AI & Machine Learning

Artificial Intelligence: The concept of Artificial Intelligence (AI) or Machine Intelligence is no more an unknown or unexplored concept to us and all the thanks go to Siri, Alexa, Cortana, and Google Assistant for being live examples. Even though these assistants evolved recently, but AI was there for a quite some time. Right from filtering our

Machine learning can help brands develop more personalised conversations with their customers, says Mariángeles Noseda,from intive-FDV It is more important than ever for brands to keep up a steady conversation with their customers. Those who become complacent with client communication could soon find foot-loose customers wandering in the direction of their competitors. As they say,

The field of AI is broad and has been around for a long time. Deep learning is a subset of the field of machine learning, which is a subfield of AI. The facets that differentiate deep learning networks in general from “canonical” feed-forward multilayer networks. Deep learning has been a challenge to define for many because it has changed forms slowly over the past decade. Here are the 10 powerful deep learning methods AI engineers can apply to their machine learning problems. 

The ability to meet quotas and achieve goals is a huge part of being in a successful sales team. For sales managers, whose jobs include coming up with those quotas and benchmarks, the process of drawing up realistic and attainable goals amid a backdrop of intensifying market and competitive pressure can be anything but straightforward.

Designing a machine learning model is a tricky task. A model may not work in practice although it has high performance on the training data. This article discusses the misuse of a machine learning model that causes the predictions not to work in the real world situation. The other reasons could be overfitting, duplicated samples, and unbiased data. It is always good to use your domain knowledge or talk to some experts and see if your prediction/recommendation results make sense or not.

For humans, forgetting is more than just a failure to remember; it’s an active process that helps the brain take in new information and make decisions more effectively. It’s possible that our brains and distinctly human processes, like forgetting, hold the map to creating strong artificial intelligence, but scientists are collectively still figuring out how to read the directions. Now, data scientists are applying neuroscience principles to improve machine learning, convinced that human brains may hold the key to unlocking Turing complete artificial intelligence.

Keras is one of the most popular Deep Learning libraries out there at the moment and made a big contribution to the commoditization of artificial intelligence. It is simple to use and it enables you to build powerful Neural Networks in just a few lines of code. In this post, you will discover how you can build a Neural Network with Keras that predicts the sentiment of user reviews by categorizing them into two categories: positive or negative. 

In recent years, deep learning has revolutionized computer vision. And thanks to transfer learning and amazing learning resources, anyone can start getting state of the art results within days and even hours, by using a pre-trained model and adapting it to your domain. As deep learning is becoming commoditized, what is needed is its creative application to different domains. Today, deep learning in computer vision has largely solved visual object classification, object detection, and recognition. In these areas, deep neural networks outperform human performance.

While Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) are becoming more important for businesses due to their applications in Computer Vision (CV) and Natural Language Processing (NLP), Reinforcement Learning (RL) as a framework for computational neuroscience to model decision making process seems to be undervalued. Besides, there seems to be very little resources detailing how RL is applied in different industries. Despite the criticisms about RL’s weaknesses, RL should never be neglected in the space of corporate research given its huge potentials in assisting decision making. 

Repeatable finance process such as routine transactions recording and reconciliations are ripe for automation. Finance functions must, therefore, embrace the disruptors of today to transform their own operating models and unlock an environment of extreme automation. Technologies are “extremely automating” finance operations as we know them and slowly but surely developing intelligent finance functions that are viewed as strategic advisors to the business. There are at least seven technologies that will deliver extreme finance automation.

A data set is called imbalanced if it contains many more samples from one class than from the rest of the classes. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while other classes make up the majority. In this scenario,