Deep Learning in Python

Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network

How Deep Learning Works

Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world. This data, known simply as big data, is drawn from sources like social media, internet search engines, e-commerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be shared through fintech applications like cloud computing.

However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to AI systems for automated support.

Advantages of Deep Learning

• Ability to generate new features from the limited available training data sets.
• Its ability to work on unsupervised learning techniques helps in generating actionable and reliable task outcomes.
• It reduces the time required for feature engineering, one of the tasks that require major time in practicing machine learning. (Speaking of machine learning, you can also check out our latest blog on the topic)
• With continuous training, its architecture has become adaptive to change and is able to work on diverse problems.

Disadvantages of Deep Learning

With the increasing popularity, deep learning also has a handful of threats that needs to be addressed:

• The complete training process relies on the continuous flow of the data, which decreases the scope for improvement in the training process.
• The cost of computational training significantly increases with an increase in the number of datasets.
• Lack of transparency in fault revision. No intermediate steps to provide the arguments for a certain fault. In order to resolve the issue, a complete algorithm gets revised.
• Need for expensive resources, high-speed processing units, and powerful GPU’s for training to the data sets.

Practical Examples of Deep Learning

1. Virtual Assistants: The core functionality that requires translating the speech and language of the human’s speech, is deep learning. The common examples of virtual assistants are Cortana, Siri, and Alexa.
2. Vision for Driverless, Autonomous Cars: In order to navigate an autonomous car, say, a Tesla, one needs a human-like experience and expertise. To understand the scenarios of roads, the working of signals, pedestrians, significances of different signs, speed limits, and many more situations like these, a large amount of real data is required. With the large data, the efficiency of the algorithms will be improved which will subsequently increase the decision-making flow.
3. Service and Chat Bots: The continuous interaction of chatbots with human beings for providing customer services requires strong responses. To respond in a helpful manner to all the tricky questions and apt response, deep learning is required for training algorithms.
4. Translations: Translating the speech automatically in multiple languages requires deep learning supervision. This is a helpful mechanism for tourists, travelers, and government officials.
5. Facial Recognition: Facial recognition has many features from being used in security to the tagging mechanism/feature used on Facebook. Along with the importance, it has its fair share of issues as well. For example, to recognize the same person with weight gain, weight loss, beard, without a beard, new hairstyles, etc.
6. Shopping and Entertainment: All the shopping applications like Amazon and Myntra and entertainment applications like Amazon Prime and Netflix store your data and buying habits to show the suggestions for future buying and watching. It always comes as a title “You may like to watch/buy”. The more data is inputted in the Deep learning algorithm, the more efficient it becomes in decision making.
7. Pharmaceuticals: Customizing medicines based on the particular genome and diseases. Deep learning has widened the scope of such applications and has gained the attention of the largest pharmaceutical companies.