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From this post you will come to know how particular predictions are being made and how models focus on various aspects of parameters it has learned.
Here we will discuss how Convolutional Neural Networks and Autoencoders are used to denoise an image.
From this article you can learn how to plot Geospatial data.
This post explains most underlined
Plateau phenomenon and it’s remedies
which is related to optimization of ML model.
This blog discuss about how to use SOTA BERT for pre-processing the textual data
Neural network pruning, which comprises methodically eliminating parameters from an existing network, is a popular approach for minimizing the resource requirements at test time.
This post discuss about how identify drift in dataset, how it evolve, it’s various types, and how we can address it.
Statistics is a branch of mathematics that deals with quantified models and representations to analyze and perform experiments on real-world data.
With the fast development in the machine learning frameworks, comparative approaches are being created within the capacity of ML engineering, which handles the special complexity of the practical application of machine learning.
Machine learning and deep learning models are normally used to solve regression and classification problems. In a supervised learning problem, during the training process, the model learns how to map the input to the realistic probability output.
Relational data represent relationships between entities anywhere on the web (e.g. online social networks) or in the physical world (e.g. structure of the protein).
Dimensionality reduction is the transformation of data from high dimensional space into a low dimensional space so that low dimensional space representation retains nearly all the information ideally saying all the information only by reducing the width of the data.
KNIME stands for Konstanz Information Miner which was developed at the Konstanz University, Germany in 2004. It is open-source software written in Java. KNIME relies on predefined components called Nodes for building and executing the workflow.
The first-hand data is usually messy and comes from different sources and distributions. To feed them into the machine learning model they need to be standardized and cleaned up.
On the other hand, online learning is a combination of different techniques of ML where data arrives in sequential order and the learner (algorithm/model) aims to learn and update the best predictor for future data at every step.
In our daily routine, we unknowingly perfectly transfer the knowledge of some activity or task to the related one. Whenever we come across a new problem statement or task, first we recognize it and try to apply the relevant experience which results in hassle-free completion of the task.
In the era of Data Science where knowledge of programming languages like Python and R is essential to implement the fundamental algorithms and techniques related to Machine learning and Data analytics.
Long Short Term Memory in short LSTM is a special kind of RNN capable of learning long term sequences. They were introduced by Schmidhuber and Hochreiter in 1997. It is explicitly designed to avoid long term dependency problems. Remembering the long sequences for a long period of time is its way of working.
To fill the gap between Source data (train data) and Target data (Test data) a concept called domain adaptation is used. It is the ability to apply an algorithm that is trained on one or more source domains to a different target domain.
Background subtraction is a widely used approach to detect moving objects in a sequence of frames from static cameras. The base in this approach is that of detecting moving objects from the difference between the current frame and reference frame, which is often called ‘Background Image’ or ‘Background Model’.
The Principal Component Analysis is a popular unsupervised learning algorithm that is widely known for dimensionality reduction. It increases the interpretability and also reduces the loss of information while reducing the dimensionality.
Before 2015 when the first attention model was proposed, machine translation was based on the simple encoder-decoder model, a stack of RNN and LSTM layers. The encoder is used to process the entire sequence of input data into a context vector. This is expected to be a good summary of input data. The final stage of the encoder is the initial stage of the decoder.
Monte Carlo simulation, also known as the Monte Carlo method or multiple probability simulations, is a mathematical tool used to estimate the possible outcomes of uncertainty in an event.
Innovation of deep neural networks has given rise to many AI-based applications and overcome the difficulties faced by computer vision-based applications such image classification, object detections etc. and frameworks like Tensorflow, PyTorch, Theano, Keras, MxNet has made these task simpler than ever before.
Creating representations of words is to capture their meaning, semantic relationship, and context of different words; here, different word embedding techniques play a role. A word embedding is an approach used to provide dense vector representation of words that capture some context words about their own.
Sentiment analysis is a part of natural language processing used to determine whether the sentiment of the data under observation is positive, negative or neutral. Usually, sentiment analysis is carried on text data to help professionals monitor and understand their brand and product sentiment across the industry and customers by taking the feedback.
In any data science project life cycle, cleaning and preprocessing data is the most important performance aspect. Say if you are dealing with unstructured text data, which is complex among all the data, and you carried the same for modeling two things will happen. Either you come up with a big error, or your model will not perform as you expected.
Many algorithms belong to the family of tree and ensemble, which are hard for computational problems and exposed to many hyperparameters that can be modified to improve the performance. However, manually exploring those parameters and setting those for optimized solutions is a rigorous task and often leads to unsatisfactory results.
Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm. It has quite effective implementations such as XGBoost as many optimization techniques are adopted from this algorithm. However, the efficiency and scalability are still unsatisfactory when there are more features in the data.
Initially developed by Intel, OpenCV is an open-source computer vision cross-platform library for real-time image processing and which has become a standard tool for all things related to computer vision applications. In 2000, the first version of OpenCV was released; since then, its functionality has been very much enriched and simplified by the scientific community. Later in 2012, a nonprofit foundation OpenCV.org took the initiative for maintaining a support site for developers and users.
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