Author: Naveen
Naveen PandeyDay 7 – What Are GANs? | Generative Adversarial Networks in Deep Learning
In this article, we will explore an important and popular deep learning neural network called Generative Adversarial Networks (GANs). GANs were introduced in 2014 by Ian J. Goodfellow and co-authors and have since become very popular in the field of machine learning. GANs are an unsupervised learning task that consists of two models, the generator…
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Deque : Memory Efficient Alternative To Python Lists
In this blog, we will be covering deque, which stands for Double Ended Queue in Python. We will explore why this data structure is very useful, especially when managing a stack in Python. We will go over the methods that come with the Double Ended Queue and how we can use it to handle queues…
Read MoreDay 6 – What is Loss Function in Deep Learning | Loss Function in Machine Learning | Loss Function Types
In this blog, we will cover the concept of a loss function and its significance in artificial neural networks. Loss functions play a crucial role in model training, as they are used by stochastic gradient descent to minimize the error during the training process. We will discuss how loss functions are calculated and their importance…
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11 Tips to Instantly Improve Your Python Code
Python is a powerful programming language known for its simplicity and readability. In this article, we will explore 11 tips that can instantly improve your Python code. These tips include best practices that make your code cleaner and more pythonic. Tip 1: Iterate with `enumerate` instead of `range(len())` When you need to iterate over a…
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Understanding Principal Component Analysis in Machine Learning
In our fast-paced world, data grows more complex each day and, by extension, more challenging to interpret. In machine learning, we use a mathematical technique called Principal Component Analysis (PCA) to simplify our data —that is, reduce features or dimensions while trying to maintain as much information as possible. Why PCA Matters? There are various…
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What is Feature Engineering in Machine Learning | Feature Engineering Techniques
If you are into machine learning, then you probably know that feature engineering is an important step in building a machine-learning model that actually works. Feature engineering is the process of transforming existing features or creating new features to improve the performance of a machine-learning model. Feature engineering is the process of taking raw data…
Read MoreLeNet-5 Architecture Explained | Introduction to LeNet-5 Architecture
LeNet-5 is a compact neural network comprising fundamental components of deep learning convolutional layers, pooling layers, and fully connected layers. It serves as a foundational model for other deep learning architectures. Let’s talk about the LeNet-5 and enhance our understanding of convolutional and pooling layers through practical examples. Introduction to LeNet-5 LeNet-5 consists of seven…
Read MoreAlexnet Architecture Explained | Introduction to Alexnet Architecture
The introduction of AlexNet in 2012 has changed the image recognition field. Thousands of researchers and entrepreneurs were able to approach artificial intelligence in a different manner by using this deep neural network that Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton created together. Considering how strictly quantitative and limited computer vision technology was: barely classifying…
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Top 10 AI and ML Project Ideas for 2023
Artificial Intelligence (AI) is a rapidly growing field that can seem daunting to beginners. However, there are many basic AI projects that beginners can take up to gain experience and knowledge. In this blog post, we will explore 10 AI and ML projects ideasthat are perfect for beginners. These projects cover a wide range of…
Read MoreResNet(Residual Networks) Explained – Deep Learning
In this blog post, we will explore the concept of residual networks in deep learning. Residual networks, also known as ResNets, have revolutionized the field of deep learning by enabling the training of extremely deep neural networks. We will discuss the motivation behind ResNets, their architecture, and how they address the challenges of training deep…
Read MoreSigmoid Activation Function in Detail Explained
When it comes to artificial neural networks, the Sigmoid activation function is a real superstar! It might sound like a fancy term, but don’t worry; we’re going to break it down in a way that even your grandma would understand. What’s the Buzz About Activation Functions? Before we zoom in on the Sigmoid activation function,…
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Efficient Data Manipulation with Apply() Function in Pandas
If you’re a data enthusiast like me, you’ve probably dabbled in the world of Python and Pandas, the go-to library for data manipulation and analysis. Now, imagine you have a massive dataset with thousands of rows, and you want to perform some custom operations on it. Well, don’t fret! Pandas has your back, and it…
Read MoreFeatured Articles
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Zero to Python Hero – Part 5/10: Essential Data Structures in Python: Lists, Tuples, Sets & Dictionaries
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Top 5 Skills Every Engineer Should Learn in 2026
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Zero to Python Hero - Part 3/10 : Understanding Type Casting, Operators, User Input and String formatting (with Code Examples)
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Dynamic Programming in Reinforcement Learning: Policy and Value Iteration
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