**How To Choose An NLP Vendor For Your Organization**

Companies using Natural Language Processing are already seeing the business impact from improved customer experience to business growth. An organization that commits to NLP can enjoy the benefits of a shared understanding of data and goals, improved decision-making, fact-based analysis that avoids guesswork and allows for refined planning and forecasting at every level of the organization.

1. What is my business use-case?

2. How accurate is the NLP solution?

3. Can the solution be customized according to my needs?

4. What amount of training data would be required?

5. Does the model improve on continuous usage?

6. Is the NLP solution affordable at scale?

7. Do they provide an on-premise solution?

8. What are its integration capabilities?

1. What is my business use-case?

2. How accurate is the NLP solution?

3. Can the solution be customized according to my needs?

4. What amount of training data would be required?

5. Does the model improve on continuous usage?

6. Is the NLP solution affordable at scale?

7. Do they provide an on-premise solution?

8. What are its integration capabilities?

**Wikum – Summarize large discussion threads.**

By harnessing the power of people backed by state-of-the-art human-computer interaction and machine learning techniques, users get complete control over the collaborative summarization process.

Integration is the process of evaluating integrals. It is one of the two central ideas of calculus and is the inverse of the other central idea of calculus, differentiation. Generally, we can speak of integration in two different contexts: the indefinite integral, which is the anti-derivative of a given function; and the definite integral, which we use to calculate the area under a curve.

**When Machine Learning Prediction Excels**

In the previous post, Prediction Models: Traditional versus Machine Learning, we looked at 3 kinds of prediction models and clarified the difference between traditional and machine learning models for prediction. In this post we’ll see that machine learning prediction models excel in conditions in which other prediction models suffer.

**Optimal Transport on Large Networks**

This article presents a set of tools for the modeling of a spatial allocation problem in a large geographic market and gives examples of applications. In our settings, the market is described by a network that maps the cost of travel between each pair of adjacent locations. Two types of agents are located at the nodes of this network. The buyers choose the most competitive sellers depending on their prices and the cost to reach them. Their utility is assumed additive in both these quantities. Each seller, taking as given other sellers prices, sets her own price to have a demand equal to the one we observed. We give a linear programming formulation for the equilibrium conditions. After formally introducing our model we apply it on two examples: prices offered by petrol stations and quality of services provided by maternity wards (only the later is described here for privacy issues). These examples illustrate the applicability of our model to aggregate demand, rank prices and estimate cost structure over the network. We insist on the possibility of applications to large scale data sets using modern linear programming solvers such as Gurobi.

**Attention Is All You Need – Transformer**

Recurrent Neural Networks(RNNs), Long Short-Term Memory(LSTM) and Gated Recurrent Units(GRU) in particular, have been firmly established as state-of-the-art approaches in sequence modeling and transduction problems. Such models typically rely on hidden states to maintain historical information. They are beneficial in that they allow the model to make predictions based on useful historical information distilled in the hidden state. On the other hand, this inherently sequential nature precludes parallelization, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Furthermore, in these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, which makes it more difficult to learn dependencies between distant positions. In this article, we will discuss a model named Transformer, proposed by Vaswani et al. at NIPS 2017, which utilizes self-attention to compute representations of its input and output without using sequence-aligned RNNs. In this way, it reduces the number of operations required to relate signals from two arbitrary positions to a const

**Comparing Neural Network Architectures**

After reading François Chollet’s wonderful book Deep Learning with Python I became curious about the different neural network architectures and which one is the best for various tasks. I had already thought of getting some practice with word data so I decided to take on the project of classifying the language that a word is written in. The first direction that I think of when working with words is to use recurrent neural networks. Chollet’s book also suggests that a series of 1D convolutions could be appropriate. Finally, I found a nice blog post that achieved a similar task using a simple fully connected network. This was the best opportunity to try each of these nice networks on the same project! I collected the data myself and I used Keras to do machine learning. The full Python code can be found on my Github.

**An Introduction to Tensors for Deep Learning**

Tensors are the primary data structure used in deep learning, inputs, outputs everything within a neural network is represented using Tensors.

Beyond calculating lottery probabilities or disease likelihoods there are also other applications for Bayes theorem, for example we could build a ranking system. Let’s take a movie ranking website where users vote up/down on movies. Simple ranking schemes like percentage of positive votes or up minus down votes perform poorly.

**Predicting Diabetes using Logistic Regression with TensorFlow.js**

Build a Logistic Regression model in TensorFlow.js using the high-level layers API, and predict whether or not a patient has Diabetes. Learn how to visualize the data, create a Dataset, train and evaluate multiple models.

**Google football environment?—?installation and Training RL agent using A3C**

Google brain team released an open source football environment for the purpose of reinforcement learning research some days back. They have provided the code base(GitHub) and also their research paper on it.

I’ve analyzed 7,000 ‘AI Startups’. Most underestimate the challenges that plague AI. Does yours?

Downloadable Excel File

**Semantic Segmentation: The easiest possible implementation in code!**

Segmentation is crucial for image interpretation tasks. Don’t stay behind on the trend then. Let’s implement it and in no time you’ll be a pro!

**Exploratory Data Analysis with Python: Medical Appointments Data**

Exploratory data analysis’ is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as those we believe to be there.’

Measuring Machine Learning