Have you ever wondered how Google’s translator App can translate entire paragraphs from one language into another in a matter of milliseconds?

How Netflix and YouTube can figure out our taste in movies or videos and give us appropriate recommendations?

Or how self-driving cars are even possible? All of this is a product of Deep Learning and Artificial Neural Networks. The definition of Deep Learning and Neural networks will be addressed in the following.

A newly re-invigorated form of machine learning, which is itself a subset of artificial intelligence, deep learning employs powerful computers, massive data sets, “supervised” (trained) neural networks, and an algorithm called back-propagation (the backdrop for short) to recognize objects and translate speech in real-time by mimicking the layers of neurons in a human brain’s neocortex.

Artificial Intelligence is a general term that refers to techniques that enable computers to mimic human behavior. Machine Learning represents a set of algorithms trained on data that make all of this possible.

How Deep Learning Works

Neural networks are layers of nodes, much like the human brain is made up of neurons. Nodes within individual layers are connected to adjacent layers. The network is said to be deeper based on the number of layers it has. A single neuron in the human brain receives thousands of signals from other neurons. In an artificial neural network, signals travel between nodes and assign corresponding weights. A heavier weighted node will exert more effect on the next layer of nodes. The final layer compiles the weighted inputs to produce an output. Deep learning systems require powerful hardware because they have a large amount of data being processed and involves several complex mathematical calculations. Even with such advanced hardware, however, deep learning training computations can take weeks.

Commonly-Used Deep Learning Applications

1. Virtual Assistants: Amazon Echo, Google Assistant, Alexa, and Siri are all exploiting deep learning capabilities to build a customized user experience for you. They ‘learn’ to recognize your voice and accent and present you a secondary human experience through a machine by using deep neural networks imitating not just speech but also the tone of a human. Virtual assistants help you shop, navigate, take notes and translate them to text, and even make salon appointments for you.
2. Facial Recognition: The iPhone’s Facial Recognition uses deep learning to identify data points from your face to unlock your phone or spot you in images. Deep Learning helps them protect the phone from unwanted unlocks and making your experience hassle-free even when you have changed your hairstyle, lost weight, or in poor lighting. Every time you unlock your phone, deep learning uses thousands of data points to create a depth map of your face and the inbuilt algorithm uses those to identify if it is really you or not.
3. Personalization: E-Commerce and Entertainment giants like Amazon and Netflix, etc. are building their deep learning capacities further to provide you with a personalized shopping or entertainment system. Recommended items/series/movies based on your ‘pattern’ are all based on deep learning. Their businesses thrive on pushing out options in your subconscious based on your preferences, recently visited items, affinity to brands/actors/artists, and overall browsing history on their platforms.
4. Natural Language Processing: One of the most critical technologies, Natural Language Processing is taking AI from good to great in terms of use, maturity, and sophistication. Organizations are using deep learning extensively to enhance these complexities in NLP applications. Document summarization, question answering, language modeling, text classification, sentiment analysis are some of the popular applications that are already picking up momentum. Several jobs worldwide that depend on human intervention for verbal and written language expertise will become redundant as NLP matures.
5. Healthcare: Another sector to have seen tremendous growth and transformation is the healthcare sector. From personal virtual assistants to fitness bands and gears, computers are recording a lot of data about a person’s physiological and mental condition every second. Early detection of diseases and conditions, quantitative imaging, robotic surgeries, and availability of decision-support tools for professionals are turning out to be game-changers in the life sciences, healthcare, and medicine domain.
6. Autonomous Cars: Uber AI Labs in Pittsburgh are engaging in some tremendous work to make autonomous cars a reality for the world. Deep Learning, of course, is the guiding principle behind this initiative for all automotive giants. Trials are on with several autonomous cars that are learning better with more and more exposure. Deep learning enables a driverless car to navigate by exposing it to millions of scenarios to make it a safe and comfortable ride. Data from sensors, GPS, geo-mapping is all combined together in deep learning to create models that specialize in identifying paths, street signs, dynamic elements like traffic, congestion, and pedestrians.
7. Text Generation: Soon, deep learning will create original text (even poetry), as technologies for text generation are evolving fast. Everything from the large dataset comprising text from the internet to Shakespeare is being fed to deep learning models to learn and emulate human creativity with perfect spelling, punctuation, grammar, style, and tone. It is already generating caption/titles on a lot of platforms which is testimony to what lies ahead in the future.
8. Visual Recognition: Convolutional Neural Networks enable digital image processing that can further be segregated into facial recognition, object recognition, handwriting analysis, etc. Computers can now recognize images using deep learning. Image recognition technology refers to the technology that is based on digital image processing technology and utilizes artificial intelligence technology, especially the machine learning method, to make computers recognize the content in the image. Further applications include coloring black and white images and adding sound to silent movies which have been a very ambitious feat for data scientists and experts in the domain.

Why is Deep Learning Important?

To say Deep Learning is important is, to say nothing about its growing popularity. It contributes heavily towards making our daily lives more convenient, and this trend will grow in the future. Whether it is parking assistance through technology or face recognition at the airport, deep learning is fuelling a lot of automation in today’s world.

