Deep learning (DL) is a subfield of machine learning (ML) that is concerned with artificial neural networks—algorithms inspired by the brain’s structure and function. Learning by observations and examples is innate to humans; deep learning aims to train computers for emulating this quality.
Where is deep learning in today’s world? It is everywhere: virtual assistants, autonomous vehicles, spam filters, product recommenders, speech recognition, image editing, this list expands into the hundreds. Deep learning is getting a lot of attention lately, and for a good reason—it’s helping to achieve results that were never possible before.
How Deep Learning (DL) Models Learn
A large set of labeled data and neural network architectures containing several layers are used to train deep learning models. At the heart of this learning is the ‘algorithm.’ The algorithm isn’t a complete set of instructions for a computer or a computer program. Instead, it is a limited sequence of steps aimed at solving a particular problem.
At its core, ML is based on trial and error. We can’t write a program manually to help an autonomous car distinguish a pedestrian from a vehicle or other objects. However, we can create an algorithm that solves this problem using data.
Deep learning (DL) works in the same way but on a deeper level. Inspired by how the human brain functions and learns, DL requires high-end machines capable of crunching numbers and enormous volumes of ‘big’ data. Unlike the ML algorithms that break down parts to create data categories for solving them individually, DL solves the problem end to end. The more data and time you feed a DL algorithm, the better it gets at solving a task.
Deep learning (DL) follows a model that is similar to ML’s, i.e., inputs on the left, the learning process in the middle, and outputs on the right. However, the model involves more than one algorithm. Also called artificial neurons, algorithms in DL are stacked together side-by-side or on top of each other to perform the process of learning.
This network of interconnected neurons is called an artificial neuron network (ANN). The neurons learn through a hierarchical representation of the data. Each group of similar neurons called layer abstracts information at their level and passes it on to the next layer.
Understanding the Deep Learning Applications
An early application of ANN was handwriting recognition, used to convert images into machine-readable text — a technology now known as OCR. Since 2012, stepping beyond traditional machine-learning approaches into a new cognition level, DL has grown to offer valuable applications to our everyday life.
It is now present in nearly everything we use computers for. Some examples include automotive navigation systems, machine translation, text generation and classification, image recognition and face tags, image caption generation and colorization, voice search and virtual assistants, self-driving cars, movie and book recommenders, future and spot market price prediction, earthquakes prediction, and cancer diagnostics.
This is certainly the computational advancements that have led to the rising interest in DL and its applications. However, there are other aspects involved, and the most outstanding one is business values. Apple, Amazon, Facebook, and Google would not invest millions in this area if it was not for the benefits they gain from it.
This has created a hyper-competitive environment that has kept companies like Google and Amazon racing endlessly, fighting to achieve a bit more profitability and market share. This has created an environment where businesses build capabilities and more access and control over data.
Two developments have hugely contributed to this upward trend: super-sized datasets that were unimaginable to handle even ten years ago, and the boom in IoT that depends on sensors constantly producing data. This trend does not seem to be less than a business opportunity.
Deep Learning and Artificial Intelligence
When discussing deep learning, it is important to touch upon its connection to artificial intelligence (AI). Many folks use the terms ML, DL, and AI interchangeably. So, this might serve as a clarification.
AI is one of the largest and most nebulous subjects in Computer Science. It ranges from studying thinking machines to creating and optimizing algorithms used in ML. AI is an ensemble of methods that include sensing, reacting, producing motion, etc. All of this to get closer to human intelligence and to perform what is easy for humans to do but not yet for computers. The term was coined by John McCarthy in 1956. However, the journey began much earlier than that.
Some of the current applications of AI include patrolling robots, smart homes, criminal identification, and virtual nurses and trainers. AI is still a wide-ranging subject with no clear definition to its boundaries, a fact that holds true for human intelligence. How do we quantify a machine’s level of intelligence as compared to humans? That is yet to be answered. But how does it relate to ML and DL? Both ML and DL are subfields and key pieces to AI. ML is a broader category than DL and involves more traditional statistical and computational techniques.
The successful applications of DL are numerous, but so are its failures: fatality in Tesla autopilot, a ‘crime-fighting’ robot injuring a 16-month child, Microsoft’s Tay.ai leaving racist remarks, etc. For this reason, it is crucial to understand how deep learning is being applied, what developments are being made in the field, and which possible pitfalls to watch out for. This can go a long way in enabling DL for competitive advantage and using it to fuel business growth.
by Bobby J Davidson
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