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Rajesh Yerremshetty Zero to Python Hero – Part 5/10: Essential Data Structures in Python: Lists, Tuples, Sets & Dictionaries
The fundamental way of storing, accessing and manipulating of data in python is data structures. Python provides an convenient and adaptable collection of objects to store and data and sort it in different ways, be it a list, a tuple,...
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Naveen Pandey Top 5 Skills Every Engineer Should Learn in 2026
The world of engineering is changing faster than ever before. Technologies that were once futuristic like artificial intelligence, machine learning, and cloud computing are now driving industries forward. By 2026, the engineers who thrive won’t just be the one who...
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Zero to Python Hero - Part 4/10 : Control Flow: If, Loops & More (with code examples)
A major element of any programming language is the capability to take decisions and repeat them -this is the so-called control flow. Control flow is a feature available in Python that enables us to have the control of how code...
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Zero to Python Hero - Part 3/10 : Understanding Type Casting, Operators, User Input and String formatting (with Code Examples)
Type Casting & Checking What is Type Casting? Type casting (also called type conversion) is the process of converting a value from one data type to another. It’s like translating between different languages – sometimes you need to convert a number to...
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Dynamic Programming in Reinforcement Learning: Policy and Value Iteration
The core topic of reinforcement learning (RL) Dynamic Programming in RL: Policy and Value Iteration Explained provides fundamental solutions to resolve Markov Decision Processes (MDPs). This piece teaches about Policy Iteration and Value Iteration alongside their mechanisms as well as...
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What is Vanishing and exploding gradient descent?
Vanishing and exploding gradient descent is a type of optimization algorithm used in deep learning. Vanishing Gradient Vanishing Gradient occurs when the gradient is smaller than expected. It causes the earlier layers to start degrading before the later ones do, causing a decrease in the overall learning rate of that subset of layers. The weights…
Read MoreWhat is forward and backward propagation in Deep Learning?
Forward propagation is a process in which the network’s weights are updated according to the input, output and gradient of the neural network. In order to update the weights, we need to find the input and output values. The input value is found by taking the difference between the current hidden-state value and that of…
Read MoreWhat is multilayer perceptron?
A multi-layer perceptron is a type of artificial neural network. It has one or more hidden layers between the input and output layers, each of which can be thought of as a series of processing units connected to each other in a hierarchical tree structure. The input layer nodes are connected to the hidden layer…
Read MoreDifference between machine learning and machine reasoning?
Machine Learning is a subset of artificial intelligence, which is a type of statistical learning. It provides computer programs with the ability to automatically learn from data without being explicitly programmed where to look for patterns. Machine Learning algorithms do not need to be explicitly programmed where to look for patterns in order to find…
Read MoreDifference between perceptron and neuron?
Perceptrons are a type of artificial neural network that can be used for classification and regression. They are supervised learning algorithms, meaning they need labeled input data in order to learn. how to map inputs to outputs. What independent variables do perceptrons need? Perceptrons require at least one input and one output. What are the…
Read MoreWhat is perceptron?
The perceptron is a type of artificial neural network (ANN) that is designed to recognize patterns in data. It can be used to identify objects, classify images, and detect changes in the environment. The perceptron was invented by Frank Rosenblatt in 1957 while he was working at Cornell Aeronautical Laboratory as part of a research…
Read MoreWhat is L1 and L2 regularization in Deep Learning?
In deep learning, L1 and L2 regularization are regularization techniques used to penalize the model’s weights during the training process. This penalty discourages the model from assigning excessive importance to certain features, thereby reducing the risk of overfitting. L1 Regularization L1 regularization, also known as Lasso regularization, adds a penalty proportional to the absolute value…
Read MoreSome examples of simple gradient-based NLP models.
There are a lot of simple gradient-based NLP models that can be used to solve a variety of natural language processing tasks. Some of these include: Part-of-speech tagging: sentence parsing is a task that assigns part of speech tags to words in text and is used to analyze sentences. A task that assigns part of…
Read MoreHow is NLP revolutionizing financial services?
NLP is revolutionizing the way we interact with financial services. It’s allowing us to have a more natural conversation with our banks, and this is allowing us to do things that we couldn’t do before. How did you get into NLP? I got into NLP because I had a need, but it turns out that…
Read More5 things you must know about Computer Vision!
Computer Vision is a technology that enables machines to see, and it is one of the most important technologies in Artificial Intelligence. It is also one of the most difficult technologies to understand. for example, persons. How does machine vision work? The image data can be processed and an object in the image can be…
Read MoreWhat is upsampling and downsampling?
In a classification task, there is a high chance for the algorithm to be biased if the dataset is imbalanced. An imbalanced dataset is one in which the number of samples in one class is very higher or lesser than the number of samples in the other class. An example of an imbalanced dataset is…
Read MoreWhat is GMM and Agglomerative clustering?
A Gaussian mixture is a statistical model that assumes all the data points are generated from a linear combination of multivariate Gaussian distributions. This assumption has unknown parameters that can be estimated from the data, which we refer to as hyperparameters. Firstly, K-means employs the Gaussian distributions and centers of latent Gaussians. However, unlike K-means,…
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