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Suppose you have a script in the Google Cloud’s VM Instance that needs to run every day/week/month at a particular time ( e.g., a data pipeline for Machine Learning model training, data crawling, etc.). In that case, this article will guide you through automating the task. Different ways you can automate a script on …
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There are several key differences between using machine learning for research and using it for production. One of the main differences is the focus of the work. Machine learning for research typically focuses on exploring new ideas and techniques, and on advancing the state of the art in the field. In contrast, machine …
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PyTorch and Keras are both open-source deep learning frameworks, but they have some significant differences. PyTorch is a low-level framework that allows you to define your own computation graphs, while Keras is a high-level framework that provides a pre-defined set of layers and routines for building deep learning …
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Author: Sadman Kabir Soumik GPT-3, or Generative Pretrained Transformer 3, is a state-of-the-art language model developed by OpenAI. It has been trained on a massive amount of text data, including books, articles, and websites, to generate coherent and relevant text based on a given context. GPT-3 is a …
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What is Vanishing Gradient Problem Neural networks are trained using stochastic gradient descent. This involves first calculating the prediction error made by the model and using the error to estimate a gradient used to update each weight in the network so that less error is made next time. This error gradient is …
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Combine two lists as a dictionary | dict(zip) Program 1keys = ['a', 'b', 'c'] 2values = [1, 2, 3] 3dictionary = dict(zip(keys, values)) 4print(dictionary) Output 1{'a': 1, 'b': 2, 'c': 3} Program 1keys = ('name', 'age', 'location') 2values = …
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There are several types of ensemble techniques in machine learning, including: Bagging, Boosting, Stacking, Blending, Bootstrapped ensembles, Bayesian model averaging. Bagging Bagging (short for bootstrapped aggregating) is an ensemble technique that involves training multiple models on different subsets of the …
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Python's collections module implements specialized container datatypes providing alternatives to Python’s general purpose built-in containers, dict, list, set, and tuple. This module has the following containers: 11. Counter() 22. namedtuple() 33. deque() 44. defaultdict() 55. OrderedDict() 66. UserDict() 77. …
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Decision Tree, Random Forest (RF), and Gradient Boosting (GB) are three popular algorithms used for supervised learning tasks such as classification and regression. In this blog, we will compare these three algorithms in terms of their features, performance, and usability. Decision Tree is a simple and intuitive …
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Start With A Simple Architecture To begin building an app, we will start from the beginning. We will create a basic app with some users. The easiest way to do this is to put the whole app on one server. This is a common way to start. The app and any API's will run on a server like Apache or Tomcat. We will also use a …
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Security Linux tends to be a highly reliable and secure system than any other operating system (OS). Linux and Unix-based OS have fewer security flaws, as many developers constantly review the code. And anyone has access to its source code. So, you won’t need any anti-virus software to protect your PC from malware and …
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Word embedding is a technique in natural language processing (NLP) where words are represented as vectors of real numbers. This allows words with similar meanings to have similar representation, and can be used in various NLP tasks such as machine translation and text classification. There are several different …
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Lambda The Lambda function, also known as an anonymous or inline function, is a way to create a function without giving it a name. This can be useful when you need to define a function that will only be used once, or when you want to pass a function as an argument to another function. Here is an example of using a …
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Recurrent Neural Network A recurrent neural network (RNN), is a type of neural network that can process sequential data, like text, audio, or time series data. Here's how it works: first, the RNN takes in some input data, which could be a word in a sentence, a sound wave from an audio recording, or a measurement from a …
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Bit Manipulation Bit manipulation is a technique used in computer science to manipulate data at the level of its binary representation. This can be useful for a variety of tasks, such as low-level optimization, data compression, and cryptography. At its core, bit manipulation involves working with individual bits, …
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Cleaning text for natural language processing (NLP) tasks is an important step that can help improve the performance of your model. In this blog post, we will discuss some common text cleaning techniques and how to apply them to your text data. The first step in cleaning text for NLP is to remove any noisy or …
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REST (Representational State Transfer) and gRPC are two different techniques for creating web APIs (Application Programming Interfaces). What is REST API? A popular architectural design for developing web APIs is REST. It is built on the HTTP (HyperText Transfer Protocol) and operates on resources defined by URLs using …
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Dynamic programming is a method for solving complex problems by breaking them down into smaller subproblems. It is a mathematical optimization technique that is mainly used for solving problems that exhibit the properties of overlapping subproblems and optimal substructure. The basic idea behind dynamic programming is …
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Let's say you've got a big school project to do. You need to write a report, paint a picture, and build a model. Here's how you might tackle the project under different scenarios: Multi-processing This is like having multiple copies of yourself, each working independently on a different part of the project. One of you …
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There are several different types of recommender systems, each with its own unique characteristics and applications. Some of the most commonly used types of recommender systems include: Content-based recommender systems: These systems recommend items to users based on the characteristics of the items themselves. For …
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