December 7, 2019: OceanPro, the start-up accelerator of Maersk for shipping the logistics industry, announced the graduation of its second cohort of Indian start-ups showcasing the artificial intelligence (AI) and machine learning (ML) led solutions developed over the last 4 months.
Stratforge, Tripz, Linkstreet and Shipmnts.com are the start-ups that showcased solutions focused on simplifying the customer payment process, optimizing the truck/ container moves, transforming the enterprise learning to make it more customized, effective and engaging, and digitising the document assessment process.
And we still have miles to go before Alex becomes the true logistician to #automate #global #SupplyChain functions for all stakeholders involved.
#ArtificialIntelligence #MachineLearning #DeepLearning #NLP @karan_n_shah pic.twitter.com/HSiucNXBsf
OceanPro launched its first set of 7 start-ups last year.
“One of the solutions developed from the initial cohorts is now being used in inland tracking by 3 countries and is scheduled for a wider global roll out.
SEO is getting influenced by Artificial Intelligence and Machine Learning.
Read the ways in which AI is affecting SEO and can SEO be replaced with AI?
Artificial intelligence and machine learning are the a part of computing that are correlated with one another.
These two technologies are the foremost trending technologies which are used for creating intelligent systems.
Even in the tech field in general, everyone talks about artificial intelligence vs machine learning and many are eager to learn about them.Visit for more details: https://pradtutorials.com/artificial-intelligence-vs-machine-learning/
IntroductionPrepared learning machine best machine learning course online models do not always reflect the real desire for the work being done.
Interpreting for models allows us to evaluate their decisions and obtain information that the purpose alone cannot.Interpretation takes many forms and can be difficult to explain; we first examine the general frameworks and sets of definitions in which the interpretation of a model can be analyzed and compared.Next, we analyze a few well-known examples of interpretation – LIME (Ribeiro et al.
We conclude by making some general statements about the sector by looking at these examples.
Desired features in certain situations can often be a domain- or a problem, and although many papers seek models that translate into different domains or applications, the machine learning models community does not have a cohesive framework to discuss what structures make a model definition and what we do.Hopefully, do with descriptive results.
In many cases, the intended function cannot accurately capture the real-world costs of model decisions.The costs associated with ethics or fairness are difficult to determine in the intended work, and researchers may be unaware or unable to anticipate these costs in advance.Interpretation is required if model metrics are inadequate.
Interpreting allows us to understand exactly what a learning model is, what other information a model can provide, and the reasons behind its decisions, and to consider all of this in the context of the real-world problem we are trying to solve.
