Tag investment

AI technology for a better tomorrow

by WebAdmin NLP, Technology

Getting started with AI? Perhaps you’ve already got your feet wet in the world of Machine Learning, but still looking to expand your knowledge and cover the subjects you’ve heard of but didn’t quite have time to cover?

1. NLP – Natural Language Processing

Natural Language Processing (NLP) is a common notion for a variety of Machine Learning methods that make it possible for the computer to understand and perform operations using human (i.e. natural) language as it is spoken or written.

The most important use cases of Natural Language Processing are:

He goal of this task is to predict a class (label) of a document, or rank documents within in a list based on their relevance. It could be used in spam filtering (predicting whether an e-mail is spam or not) or content classification (selecting articles from the web about what is happening to your competitors).

2. Reinforcement learning

Reinforcement Learning differs in its approach from the approaches we’ve described earlier. In RL the algorithm plays a “game”, in which it aims to maximize the reward. The algorithm tries different approaches “moves” using trial-and-error and sees which one boost the most profit.

3. Dataset

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely -- it's almost like magic!

Aron Larsson

– CEO, Strategy Director

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AI simplified: What computers are good at

by WebAdmin Technology

Getting started with AI? Perhaps you’ve already got your feet wet in the world of Machine Learning, but still looking to expand your knowledge and cover the subjects you’ve heard of but didn’t quite have time to cover?

1. Investment banks can use AI in six critical ways

Natural Language Processing (NLP) is a common notion for a variety of Machine Learning methods that make it possible for the computer to understand and perform operations using human (i.e. natural) language as it is spoken or written.

The most important use cases of Natural Language Processing are:

Sentiment analysis aims to determine the attitude or emotional reaction of a person with respect to some topic – e.g. positive or negative attitude, anger, sarcasm. It is broadly used in customer satisfaction studies (e.g. analyzing product reviews).

2. Reinforcement learning

Reinforcement Learning differs in its approach from the approaches we’ve described earlier. In RL the algorithm plays a “game”, in which it aims to maximize the reward. The algorithm tries different approaches “moves” using trial-and-error and sees which one boost the most profit.

3. Dataset

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

It’s not by any means exhaustive, but a good, light read prep before a meeting with an AI director or vendor – or a quick revisit before a job interview!

Aron Larsson

– CEO, Strategy Director

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Deep Learning Chatbot – analysis and implementation

by WebAdmin Natural Language Processing

If you have a business with a heavy customer service demand, and you want to make your process more efficient, it’s time to think about introducing chatbots. In this blog post, we’ll cover some standard methods for implementing chatbots that can be used by any B2C business.

1. Chatbots

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The most important use cases of Natural Language Processing are:

Document Summarization is a set of methods for creating short, meaningful descriptions of long texts (i.e. documents, research papers).

2. Deep learning

At this point, your data is prepared and you have chosen the right kind of chatbot for your needs. You will have a sufficient corpora of text on which your machine can learn, and you are ready to begin the process of teaching your bot. In the case of a retrieval model bot, the teaching process consists of taking in an input a context (a conversation with a client with all prior sentences) and outputting a potential answer based on what it read. Google Assistant is using retrieval-based model (Smart Reply – Google). Which can help give you an idea of what it looks like.

3. Conclusion

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

It’s not by any means exhaustive, but a good, light read prep before a meeting with an AI director or vendor – or a quick revisit before a job interview!

Aron Larsson

– CEO, Strategy Director

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.

How companies are making money by recommend system

by WebAdmin Business

Simply put, a recommender system is an AI algorithm (usually Machine Learning) that utilizes Big Data to suggest additional products to consumers based on a variety of reasons. These recommendations can be based on items such as past purchases, demographic info, or their search history.

1. There are many types of recommender systems available

Choosing the right type of recommender system is as important as choosing to utilize one in the first place. Here is a quick overview of the options available to you.

The most important use cases of Natural Language Processing are:

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2. Reinforcement learning

Reinforcement Learning differs in its approach from the approaches we’ve described earlier. In RL the algorithm plays a “game”, in which it aims to maximize the reward. The algorithm tries different approaches “moves” using trial-and-error and sees which one boost the most profit.

3. Dataset

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

It’s not by any means exhaustive, but a good, light read prep before a meeting with an AI director or vendor – or a quick revisit before a job interview!

Aron Larsson

– CEO, Strategy Director

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.

Machine Learning Terms every manager should know

by WebAdmin Technology

Getting started with AI? Perhaps you’ve already got your feet wet in the world of Machine Learning, but still looking to expand your knowledge and cover the subjects you’ve heard of but didn’t quite have time to cover?

1. NLP – Natural Language Processing

Natural Language Processing (NLP) is a common notion for a variety of Machine Learning methods that make it possible for the computer to understand and perform operations using human (i.e. natural) language as it is spoken or written.

The most important use cases of Natural Language Processing are:

Document Summarization is a set of methods for creating short, meaningful descriptions of long texts (i.e. documents, research papers).

2. Reinforcement learning

Reinforcement Learning differs in its approach from the approaches we’ve described earlier. In RL the algorithm plays a “game”, in which it aims to maximize the reward. The algorithm tries different approaches “moves” using trial-and-error and sees which one boost the most profit.

3. Dataset

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

It’s not by any means exhaustive, but a good, light read prep before a meeting with an AI director or vendor – or a quick revisit before a job interview!

Aron Larsson

– CEO, Strategy Director

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.