Everything You Need to Know About Deep Learning
Deep learning is an artificial intelligence (AI) function that mimics/copies the human brain’s processing of data and pattern creation in order to make decisions. Deep learning is an artificial intelligence branch of machine learning that uses neural networks to learn unsupervised from unstructured or unlabelled data. Deep neural learning or deep neural networks are other terms for the same.
Key Learning’s from Deep Learning
- Deep learning is an artificial intelligence function that mimics the human brain’s processing of data to identify objects, recognise expression, translate languages, and make decisions.
- Deep learning AI can learn without the need for human intervention, using both structured and unlabelled data.
- Deep learning, a form of machine learning, can aid in the detection of fraud and money laundering, among other things.
How Deep Learning works?
Deep learning has progressed in lockstep with the modern age, which has resulted in an avalanche of data in all types and from all corners of the globe. Big data is gathered from a variety of outlets, including social media, internet search engines, e-commerce websites, and online cinemas. This massive volume of data is easily available and can be shared using fintech tools such as cloud computing.
However, since the data is typically unstructured, it could take decades for humans to comprehend and extract useful knowledge. Companies are gradually adopting AI systems for automated support as they recognise the enormous potential that can be realised by unlocking this wealth of data.
Deep Learning vs. Machine Learning
Machine learning, a self-adaptive algorithm that gets progressively better analysis and patterns with experience or newly added data, is one of the most popular AI techniques used for processing big data.
Machine learning software could be used by a digital payments company to identify the occurrence or potential for fraud in its system. A computational algorithm built into a computer model will process all transactions that take place on the digital network, look for patterns in the data collection, and flag any anomalies found by the pattern.
Deep learning, a subset of machine learning, employs artificial neural networks at a hierarchical level to carry out the machine learning process. Artificial neural networks are constructed in the same way as the human brain, with neuron nodes linked in a web-like pattern. Although conventional programmes construct analysis with data in a linear manner, deep learning systems’ hierarchical function allows machines to process data in a nonlinear manner.
A conventional approach to detecting fraud or money laundering would rely on the amount of transaction that follows, while a deep learning nonlinear technique might take into account time, geographic location, IP address, retailer sort, and any other feature that might indicate fraudulent behaviour. The neural network’s first layer processes raw data inputs such as transaction amount and passes it on to the next layer as output. The second layer takes the information from the first layer and adds additional information such as the user’s IP address before passing it on.
The next layer takes the information from the previous layer and adds raw data such as geographic location to improve the machine’s pattern. This happens at any stage of the neuron network.
Example of Deep Learning
A deep learning example can be created by combining the fraud detection method described above with machine learning. If the machine learning system developed a model with parameters based on the amount of money a user sends or receives, the deep-learning method will begin to build on the machine learning results.