Artificial intelligence

TensorFlow Quantum: beauty and the beast

Reading Time: 4 minutes So, we are finally here, after a long wait, we are going to be in an era of quantum computing. TFQ, the beauty of TensorFlow and beast nature of quantum computing. Quantum computing is becoming a technology to observe more closely in 2020. We have seen some recent announcements from Honeywell, Google and others, it’s worth looking forward to new pieces of hardware coming this year. Now, Google has Continue Reading

MachineX: Anticipate Customer behavior for Retailing

Reading Time: 4 minutes In this blog, we are going to see the power of Customer behavior Anticipation and how it can derive the success of the retail sector. Nowadays, Machine learning is playing an important in the success of different sectors. we can talk about Healthcare, Finance, Manufacturing, Agriculture, now even in Education. Retail is one of the sectors, which is getting huge benefits from machine learning and Continue Reading

MachineX: The Power of Recommendation Engines

Reading Time: 4 minutes In this blog, we are going to talk about, what actually Recommendation Engines is and different types of same. You can see the full webinar, related to this blog here : Recommender Engines or Systems is one of the most mainstream utilization of data science today. They are utilized to predict the “rating” or “preference” that a user would provide for a thing. Pretty much Continue Reading

Boosting medical diagnosis with Klickare

Reading Time: 4 minutes In this blog, we are going to see how KlicKare can boost up medical diagnosis by using deep learning. Medical diagnostics are a category of medical tests designed to detect infections, conditions, and diseases. These medical diagnostics fall under the category of in-vitro medical diagnostics (IVD) which be purchased by consumers or used in laboratory settings. Biological samples are isolated from the human body such Continue Reading

MachineX: Alphabets of PyTorch (Part 1)

Reading Time: 6 minutes Overview In this blog, you’ll get an introduction to deep learning using the PyTorch framework, we will see some basics of PyTorch. Introduction to PyTorch PyTorch is a Python machine learning package based on Torch, which is an open-source machine learning package based on the programming language Lua. Two main features: Tensor computation (like NumPy) with strong GPU acceleration Automatic differentiation for building and training Continue Reading

Knoldus-Clutch-AI-Big-Data-Top

Knoldus Joins Clutch’s Research of Top AI & Big Data Companies in 2018

Reading Time: 2 minutes The advent of the digital economy is a development that has changed the landscapes of every industry across the world. There is a new key ingredient for success; the best performing businesses are those with the best digital platforms, built to drive performance and bring customer interaction to new heights. At Knoldus, we are a team of developers and innovators dedicated to helping businesses reach Continue Reading

MachineX: Logistic Regression with KSAI

Reading Time: 2 minutes Logistic Regression, a predictive analysis, is mostly used with binary variables for classification and can be extended to use with multiple classes as results also. We have already studied the algorithm in deep with this blog. Today we will be using KSAI library to build our logistic regression model. Setup

MachineX: An Introduction to KSAI, a machine learning library

Reading Time: 3 minutes Take a closer look at Linkedin or any media platform for a couple of minutes, you’ll find that the hot topic in the technology section nowadays is Machine Learning and Artificial Intelligence. Why Machine learning and artificial intelligence? Well needless to say it is transforming the world like anything. People are doing good in business by predicting different aspects, doctors are doing good in medical Continue Reading

KnolX: NAIVE BAYES CLASSIFIER

Reading Time: < 1 minute Hi all, Knoldus has organized a 30 min session on 27th April 2018 at 4:00 PM. The topic was NAIVE BAYES CLASSIFIER. Many people have joined and enjoyed the session. I am going to share the slides here. Please let me know if you have any question related to linked slides.

KnolX: Machine Learning with Artificial Neural Networks

Reading Time: < 1 minute Hi all, Knoldus has organized a 30 min session on 8th December 2017 at 4:15 PM. The topic was Machine Learning with Artificial Neural Networks. Many people have joined and enjoyed the session. I am going to share the slides here. Please let me know if you have any question related to linked slides.   Machine Learning with Artificial Neural Networks from Knoldus Inc. Here’s the video of the Continue Reading

MachineX: Total Support Tree for Association Rule Generation

Reading Time: 5 minutes In our previous blogs on Association Rule Learning, we have seen the FP-Tree and the FP-Growth algorithm. We also generated the frequent itemsets using FP-Growth. But a problem arises when we try to mine the association rules out of these frequent itemsets. Generally, the number of frequent itemsets is massive and to run an algorithm on them becomes very memory inefficient. So, to store these Continue Reading

MachineX: Frequent Itemset generation with the FP-Growth algorithm

Reading Time: 4 minutes In our previous blog, MachineX: Understanding FP-Tree construction, we discussed the FP-Tree and its construction. In this blog, we will be discussing the FP-Growth algorithm, which uses FP-Tree to extract frequent itemsets in the given dataset. FP-growth is an algorithm that generates frequent itemsets from an FP-tree by exploring the tree in a bottom-up fashion. We will be picking up the example we used in Continue Reading

MachineX: Understanding FP-Tree construction

Reading Time: 5 minutes In my previous blog, MachineX: Why no one uses apriori algorithm for association rule learning?, we discussed one of the first algorithms in association rule learning, apriori algorithm. Although even after being so simple and clear, it has some weaknesses as discussed in the above-mentioned blog. A significant improvement over the apriori algorithm is FP-Growth algorithm. To understand how FP-Growth algorithm helps in finding frequent Continue Reading