Author Archives: Ajitesh Kumar
MSE vs RMSE vs MAE vs MAPE vs R-Squared: When to Use?
Last updated: 22nd April, 2024 As data scientists, we navigate a sea of metrics to evaluate the performance of our regression models. Understanding these metrics – Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-Squared – is crucial for robust model evaluation and selection. In this blog, we delve into the intricacies of these different metrics while learning them based on clear definitions, formulas, and guidance on when to use which of these metrics. Different Types of Regression Models Evaluation Metrics The following are different types of regression model evaluation metrics including MSE, RMSE, MAE, MAPE, R-squared, and Adjusted …
Gradient Descent in Machine Learning: Python Examples
Last updated: 22nd April, 2024 This post will teach you about the gradient descent algorithm and its importance in training machine learning models. For a data scientist, it is of utmost importance to get a good grasp on the concepts of gradient descent algorithm as it is widely used for optimizing/minimizing the objective function / loss function / cost function related to various machine learning models such as regression, neural network, etc. in terms of learning optimal weights/parameters. This algorithm is essential because it underpins many machine learning models, enabling them to learn from data by optimizing their performance. Introduction to Gradient Descent Algorithm The gradient descent algorithm is an optimization …
Loss Function vs Cost Function vs Objective Function: Examples
Last updated: 19th April, 2024 Among the terminologies used in training machine learning models, the concepts of loss function, cost function, and objective function often cause a fair amount of confusion, especially for aspiring data scientists and practitioners in the early stages of their careers. The reason for this confusion isn’t unfounded, as these terms are similar / closely related, often used interchangeably, and yet, they are different and serve distinct purposes in the realm of machine learning algorithms. Understanding the differences and specific roles of loss function, cost function, and objective function is more than a mere exercise in academic rigor. By grasping these concepts, data scientists can make …
Model Parallelism vs Data Parallelism: Examples
Last updated: 19th April, 2024 Model parallelism and data parallelism are two strategies used to distribute the training of large machine-learning models across multiple computing resources, such as GPUs. They form key categories of multi-GPU training paradigms. These strategies are particularly important in deep learning, where models and datasets can be very large. What’s Data Parallelism? In data parallelism, we break down the data into small batches. Each GPU works on one batch of data at a time. It calculates two things: the loss, which tells us how far off our model’s predictions are from the actual outcomes, and the loss gradients, which guide us on how to adjust the …
Model Complexity & Overfitting in Machine Learning: How to Reduce
Last updated: 4th April, 2024 In machine learning, model complexity, and overfitting are related in that the model overfitting is a problem that can occur when a model is too complex for different reasons. This can cause the model to fit the noise in the data rather than the underlying pattern. As a result, the model will perform poorly when applied to new and unseen data. In this blog post, we will discuss model complexity and how you can avoid overfitting in your machine-learning models by handling the model complexity. As data scientists, it is of utmost importance to understand the concepts related to model complexity and how it impacts …
6 Game-Changing Features of ChatGPT’s Latest Upgrade
OpenAI has once again set the tech world abuzz with its latest enhancement to ChatGPT, making it a lot easier to use. With a clear focus on user-friendliness and accessibility, this update marks a significant leap forward. Here are the updates on the latest features: Ease of Access: No Sign-Up Required The sign-up barrier has been eliminated, allowing instant access to its ChatGPT. This ensures that access to ChatGPT is just a click away for anyone curious enough to explore it. Customizable Creativity: Choose an Image Style The integration of DALL·E GPT into ChatGPT now includes an option to choose from various image styles, adding a layer of personalization to …
Self-Prediction vs Contrastive Learning: Examples
In the dynamic realm of AI, where labeled data is often scarce and costly, self-supervised learning helps unlock new machine learning use cases by harnessing the inherent structure of data for enhanced understanding without reliance on extensive labeled datasets as in the case of supervised learning. Simply speaking, self-supervised learning, at its core, is about teaching models to learn from the data itself, turning unlabeled data into a rich source of learning. There are two distinct methodologies used in self-supervised learning. They are the self-prediction method and contrastive learning method. In this blog, we will learn about their concepts and differences with the help of examples. What is the Self-Prediction …
Free IBM Data Sciences Courses on Coursera
In the rapidly evolving fields of Data Science and Artificial Intelligence, staying ahead means continually learning and adapting. In this blog, there is a list of around 20 free data science-related courses from IBM available on coursera.org that can help data science enthusiasts master different domains in AI / Data Science / Machine Learning. This list includes courses related to the core technical skills and knowledge needed to excel in these innovative fields. Foundational Knowledge: Understanding the essence of Data Science lays the groundwork for a successful career in this field. A solid foundation helps you grasp complex concepts easily and contributes to better decision-making, problem-solving, and the capacity to …
Self-Supervised Learning vs Transfer Learning: Examples
