How to Pursue a Successful and High-Paying Career in Deep Learning?

Here we have assembled what you should know to land a high-paying and deep learning job in 2022.

Deep Learning is a subfield of machine learning concerned with algorithms. It is based on artificial neural networks with representation learning. It is a machine learning technique that teaches computers to do what comes naturally to humans. ML is a subset of artificial intelligence and DL is, in turn, a subset of ML. Though DL is a growing field, which is getting a lot of attention, getting a job in deep learning is very difficult.

Deep learning is one of the most promising developments in the field of AI research. DL is a very deep and complex artificial neural network, usually referred to as deep neural networks. DL uses unstructured data to build artificial neural networks. DL offers lucrative career opportunities for interested programmers and computer scientists. DL tech is being leveraged in natural language processing, image recognition, speech-to-text processing, audio recognition, object detection, etc. But before taking a DL career there are a few things to know.

As the deep learning field is taking off, it is becoming an essential component in software development. DL is powering innovations in fields like law enforcement and healthcare, helping the experts identify patterns and prescribe unique solutions. Here is the list for pursuing a successful high-paying career in DL:

How to get started in Deep Learning:

Those who are focusing on a career in deep learning should focus on the algorithms and state-of-the-art novel engineering and software technologies required for deep neural network systems. It includes algorithms, applications of data analytics and big data technologies, ML and decision-making algorithms, digital signal processing principles, python programming languages, etc.

Deep Learning Concepts:

• Mathematics, calculus, linear algebra, probability, and Statistics are crucial to building a strong DL career.
• Classification, linear regression, logistic regression, continuous probability estimation, etc are learning parts in ML.
• The application of Convolutional Neural Networks (CNN) is crucial for real-world cases.
• Generative Adversarial Network (GAN) are generative models and it's essential for creating images of human faces that don't even exist in the real world in DL.
• Theano and TensorFlow are the top 2 python platforms in DL. Keras is a minimalist Python library specially designed for DL's clean and accessible environment.
• Should learn how the various Deep Learning concepts and techniques can be used in NLP tasks.
• Sequence models include Deep Learning techniques like Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), and Gated Recurrent Unit (GRU).
• It is essential to know how to debug a deep learning model by validating the input data, testing it, and ensuring that the data is correctly split into a training set and test set.
• Learn how to improve and enhance the models to obtain the best possible results.
• Proficient in handling and pre-processing image data, understanding hyperparameter tuning, and transfer learning to improve the performance of the DL model.

Career opportunities in Deep Learning:

For those who acquire skills in deep learning, there is a number of job opportunities in multinational corporations across India and the world in various domains, including software engineers, DL scientists, electronic engineering systems analysts, DL engineers, data insight analysts, image processing engineers, etc.