You and I live in a world where, for better and for worse, we are constantly surrounded by algorithms. From song recommendations to driverless cars, and from financial fraud detection to medical image processing. Every sector has benefitted from the rapid incorporation of deep learning algorithms. The advancement of deep learning techniques has opened a range of opportunities. These advancements are happening fast because now “we have a software essentially writing software” says Jen-Hsun Huang, CEO of graphics processing leader, Nvidia.
The data revolution started in your lifetime, and you want to be a part of it. You feel the necessity to learn digital techniques. You realise that deep learning with its quick progression is the new modern approach to solve complex equations and upskilling in this domain will take you to new heights. However, there are certain things you should know before you dive deeper into the field of deep learning.
Learn the differences between artificial intelligence, machine learning and deep learning.
This article examines the prerequisites for an efficient Deep Learning career. To understand the concepts and have a smooth learning journey. We recommend you meet the following requirements:
Mathematics is at the core of machine learning and deep learning. It provides the means of implementing how your system’s goal can be reached.
Consider automated vehicles, for decades, we could not make any progress in training computers to recognize people and objects. The breakthrough came in when we realized that highly parallel networks with simpler nodes can solve this problem, provided we know how to train them.
Mathematics gives us a robust answer in the form of minimizing procedures and back-propagation when training models. This is why mathematics is crucial because it shows you how to solve difficult abstract problems.
The following are the minimum level of mathematics you need to be a deep learning researcher/engineer.
2. Linear algebra
The concepts of linear algebra are the most essential ingredient for the recipe of deep learning algorithms. It gives you better insights into how your algorithm works under the hood. If you are starting with machine learning, you don’t need to understand any of those fancy linear algebra. However, Eventually, as you progress and make your transition into deep learning, you should deepen your understanding. This will aid you to make better decisions during your system’s development. The benefits of learning linear algebra are manifold. It improves your math skills, programming skills, and prepares the learner to learn and explore the broader side of ML. Linear Algebra makes more sense to a regular practitioner. Once you see how the operations work on real data, it is fun, and then it is hard to avoid developing a strong intuition for the methods.
Most of us last saw calculus in school, but derivatives are a critical part of deep neural networks, which are trained by optimizing a loss function. It’s not just any old scalar calculus. You need differential matrix calculus, the shotgun wedding of multivariate calculus and linear algebra. Maybe need isn’t the right word, Thanks to the automatic differentiation built into modern deep learning libraries. But, if you want to grok what is happening inside these libraries, you better understand certain bits of the field of matrix calculus.
Probability is the art of quantifying uncertain things. A deep learning system utilizes a lot of data to derive patterns in the data. Whenever a vast amount of data is used, uncertainty grows up, and that is when probability becomes relevant. Mastering the art of probability aids you in establishing the motivations of your deep learning system and validating the results yielded by the system. Several models in deep learning such as the bayesian model, probabilistic graphical model, and hidden markov models depend entirely on probability concepts.
To implement your DL aspirations, you should use a programming language that is flexible, stable and has a variety of tools, Python offers all of this. Which is why Python is the most preferred language in general. Python is renowned for its combination of simplicity, shorter development time and consistent syntax. This has several advantages when it comes to machine learning and deep learning. As a DL researcher, you will have to work on extremely complex algorithms, so the less you have to worry about your intricacies of coding, the more you can focus on your model and achieve the goals of the project. Additionally, Python boasts a large active group who are happy to help, which can be indispensable when building complex projects. While other programming languages can be useful, there is no escaping from the fact the Python is the cutting edge, so start learning it if you want to get into the deep learning club.
6. Basic Machine learning
Deep learning is a specific kind of machine learning. To understand deep learning. You don’t need to know every single machine learning algorithm or how it works. Nevertheless, it is essential that you understand basic concepts about how to evaluate machine learning models in general. Before you start with deep learning, I recommend you to get familiar with machine learning paradigms, How to measure a model’s accuracy, the concepts of underfitting and overfitting, etc. otherwise you would be lost in even understanding what neural net is supposed to do and how to test it. Besides, learning classical machine learning provides a theoretical background.
Great Learning’s Deep Learning Certificate program is a comprehensive course that teaches you the essentials of the discipline and its industrial applications.