deep learning models are considered black-box models. It is not easy to understand how a defined model is functioning with the data. visualizing the deep learning models can help in improve interpretability.
A Hoeffding tree is an incremental decision tree that is capable of learning from the data streams. The basic assumption about the data is that data is not changing over time helps in building a Hoeffding tree
As we have been using graphs to represent semantic relationships between different entities through nodes and arcs, similarly, the knowledge graph is used to model
There are a number of studies in which we can see that neural networks are generalized and can be applied to the regular grid structure which is helpful in working on arbitrarily structured graphs
The information gained in the decision tree can be defined as the amount of information improved in the nodes before splitting them for making further decisions.
In swarm learning, a global system is divided into agents with their environment. And interaction behaviour of the agents leads to the global solution behaviour. In terms of neural networks,
In the last few years, we have seen that self-supervised learning methods are emerging rapidly. It can also be noticed that models using self-supervised learning
We can simply apply the dimension reduction by choosing the random projection of the data. Locally-Linear Embedding is a approach for dimension reduction
Pix2seq is a new approach which is designed in intuition which states that if a neural network is already trained about the where and what the objects are. We just need to train the network to read them out
When a time series data gets collected, there is other additional information that also gets collected along with it. This important information embedded in the time-series data must be described as its characteristics.
In data analytics and machine learning, when we apply the behavioural science insights in the studies, it always helps in improving the experience in delivering
Bayesian statistics is one of the most popular concepts in statistics that are widely used in machine learning as well. Many of the predictive modelling
A hybrid recommendation system is a special type of recommendation system which can be considered as the combination of the content and collaborative filtering method.
word embedding term is used for the representation of words for text analysis. There are different models used for word embedding tasks.
A Hidden Markov Model (HMM) is a statistical model which is also used in machine learning. It can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable.
Machine learning algorithms have amazing capabilities of learning. These capabilities can be applied in the blockchain to make the chain smarter than before
Few-shot learning can also be called One-Shot learning or Low-shot learning is a topic of machine learning subjects where we learn to train the dataset with lower or limited information.
Survival analysis is done for answering the questions like what portion of the population will survive in a certain time, the portion which is surviving at what rate they will fail
Dynamic Neural networks can be considered as the improvement of the static neural networks by adding more decision algorithms we can make neural networks learning dynamically for the input.
Vertex AI is an API developed by Google research that consists of AutoML and AI Platform in one place. As we know the AutoML that allows us to train models on different kinds of data
A stochastic process can be considered as the Markov chain if the process consists of the Markovian properties which are to process the future.
In scalable machine learning, we try to build a system where the components of the system have their own work or task which helps the whole system to lead towards the solution of the problem rapidly
In most classification problems, the models classify the data into multiple classes. A one-class classification is an approach where the specific data elements in the
One of the most useful popular libraries of the python programming language used for machine learning is scikit-learn. The reason behind this popularity is that
Weak supervision is a part of machine learning where unorganized or imprecise data are used to provide indications to label a large amount of unsupervised data so that data can be used in machine learning
The data which is generated continuously in an incremental manner from different sources can be considered as the streaming data.
when the elements of the gradient become exponentially small so that the update of the parameters with the gradient becomes almost insignificant
Time series modelling needs a series of steps to be performed such as processing the time series data, analyzing the data before modelling with different
Metadata is something that provides information about the data or we can say it is data about data. This is the data that consists of information about the data or the data that describes the data on which we are working.
it is required to understand the intuition of words in different positions and hold the similarity between the words as well. WordNET is a lexical database of semantic relations between words in more than 200 languages.
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