Deep learning models are capable to focus on the right features by themselves, with little guidance from the programmer. They can generate the features on which the outcome will depend on. At the same time, it also solves the dimensionality problem as well. If you have a large number of inputs and outputs then you can make use of deep learning algorithm. You might wonder what deep learning is. Deep learning is evolved from machine learning, whereas machine learning is nothing but the subset of AI, and the idea behind AI is to imitate human behavior. This idea is also with deep learning as well. It builds a learning algorithm that can mimic brain using matrix mathematics. You can refer this article to understand the difference between machine learning and deep learning in detail.

Deep learning is implemented through neural networks, and the idea behind neural networks is nothing but neurons (brain cells). It is implemented with the help of deep networks and deep networks are nothing but the neural networks with multiple hidden layers.

Why has deep learning evolved recently?

Deep networks have become feasible because of two reasons.

1) Increased computing power

2) A huge amount of data.

Wondering, why does a neural network need so much data? Essentially, the more data there is, the more robustly the network can be trained.

Put yourself in the shoes of a deep learning network trying to recognize a dog for example. There are many camera angles and lighting conditions to work with but on the other hand, if you have seen thousands of different dogs you’d have a much easier time to recognize one. This is the importance of data. Computers aren’t as intuitive as humans and need more examples in the field of deep learning; data is the essence that allows machines to learn. Thanks to the exponential growth of computing power and the vast amount of data, deep learning networks have gained the ability to recognize objects and translate speech in real time. They are everywhere from our phones to smart home systems.

This technology is also making personal health care easier. Data from the wearable provide patient’s specific information directly to their healthcare professionals. It can even help predict a range of diseases by analyzing eyeball retinas. It can diagnose any other disease as accurately as doctors. Moreover, the forthcoming years will be dominated by the deep learning trends in 2019.

1) Building AI innovations on Cloud

In 2019 and beyond, business endeavors will look to enhance their mechanical foundation and cloud facilitating processes for supporting their machine learning and AI efforts. As deep learning influences organizations to advance and enhance with their machine learning and AI contributions, more specific tooling and framework should have been facilitated on the cloud to help altered utilize cases, like solutions for combining multi-modular tactile contributions for human collaboration (think and touch).

2) Create new AI audit trails

AI and its adaptability accompany one of the greatest obstructions to its organization, especially in directed enterprises, is the clarification with respect to how AI achieved a choice and gave its expectations. 2019 will be marked as a new era in making AI audit trails clarifying the nitty-gritty of how AI and deep learning achieve a conclusion.

In future, AI will be deployed for historic applications like drug recovery which can detrimentally affect human life if a wrong choice is made. Therefore, audit trails to AI and deep learning forecasts are extremely critical.

3) AI the hyped reality


Deep learning controlled Robots that do daily household work, self-driving cars and automation cabs could be fun and enormously profitable yet exists in far away future than the hype recommends. The overhype encompassing AI and deep learning will propel venture capitalists to divert their capital somewhere else to the following enormous thing like 4d printing or quantum computing. Entry bars for deep learning venture speculations will be higher and by then, the AI bubble will dive. To maintain a strategic distance from that, innovation needs to assist clients with recognizing that AI, machine learning and deep learning are significantly more than just buzzword, and have the ability to make our daily lives much easier. Reality says the time is ready to spend less effort on the exploration of deep learning outcomes and rather center on conveying solutions to real-life problems.

4) AI the shining star

Gone are the days when AI was the toast of science fiction motion pictures; however innovation has at last gotten up to speed with creative energy and versatility. At present, AI has turned into a reality and incredibly, business and society experience some type of AI in their ordinary activities.

Deep learning has significantly enhanced the manner in which we live and cooperate with innovation. Amazon’s deep learning offering Alexa is fuelled to do various capacities by means of voice communications, such as playing music, doing online shopping, and answering factual inquiries. Amazon’s most recent offering, AmazonGo that takes a shot at AI enables customers to leave a shop with their shopping sacks and consequently get charged with a bill receipt sent specifically on their registered mobile numbers.

5) Influencing Bias

Human bias is a noteworthy test for a dominant part of decision-making models. The distinction and fluctuation of AI calculations depend on the information sources they are fed. Data researchers have reached a resolution that even machine learning arrangements have their very own inclinations that may trade off on the integrity of their information and yields. AI biases can go undetected for various reasons, unmistakably being training data biases. Bias in training datasets impacts certifiable applications that have come up from the biases in machine learning datasets including ineffectively targeted on online marketing campaigns, racially unfair facial algorithm and sexual orientation enrolling inclinations on employment sites.


AI establishes the framework for a new era and a considerable lot of the achievements in innovation are simply founded on this. In this post, we’ve secured a portion of the prominent patterns in deep learning. The self-driving car utilizes a blend of various models, for example, deep reinforced learning and convolutional neural systems for visual acknowledgment.

Deep learning as technology has the ability to complete highly skilled work that was traditionally expensive. It could lead to the new scientific breakthroughs and a drastic fall in the price of goods and services.

Meanwhile, if the idea behind deep learning fascinates you then you can enroll for the course Deep Learning with Keras and Python covering machine learning, convolutional neural networks, graph-based models and much more interesting topics!