Machine Learning (ML)

Introduction to Data Science

In this article, we will get to know what data science is, why is it becoming more sought after and also about its importance.

TensorFlow

Visualizing Neural Network Models in TensorFlow

In this article, we have explored the approach to visualize Neural Network Models in TensorFlow. We have explored how to use TensorBoard.

TensorFlow

New features in TensorFlow v2.8

TensorFlow 2.8 has been finally released. Let us take a look at some of the new features and improvements being rolled out in this version. This new version comes with lots of additions, bug fixes and changes.

Machine Learning (ML)

Volumetric Image Segmentation

In this article, we have explored the Machine Learning application: Volumetric Image Segmentation in depth and covered the different ML models used for it like 3D U-Net.

Machine Learning (ML)

Mathematics for Data Science

In this article, we have explored where and how different domain of mathematics are used in Data Science. We have covered core ideas of Probability, Linear Algebra, Eigenvalues, Statistics and much more.

Machine Learning (ML)

One hot encoding in TensorFlow (tf.one_hot)

This article discusses about one of the commonly used data pre-processing techniques in Feature Engineering that is One Hot Encoding and its use in TensorFlow.

TensorFlow

Initializing Tensors in TensorFlow

In this article, we have explored the idea of Tensors in TensorFlow, different types of tensor and how to initialize and use them.

TensorFlow

Dropout operation in TensorFlow (tf.nn.dropout)

This article discusses about a special kind of layer called the Dropout layer in TensorFlow (tf.nn.dropout) which is used in Deep Neural Networks as a measure for preventing or correcting the problem of over-fitting.

Machine Learning (ML)

Interview questions on Data Science

In this article, we have presented 40 Interview questions on Data Science covering several topics including Multiple choice questions (MCQs) and Descriptive questions with answers.

Machine Learning (ML)

Scaled-YOLOv4 model

The authors of YOLOv4 pushed the YOLOv4 model forward by scaling it's design and scale and thus outperforming the benchmarks of EfficientDet. This resulted in Scaled-YOLOv4 model.

Machine Learning (ML)

SELU (Scaled Exponential Linear Unit) Activation Function

We will look at the very promising but not very common activation function called SELU (Scaled Exponential Linear Unit) and understand its main advantages over other activation functions like ReLU (Rectified Linear Unit). This article presents lots of essential points about this function.

Machine Learning (ML)

MatMul in TensorFlow

In this article, we have explored MatMul operation in TensorFlow (tf.linalg.matmul()) and have presented a sample TensorFlow Python code performing MatMul (Matrix Multiplication).

Machine Learning (ML)

Pointwise Convolution

In this article, we will cover Pointwise Convolution which is used in models like MobileNetV1 and compared it with other variants like Depthwise Convolution and Depthwise Seperable Convolution.

Machine Learning (ML)

YOLOv4 model architecture

This article discusses about the YOLOv4's architecture. It outperforms the other object detection models in terms of the inference speeds. It is the ideal choice for Real-time object detection, where the input is a video stream.

Machine Learning (ML)

EfficientDet model architecture

In this article, we have explored EfficientDet model architecture which is a modification of EfficientNet model and is used for Object Detection application.

Machine Learning (ML)

RNN Based Encoder and Decoder for Image Compression

In this article, we will be discussing a about RNN Based Encoder and Decoder for Image Compression.

Machine Learning (ML)

Law of Large Numbers

The law of large numbers is one of the intuitive laws in probability and statistics.

Machine Learning (ML)

Image compression using K means clustering

In this article, we will look at Image Compression using K-means Clustering which is an unsupervised learning algorithm. This is a lossy Image Compression technique.

Machine Learning (ML)

Xavier Initialization

In this article, we have explained the concept of Xavier Initialization which is a weight initialization technique used in Neural Networks.

Machine Learning (ML)

Understanding Inception-ResNet V1 architecture

In this article, we have explored the architecture of Inception-ResNet v1 model and understand the need for the model.

Machine Learning (ML)

ReLU (Rectified Linear Unit) Activation Function

We will take a look at the most widely used activation function called ReLU (Rectified Linear Unit) and understand why it is preferred as the default choice for Neural Networks. This article tries to cover most of the important points about this function.

Machine Learning (ML)

Image Compression: ML Techniques and Applications

In this article, we will discuss Image Compression application in depth involving Machine Learning Techniques like RNN based Encoder and Decoder and applications of Image Compression.

Machine Learning (ML)

Pose Estimation

In this article, we will be discussing about pose estimation and its applications. When the machine uses computer vision to detect the shape and the structure of the human body, it is known as pose estimation.

Machine Learning (ML)

Central limit theorem

Central limit theorem is an important theorem in statistics and probability. But before gaining more knowledge about it, let us first get to know about normal distribution and sampling from a distribution.

Machine Learning (ML)

Q-learning Function: An Introduction

In this article, we will be covering the Q-function, which is a value based learning algorithm in reinforcement learning.