Last updated: 3rd March, 2024 Understanding the difference between self-supervised learning and transfer learning, along with their practical applications, is crucial for any data scientist looking to optimize model performance and efficiency. Self-supervised learning and transfer learning are two pivotal techniques in machine learning, each with its unique approach to leveraging data for model training. Transfer learning capitalizes on a model pre-trained on a broad dataset with diverse categories, to serve as a foundational model for a more specialized task. his method relies on labeled data, often requiring significant human effort to label. Self-supervised learning, in contrast, pre-trains models using unlabeled data, creatively generating its labels from the inherent structure …
OKRs vs KPIs vs KRAs: Differences and Examples
Last updated: 21st Feb, 2024 The difference between OKRs , KPIs, and KRAs is often confused, but the concept is a great way to measure the progress toward achieving your business objectives. As business analysts, product managers, and project or team leaders, it is important to understand the concepts of OKRs, KPIs, & KRAs and what’s the differences between them. In this blog post, we will discuss OKR vs KPI vs KRAs and how they can be used for setting goals/objectives and measuring different aspects of your team’s and organization’s performance in achieving those goals. We’ll also go over real-world examples so you can get a better understanding of how these metrics …
CEP vs Traditional Database Examples
In this blog, we will learn about the differences between complex event processing (CEP) and traditional database querying with the help of examples. We will learn about how these two methodologies tackle data to extract meaningful insights but in fundamentally different ways. In complex event processing, data flows dynamically which is then matched with pre-defined patterns thereby generating insights in real-time. Traditional Database Querying In a conventional database querying scenario, the data is stored first, and then queries are run against this stored data to find patterns or retrieve information. This process is reactive, in that the query is formulated based on a need to find out something specific about …
Retrieval Augmented Generation (RAG) & LLM: Examples
Last updated: 26th Jan, 2024 Have you ever wondered how to seamlessly integrate the vast knowledge of Large Language Models (LLMs) with the specificity of domain-specific knowledge stored in file storage, image storage, vector databases, etc? As the world of machine learning continues to evolve, the need for more sophisticated and contextually relevant responses from LLMs becomes paramount. Lack of contextual knowledge can result in LLM hallucination thereby producing inaccurate, unsafe, and factually incorrect responses. This is where context augmentation with prompts, and, hence retrieval augmentated generation method, comes into the picture. For data scientists and product managers keen on deploying LLMs in production, the Retrieval Augmented Generation pattern offers …
Attention Mechanism in Transformers: Examples
Last updated: 1st Feb, 2024 The attention mechanism allows the model to focus on relevant words or phrases when performing NLP tasks such as translating a sentence or answering a question. It is a critical component in transformers, a type of neural network architecture used in NLP tasks such as those related to LLMs. In this blog, we will delve into different aspects of the attention mechanism (also called an attention head), common approaches (such as self-attention, cross attention, etc.) to calculating and implementing attention, and learn the concepts with the help of real-world examples. You can get good details in this book: Generative Deep Learning by David Foster. You …
NLP Tokenization in Machine Learning: Python Examples
Last updated: 1st Feb, 2024 Tokenization is a fundamental step in Natural Language Processing (NLP) where text is broken down into smaller units called tokens. These tokens can be words, characters, or subwords, and this process is crucial for preparing text data for further analysis like parsing or text generation. Tokenization plays a crucial role in training machine learning models, particularly Large Language Models (LLMs) like GPT (Generative Pre-trained Transformer) series, BERT (Bidirectional Encoder Representations from Transformers), and others. Tokenization is often the first step in preparing text data for machine learning. LLMs use tokenization as an essential data preprocessing step. Advanced tokenization techniques (like those used in BERT) allow …
Large Language Models (LLMs): Types, Examples
Last updated: 31st Jan, 2024 Large language models (LLMs), being the key pillar of generative AI, have been gaining traction in the world of natural language processing (NLP) due to their ability to process massive amounts of text and generate accurate results related to predicting the next word in a sentence, given all the previous words. These different LLM models are trained on a large or broad corpus of text datasets, which contain hundreds of millions to billions of words. LLMs, as they are known, rely on complex algorithms including transformer architectures that shift through large datasets and recognize patterns at the word level. This data helps the LLMs better understand …
Amazon (AWS) Machine Learning / AI Services List
Last updated: 30th Jan, 2024 Amazon Web Services (AWS) is a cloud computing platform that offers machine learning as one of its many services. AWS has been around for over 10 years and has helped data scientists leverage the Amazon AWS cloud to train machine learning models. AWS provides an easy-to-use interface that helps data scientists build, test, and deploy their machine learning models with ease. AWS also provides access to pre-trained machine learning models so you can start building your model without having to spend time training it first! You can get greater details on AWS machine learning services, data science use cases, and other aspects in this book – …
I found it very helpful. However the differences are not too understandable for me