When we look at the difference between AI and Machine Learning, simply put, ML is a subset of AI. AI is adding intelligence to machines, with a set of algorithms so as to do tasks that would require human intelligence. ML on the other hand is training machines, to empower them to learn from experience, so as to do tasks without human intervention.

Artificial neural networks or ANNs are computer system, that have self-learning capabilities and can simulate how human brains receive, comprehend and analyze information. ANN is the key tool to machine learning solutions and can learn to deliver better results with the availability of quality and quantity of data.

What is Deep Learning? Deep Learning is an AI function that mimics the human brain in processing data. It creates patterns in objects, images, speech, etc. so as to assist in decision making. It can function without human intervention, and can work with large volumes of structured and unstructured data.

Deep Learning is a subfield of Machine Learning, or rather an evolved form of ML. Inspired by ANN, Deep Learning algorithms can identify patterns, classify them, and draw insights from it, just as human brains can. In ML systems, while learning comes through parsing and data-based learning, Deep Learning models function and make decisions much like the human brain.

We believe that technology is for everyone. Our goal in offering Deep Learning consultancy services to businesses is to aid them in accessing the power of deep tech to scale. In our experience, most businesses fail to adopt AI solutions because of lack of adequate knowledge. AI is a vast field, and each tech has its unique offering. While Deep Learning in AI is changing dynamics across business processes and verticals, it is not always the solution to all business problems. When we collaborate, we help you to understand how deep learning works, how it helps business, and why it is (or not) the answer to your business challenge. Your business has a unique challenge and a custom solution is the only answer.

Sentiment analysis, text analysis, e-discovery, fraud detection, network security, portfolio analysis, product recommendation, customer support – it is the next level of service with Deep Learning systems.

Security, malware detection, intrusion detection, spam identification, network traffic anomaly analysis, etc. The scope for Deep Learning applications is vast and expanding.

Smart manufacturing in factories is looking at Deep Learning solutions development for quality control, predictive maintenance, process monitoring, prototyping, premise security, and more.

Use of distinct Deep Learning models like reinforcement learning. Used in 3D modeling, building cooling load prediction, rut prediction, safety guardrail detection, occupancy modeling, and more.