However, deep learning’s relevance can be linked most to the fact that our world is generating exponential amounts of data today, which needs structuring on a large scale. Deep learning uses the growing volume and availability of data has been most aptly. All the information collected from these data is used to achieve accurate results through iterative learning models.

The repeated analysis of massive datasets eradicates errors and discrepancies in findings which eventually leads to a reliable conclusion. Deep learning will continue to make an impact in both business and personal spaces and create a lot of job opportunities in the upcoming time.

Deep learning Process

A deep neural network provides state-of-the-art accuracy in many tasks, from object detection to speech recognition. They can learn automatically, without predefined knowledge explicitly coded by the programmers.

Each layer represents a deeper level of knowledge, i.e., the hierarchy of knowledge. A neural network with four layers will learn more complex features than that with two layers.

Learning occurs in two phases.

1. The first phase consists of applying a nonlinear transformation of the input and create a statistical model as output.
2. The second phase aims at improving the model with a mathematical method known as derivative.

The neural network repeats these two phases hundreds to thousands of times until it has reached a tolerable level of accuracy. The repeat of this two-phase is called an iteration.

Advantages of Deep learning

In traditional Machine learning techniques, most of the applied features need to be identified by a domain expert to reduce the complexity of the data and make patterns more visible to learning algorithms to work. The biggest advantage of Deep Learning algorithms as discussed before is that they try to learn high-level features from data in an incremental manner. This eliminates the need for domain expertise and hardcore feature extraction.

Following are the benefits or advantages of Deep Learning:

1. Features are automatically deduced and optimally tuned for the desired outcome. Features are not required to be extracted ahead of time. This avoids time-consuming machine learning techniques.
2. Robustness to natural variations in the data is automatically learned.
3. The same neural network-based approach can be applied to many different applications and data types.
4. Massive parallel computations can be performed using GPUs and are scalable for large volumes of data. Moreover, it delivers better performance results when the amount of data are huge.
5. The deep learning architecture is flexible to be adapted to new problems in the future

Skills required to become a Deep Learning Engineer

A Deep Learning Engineer should possess:

1. Exceptional mathematical and statistical skills to perform complex computations.
2. In-depth understanding of data structures, data modeling, and software architecture.
3. The ability to work with various ML and DL frameworks and libraries like TensorFlow, Keras, Caffe, PyTorch, DeepLearning4J, Theano, etc.
4. The ability to write precise and efficient code in Python, Java, and R.
5. Excellent written and verbal communication skills.
6. Excellent analytical and problem-solving skills.
7. A creative bent of mind with attention to detail.

Deep Learning Engineer Salary

The Deep Learning Engineer Salary in India is determined by several factors such as a candidate’s educational qualifications, skill set, work experience, and also the company size and reputation, location, and job role offered. Typically, the salaries of Machine Learning job roles in India remain well above the market average.

The starting pay for Deep Learning Engineers in India can range anywhere between Rs. 3 – 15 LPA. Of course, individuals who fall on the higher end of the salary scale possess advanced qualifications, or have prior work experience, or may work for top players in the industry.

For instance, individuals with the following skills can demand a higher yearly compensation:

• Python/C++ Programming Language
• Natural Language Processing
• Software Development
• Big Data Analytics
• Image Processing
• Computer Vision
• Data Modelling
• Deep Learning
• Data Analysis

Then again, educational qualifications play a pivotal role in determining the salary scale. For instance, graduates with a Bachelor’s degree in Computer Engineering/Software Engineering can earn around Rs. 3.5 – 6 LPA, whereas those having a postgraduate degree in the same specialization or related fields (Electronic Engineering/Computer Science/Information Science), can make about Rs. 5 – 7.3 LPA. However, MBA graduates can earn a high starting salary (owing to their extensive knowledge of both the technical and business domains) of around Rs. 6 – 8.5 LPA or more.

Mid-level Deep Learning Engineers having more than eight years of work experience can earn an average annual salary of Rs. 7 – 12 LPA, whereas senior-level professionals having over 15 years of field experience can command salaries ranging between Rs. 25 – 48 LPA and more.

Wrapping up

As deep learning technology continues to improve, the list of potential applications is only likely to get longer and more impressive. We may be able to teach computers to recognize patterns, but human creativity will be essential in figuring out how best to put deep learning to work for society.

BIT is a training institute that offers Deep Learning courses in Vadodara and is committed to delivering the highest standard of teaching. We follow a project-based learning methodology that will help students learn to apply the techniques in the real world and students can opt to be part of our internship program where they can help the company to create deep learning products. Moreover, BIT has a team of highly trained faculty from industry and research fields. The curriculum of the course is planned under the guidance of the experts to ensure that the syllabus covers the latest developments in this field. Above all, we also offer placement assistance for deserving students. To reduce the financial burden for those who join our Deep Learning training in Vadodara, we offer EMI options by coordinating with our banking partners.

Join our intensive training program in Deep Learning and get started with the world’s top technology. Register online now!