Deploying machine learning models on mobile devices is the new phase of ML that’s about to begin. Vision models, mostly object detection models, have already made their way to mobile devices along with speech recognition, image classification, text completion etc. …
For most of us, who were once newbies in Deep Learning, trying tf.keras.layers.Conv2D for MNIST classification was fun. Convolutions are the building blocks of most algorithms in computer vision, except for some newer variants like Vision Transformers, Mixers, etc. which claim to solve image-related problems without the use of convolutions…
Model Interpretability is one of the booming topics in ML because of its importance in understanding blackbox-ed Neural Networks and ML systems in general. They help identify potential biases in ML systems, which can lead to failures or unsatisfactory user experiences.
You’ll see these headlines every now and then, as…
Probability distributions are one of the most important concepts in the Probability Theory and are found all across Mathematics, Physics and other natural sciences. In Physics, you might have studied the Maxwell-Boltzmann distribution which describes the speed of particles ( atoms ) of an ideal gas kept in the closed…
StackOverflow
The name ( or deity 🙏 ) which makes every developer smile, by providing quality answers to their questions regarding bugs, APIs etc. …
Image Segmentation is one of most useful feature that ML has provided us. Have you ever wondered how the ‘blur background’ feature works in Zoom, Google Meet or Microsoft Teams? How can a machine realize the difference between a person and the background without any depth perception?
Linear Independence is an important concept in linear algebra and can be discovered in any high-school “Coordinate Geometry” textbook. In order to grasp some other concepts like *span, dimension and basis vectors, a concrete understanding of linear independence is essential.
Instead of going through the textbook-ish way to introduce the…
The Moore-Penrose Pseudoinverse or Moore-Penrose Generalized Inverse or simply Pseudoinverse is a special matrix used to solve non-consistent linear systems of equations. It provides a least-squares solution to the problem
Considering matrices as compact representations of a linear transformation, inverse matrices, in a similar manner, represent a linear transformation which…
👉🏽 Machine Learning ( and AI as a whole ) is a math-heavy field. It highly relies on concepts of Linear Algebra, Probability, Calculus and Statistics which might be difficult sometimes, to some developers. …
Earlier this May, a group of researchers from Google released a paper “MLP-Mixer: An all-MLP Architecture for Vision” introducing their MLP-Mixer ( Mixer, for short ) model for solving computer vision problems. The research suggests that MLP-Mixer attains competitive scores on image classification benchmarks such as the ImageNet.
