

Who is a Deep learning engineer?

Deep learning research engineers are in a way computer scientists. They are experts in using deep learning platforms for specific types of programming functions to artificial intelligence. They aim to develop programming systems that impersonate brain functions.

Qualification:

Deep learning engineers should have the ability to work as part of a team, give attention to detail, have analytical skills, problem-solving skills, communication skills, mathematical skills, expert research skills, and computer programming skills.

Skills:

A programming language suitable for AI/ML/DL
Python and R are the common languages preferred for Deep Learning
Computer Science Fundamentals and Data Structures are inevitable for a Deep Learning Engineer to have knowledge about
It is obligatory to have command over the Software Engineering skills such as in Data Structures, Software Development Life Cycle, Github, and Algorithms.
Understanding Mathematics and Statistics would only be beneficial
Knowledge of cloud computing platforms

What are the tasks that are carried out by the Deep learning engineer?

Deep learning engineers execute data engineering, modeling, and deployment functions. For example: defining data requirements, collecting, labeling, inspecting, cleaning, augmenting, moving data, defining evaluation metrics, searching hyperparameters, reading research papers, converting prototyped code into production code, setting up a cloud environment to deploy the model, or improving response times and saving bandwidth are also important tasks of a Deep learning engineer.

The Advantages of Deep Learning courses :

• This is your chance to master industry-relevant and latest technological advances in the field.
• Receive all-around expertise in the field by learning about deep learning techniques and approaches
• Initiating a reliable peer connection with people working in the same industry and in the same discipline of technical science.
• Getting a better grasp over coding, algorithms, programming, and every other advanced technology and statistical development.
• Improving knowledge of machine learning and artificial intelligence to acquire a better picture of the industry standards and requirements.

Deep learning has helped humans unfold a brand new field with enormous scope and opportunities. It is an eminent career path producing propitious and remunerative jobs where both the technical skills and the creative faculty of people will be at the apex. Deep learning is one of the most revolutionary technological fields that mankind has laid its hand upon.

Tools:

• Modeling is mainly executed in Python employing packages such as NumPy, Sci-kit-learn, Pandas, Matplotlib, TensorFlow, and PyTorch.
• Data engineering can take place in Python or SQL.
• Cloud technologies such as AWS, GCP, and Azure are used to implement object-oriented programming languages.
• Deep learning engineers are protean individuals. They have both engineering and scientific skills that allow them to carry out most of the work of an AI project.

Various names of DL Engineer:

Companies may regard this role as deep learning engineer, software engineer, machine learning engineer, software engineer, software engineer, data scientist, algorithm engineer, research scientist, research engineer or full-stack data scientist according to the company’s nature and requirements.

Salary:

As per Indeed’s report, the average salary for a deep learning engineer is 62,404 INR per month in India.

Conclusion:

Aren’t you captivated by the field of Deep Learning? Do you want to pursue your career as a Deep Learning Engineer? Grab a Deep Learning Certification from DataMites and hit it big.