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Convolutional Neural Network (CNN)
In today’s instant image sharing era, it’s essential to get the tech ready to talk the language of images. While it is comfortable for our brains to interpret what an image represents and refers to, it is a complex challenge to get a computer to do the same. Computers interpret the…
Principal Component Analysis vs Linear Discriminant Analysis
In machine learning, reducing dimensionality is a critical approach. Overfitting of the learning model may result in a large number of features available in the dataset. There are two standard dimensionality reduction techniques used by machine learning experts to evaluate the collection of…
K-Means Clustering vs Hierarchical Clustering
A form of exploratory data analysis in which observations are divided into different groups with standard features is known as clustering analysis. The purpose of classification or cluster analysis is to ensure that different groups must have different observations as possible. The two main types…
Decision Tree vs. Random Forest for Classification Problems
Decision trees are part of the Supervised Classification Algorithm family. On classification issues, they work very well, the decisional route is reasonably easy to understand, and the algorithm is fast and straightforward. Random Forest is the ensemble variant of Decision Trees. Random forest is a…
What is Dimensionality Reduction in Machine Learning?
Machine learning is nothing but a research field that allows computers without any specific programming to “learn” like humans. High-dimensionality statistics and dimensionality reduction techniques that have been used for data visualization. These techniques are applied in machine…
How is Naive Bayes used in Machine Learning?
Naïve Bayes is a surprisingly powerful yet simple algorithm for predictive modeling. It is a machine learning model that can handle large volumes of data, including millions of data records. Naïve Bayes is the recommended approach for Natural Language Processing tasks such as sentiment analysis as…
Logistic Regression vs K-Nearest Neighbours vs Support Vector Machine
Machine learning is a scientific technique where computers learn how to solve a problem without directly programming them. The ML race is currently led by deep learning fuelled by improved algorithms, computing power and big data. The classical algorithms of ML still have a firm place in the field.…
What is a tensor and how is it used in machine learning?
In machine learning, there is much discussion around tensors being the cornerstone data structure. Tensor is a type of data structure used in linear algebra that can be used for arithmetic operations like matrices and vectors. In 2015, researchers at Google came up with TensorFlow, which is now…
Which NVIDIA Graphics Card is ideal for training neural networks?
If a CPU is a PC’s core, then the soul is a GPU. While most PCs can run without a good GPU, without one, neural networking is not possible. Machine learning experts suggest that complex operations like matrix manipulation, exceptional computational prerequisites, and significant computing…
ARM vs x86 Architecture: Key Differences
Three different types of the processor can run on Android: Intel, MIPS, and Arm. While the MIPS processors for phones haven’t been seen for years, Arm is the newest CPU architecture used by most modern smartphones in both Apple and Android devices. While Intel has been in the picture for a long,…
Why is Apple switching to ARM based macs?
When Apple moves to ARM processors, there are clear hardware advantages, which means that the switch would benefit both the business and consumers. Apple has some significant hardware advantages by replacing Intel processors with ARM-based ones. And it also provides opportunities to replace Intel…
What is Bitswap?
Bitswap protocol version 1.2.0 has recently landed in js-IPFS and brings a host of performance improvements around coordinating the supply of blocks across the network to peers. Machine learning experts suggest that BitSwap will expand shortly, in terms of its applications and presence. With…