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A Complete Guide on Getting Started with Deep Learning in Data Analytics
Deep learning is a method of processing information and finding communication patterns in biological nervous systems. In layman terms, it is a way of trying a find a relationship between various stimuli and the resulting response of the neural network in the brain. It tries to understand how human brain reacts to situations and this knowledge is used to develop smart processes and systems that can imitate human behaviour. In short it is the base of artificial intelligence. Data analytics experts are gaining knowledge of deep learning by enrolling in some of the best big data analytics courses available online. These courses will help them in enhancing their career prospects as it is a fast-emerging field.
Experts are excited about deep learning architecture as they can use it in various fields. Some examples of deep learning architecture are deep neural networks, deep belief networks etc. These are applied in fields like speech recognition, natural language processing, audio recognition, machine translation, social network filtering, drug design, computer vision etc.
To understand deep-learning you may need to have knowledge of linear algebra, calculus, statistics and some basic programming expertise. All of these subjects are already taught in school. However, if you are not comfortable with these subjects, you can start by going through some free online tutorials on these topics. Once you have a basic knowledge of these topics, you can move forward to learning deep learning. There are several online courses, video tutorials, podcasts and blogs to help you get started. You should look at a few courses online. You can start with the free courses to understand what suits you best.
Once you have developed an understanding of what is deep learning and what should you expect to learn in a course about it, you will need to choose the framework on which you can learn it. A deep learning framework is a tool that you use to solve a deep learning problem. Some examples of the tools prevalent today are TensorFlow, PyTorch, Apache MXNet etc. Each tool has its own strengths. To choose which one is best for you, you should research about them to know which one suits the problem that you are working on.
TensorFlow is the most popular Deep Learning framework today, because it has a good computational graph visualization and well documented APIs. It is a flexible framework which will prove useful to you from the beginning stage to the final production stage. Apache MXNet is another flexible framework that will support several languages and programming. It works on an open ecosystem for interchangeable artificial intelligence models. Another framework, Microsoft Cognitive Toolkit (CNTK) is good if you are working on speech recognition.
Once you have zeroed in on a framework, you have to decide where will you run the framework. You need a Linux based personal computer and a modern graphics processing unit (GPU). Most of the frameworks can use any computer graphics card which are commonly used for gaming for GPU acceleration. It is only once you have moved on to an advanced level of learning in deep learning, that you need to invest in an expensive and powerful GPU that is built for AI. There are cloud-based GPU services like Amazon EC2 also available where you need to pay by the hour.
After making all the above choices, you need to get all of these running. You will have to download the framework and for that you will need to get your hardware and the operating system versions in order. This might require choosing the right versions of the framework, the operating system, any installations or upgrades and selecting the correct hardware type. Since this requires a lot of adjustments and work, you can choose something called containerised frameworks. This is a service that provides the whole package of a framework along with the required components. It keeps the ready to use containers up to date by testing and checking them regularly. This is a good way to save your time and energy on setting up the system for learning.
Deep learning is one of the most exciting and intellectually rewarding fields to work in today. It will help you to have a mathematics background if you want a career in it as this field is hardcore analytics. However, as most deep learning enthusiasts are existing data analytics professionals this is not an issue. Deep learning is all set to augment data analytics as it provides data analytics the opportunity to make real life difference in the daily lives of people. It can help us drive, control robots and several large-scale operations, communicate with machines and manage our environment more efficiently. As a highly innovative field, sky is the limit for the professionals who want to make a mark in it.
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