6th World Machine Learning and Deep Learning Congress

Unity is the strength. Wonderful things can be achieved when there will be teamwork and collaboration. And ultimately it will lead always to victory. Successful brand collaborates with different brands and its collaboration depends on both brands being able to benefit from the existing market of each other, or from the gaps in the market that can only be filled, through a collaborative relationship that competitors will find hard to replicate. Brand partnerships create a lot of noise with consumers, especially if two colossal brands decide to become BFFs. Often, people want to know what brought these two brands together and what they concocted for their fans. Not only this creates a social media buzz but it also sends traffic to the websites and creates news coverage & enhances the visibility of the other products. In this blog, we will discuss the brands who helped Machine Learning Congress 2018 so far to make the conference a big hit.     ...

Artificial Intelligence (AI) incorporates everything from machine learning to the Internet of Things (IoT). Earlier the tasks like visual and speech processing, decision-making and language translation etc., that required human intelligence, can be performed by the present AI frameworks. This clearly offers opportunities in many areas within insurance. It is not only limited to this, Artificial Intelligence is rapidly advancing and discovering its own way into astonishing daily use cases, such as self-driving cars to automated assistants, leading individuals to underestimate how it’s fundamentally transforming their world’s reality. It is disrupting and improving organizations across all industries, and now it’s headed for the insurance sector. 1.       Automation: AI and automation allow insurers to cut down on validating and reconciling data received from brokers, for example accounting information. Tasks that once took weeks to finish (checking...

Introduction: In recent years Neural computing progressing at an exponential pace and advances in Deep learning and Artificial neural nets are rapidly replacing traditional computing framework. Neural computing is going to be next level paradigm shift in computer science. As new neuromorphic hardware and innovation in neural software techniques become available, it’s going to be a fundamental platform for future computing infrastructure.  Artificial neural nets are networks of neurons that learn, adapt, predict, understand and extract patterns from raw data just like biological neurons in the human brain. Deep artificial neural nets can learn complex functions and extract multidimensional complex features from raw data. Technology used: Artificial neural nets and techniques of Deep learning to train deep networks are becoming dominant in specialized tasks like natural language processing, image processing, language translation and financial predictions and man...