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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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Computer Vision interview Question Part-2
1 – What programming languages does computer vision support? Computer vision is a field of computer science and engineering that deals with how computers can be made to gain high-level understanding from digital images or videos. The following are the programming languages that support computer vision: – C++ – Java – Python – Prolog 2…
Read MoreComputer Vision interview Question Part-1
1 – What are machine learning algorithms available in OpenCV? OpenCV is an open-source library for computer vision, machine learning and image processing. It provides a set of machine learning algorithms that can be used for various applications in the field The following are some of the machine learning algorithms available in OpenCV: 1 –…
Read MoreNatural Language Processing Interview questions Part – 2
1 – What is an ensemble method in NLP? Ensemble methods are a group of machine learning algorithms that work together to solve a problem. They are typically used when the machine learning algorithm is not able to solve the problem on its own or when it has not been trained enough. An ensemble method…
Read MoreNatural Language Processing Interview questions Part – 1
1 – What are some of the common NLP tasks? NLP is the process of understanding a sentence and then generating a response. It has been used in many different industries to help humans do their jobs more efficiently. Some of the common NLP tasks are: Speech recognition: This is when a computer converts spoken…
Read MoreMachine Learning Interview questions Part -2
1 – Define precision and recall? The precision and recall are two measures of data quality. They are used to determine the proportion of relevant data that is found by a search algorithm. Precision is a measure of how many of the retrieved records are correct. Recall is a measure of how many of the…
Read MoreMachine Learning Interview questions Part -1
1 – What are Different Types of Machine Learning algorithms? There are various types of machine learning algorithms. The most popular ones include supervised learning, unsupervised learning and reinforcement learning. Supervised Learning: Supervised machine learning is when a human has to provide the correct answer for the algorithm to learn from. This is done by…
Read MoreDeep Learning interview questions Part -2
1 – What are autoencoders? Explain the different layers of autoencoders. Autoencoders are neural networks that are trained to reconstruct an input data into a desired output data. They can be thought of as the opposite of a traditional classifier, which is trained to classify inputs into pre-defined classes. Autoencoders can be seen as a…
Read MoreDeep Learning interview questions Part -1
1 – What is data normalization? What’s the need for it? Data normalization is a process of transforming data from one format to another in order to improve the quality of the data and make it more usable for analysis. In this process data is organized and formatted in such a way that it’s easier…
Read MoreWhat is Tanh activation function?
The Tanh Activation function is a scaled and shifted version of the hyperbolic tangent function, a mathematical function frequently encountered in trigonometry and calculus. The Tanh function squashes input values within the range of -1 to 1, making it a useful choice for activation functions in neural networks. Defining the Tanh Function Mathematically The mathematical…
Read MoreWhat is PReLU and ELU activation function?
PReLU(Parametric ReLU) – PReLU is vital to the success of deep learning. It solves the problem with activation functions like sigmoid, where gradients would often vanish. This approach is finding more and more success in deep learning environments. But, we can still improve upon ReLU. Leaky ReLU was introduced, which does not zero out the…
Read MoreDifference between Leaky ReLU and ReLU activation function?
What is an Activation Function? An activation function is a critical component in neural networks. It determines a neuron’s output after the neuron processes its inputs by computing a weighted sum. The activation function decides whether the neuron should be activated or not, introducing nonlinearity to the model. This nonlinearity enables the model to learn…
Read MoreWhat is ReLU and Sigmoid activation function?
The activation function is a nonlinear function that takes in the weighted sum and produces the output. They are used to provide a more simplified model of neuron behavior which can be used as an input to deep neural networks. There are many different activation functions that can be used, including sigmoid, hyperbolic tangent, logistic,…
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