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Articles by Parth Shukla
22 articles
Python Tensorflow - tf.keras.Conv2D() Function
In deep learning, computer vision is one of the most important fields used for complex tasks like image analysis, object detection, and segmentation. TensorFlow and Keras provide powerful built-in functions that automate and simplify the model training process. The Conv2D function is one of the most useful tools in Keras for applying convolutional operations to images. In this article, we'll explore what Conv2D is, how to use it, and see practical examples. What are Convolutional Operations? Convolutional operations are fundamental operations used in Convolutional Neural Networks (CNNs) to extract features from input image data. These operations use ...
Read MoreCompute Classification Report and Confusion Matrics in Python
In machine learning, classification problems require careful evaluation to understand model performance. The classification report and confusion matrix are essential tools that help us evaluate classification models and identify where they make mistakes. This article will explore these evaluation methods through practical Python examples, covering their components, interpretation, and implementation using scikit-learn. What is a Confusion Matrix? A confusion matrix is a table that summarizes the performance of a classification model by comparing predicted vs. actual values. It contains four key components ? True Positive (TP): Model correctly predicts positive class True Negative (TN): Model ...
Read MoreZipping Two Unequal Length Lists in a Python Dictionary
In Python, zipping two lists of unequal length into a dictionary is a common requirement when working with data. When lists have different sizes, we need special techniques to handle the mismatch and create meaningful key-value pairs. This article explores five different methods to zip unequal-length lists into dictionaries, each handling the length difference in a specific way. Understanding the Problem When zipping two unequal lists, we need to decide how to handle the extra elements. The most common approach is to cycle through the shorter list, repeating its values to match the longer list's length. ...
Read MoreHow to Zip Uneven Tuple in Python
In Python, when working with tuples of different lengths, the standard zip() function stops at the shortest tuple. However, there are several methods to zip uneven tuples where all elements from the longer tuple are preserved by cycling through the shorter one. What is Zipping of Uneven Tuples? Zipping combines elements from multiple tuples into pairs. For example: # Standard zip stops at shortest tuple t1 = (1, 2, 3, 4) t2 = ("a", "b") result = list(zip(t1, t2)) print("Standard zip result:", result) Standard zip result: [(1, 'a'), (2, 'b')] ...
Read MoreZip Different Sized Lists in Python
When working with lists in Python, you may need to combine elements from multiple lists of different sizes. The zip() function normally stops at the shortest list, but several techniques allow you to handle different-sized lists effectively. What is List Zipping? Zipping combines elements from multiple lists into pairs or tuples. For example: list1 = [1, 2, 3] list2 = ['One', 'Two', 'Three'] zipped = list(zip(list1, list2)) print(zipped) [(1, 'One'), (2, 'Two'), (3, 'Three')] However, when lists have different lengths, zip() stops at the shortest list ? list1 ...
Read MoreHow to Read Machine Learning Papers?
Machine Learning and Deep Learning are rapidly evolving fields with new research published daily. Whether you're a beginner or experienced practitioner, learning to read research papers effectively is crucial for staying current with the latest developments and advancing your understanding. Reading machine learning papers requires a structured approach to maximize comprehension while minimizing time investment. This article outlines a systematic 5-step process for efficiently reading and understanding ML research papers. Step 1: Find Appropriate Papers Selecting the right papers is crucial for your learning journey. Reading papers that are too advanced or irrelevant to your goals can ...
Read MoreHow to Conduct a Paired Samples T-Test
Introduction In machine learning and data science, many statistical tests are used to compare and find the differences between variables or the features of the data. These tests are mainly hypothesis tests where the conditions are defined, and according to the different tests being conducted, the relationship between variables is assumed. The t-test is also a type of statistical test that is used to compare the means of different groups of the categorical variable. In this article, we will discuss the paired t-test, which is an extension or a type of t-test used in statistics, and we will ...
Read MoreImproving Business Decision-Making Using Time Series
Introduction Time series is one of the widely used in machine learning and data science, used to forecast and analyze the data collected with time components. It is a field of intelligence where the data can be forecasted and analyzed with the help of past data collected. In industry, businesses are using the time series analyzed and related methods to improvise their decision-making process. In this article, we will discuss the ways in which the time series can help improve the decision-making process in the industry and how businesses are using the same to enhance their productivity and ...
Read MoreWhat is Padding in Neural Networks
Introduction Padding is one of the most used concepts in neural networks while working with convolutional neural networks. It is a most known concept to every neural network engineer and deep learning engineer to efficiently extract useful information from the given dataset in deep learning. In this article, we will discuss padding, what it is, why we need padding in neural networks, what is the significance of the same, and how we can apply padding in neural networks with code examples. This article will help one to understand padding from scratch and apply it when necessary. What is ...
Read MoreMachine Learning Engineer vs. Data Scientist: Which is Better?
Introduction Data Science and machine learning are the trending fields in current business scenarios, where almost all kinds of product and service-based companies are leveraging Machine learning and data science techniques to enhance their productivity and advance their workflows. In such cases, many data aspirants are trying to enter the field, but the issue here is with the role. As one single individual can not master all the fields in AI and hence the need for selection of roles comes, which becomes very confusing but important for the career. In this article, we will discuss the machine ...
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