Tag Archives: Deep Learning
Activation Functions in Neural Networks: Concepts, Examples
Last updated: 24th Nov, 2023 The activation functions are critical to understanding neural networks. There are many activation functions available for data scientists to choose from, when training neural networks. So, it can be difficult to choose which activation function will work best for their needs. In this blog post, we look at different activation functions and provide examples of when they should be used in different types of neural networks. If you are starting on deep learning and wanted to know about different types of activation functions, you may want to bookmark this page for quicker access in the future. What are activation functions in neural networks? In a …
Different Types of CNN Architectures Explained: Examples
Last updated: 16th Nov, 2023. In the fast-paced world of computer vision and image processing, one problem consistently stands out: the ability to effectively recognize and classify images. As we continue to digitize and automate our world, the demand for systems that can understand and interpret visual data is growing at an unprecedented rate. The challenge is not just about recognizing images – it’s about doing so accurately and efficiently. Traditional machine learning methods often fall short, struggling to handle the complexity and high dimensionality of image data. This is where Convolutional Neural Networks (CNNs) comes to rescue. The CNN architectures are the most popular deep learning framework. CNNs shown …
Encoder Only Transformer Models Quiz / Q&A
Are you intrigued by the revolutionary world of transformer architectures? Have you ever wondered how encoder-only transformer models like BERT, ELECTRA, or DeBERTa have reshaped the landscape of Natural Language Processing (NLP)? The rapid advancement of machine learning has led to the creation of numerous transformer architectures, each with unique features, applications, and underlying mechanics. Whether you’re a data scientist, machine learning engineer, generative AI enthusiast, or a student eager to deepen your understanding, this quiz offers an engaging and informative way to assess your knowledge and sharpen your skills. It would also help you prepare for your interviews on this topic. Encoder-only transformer models have become a cornerstone in …
Encoder-only Transformer Models: Examples
How can machines accurately classify text into categories? What enables them to recognize specific entities like names, locations, or dates within a sea of words? How is it possible for a computer to comprehend and respond to complex human questions? These remarkable capabilities are now a reality, thanks to encoder-only transformer architectures like BERT. From text classification and Named Entity Recognition (NER) to question answering and more, these models have revolutionized the way we interact with and process language. In the realm of AI and machine learning, encoder-only transformer models like BERT, DistilBERT, RoBERTa, and others have emerged as game-changing innovations. These models not only facilitate a deeper understanding of …
LLMs & Semantic Search Course by Andrew NG, Cohere & Partners
Andrew Ng, a renowned name in the world of deep learning and AI, has joined forces with Cohere, a pioneer in natural language processing technologies. Alongside him are Jay Alammar, a well-known educator and visualizer of machine learning concepts, and Serrano Academy, an esteemed institution dedicated to AI research and education. Together, they have launched an insightful course titled “Large Language Models with Semantic Search.” This collaboration represents a fusion of expertise aimed at addressing the growing needs of semantic search in various applications. In an era where keyword search has dominated the search landscape, the need for more sophisticated, content-aware search capabilities is becoming increasingly evident. Content-rich platforms like …
Transformer Architecture Types: Explained with Examples
Are you fascinated by the power of deep learning models that can translate languages, generate creative writing, and even answer complex questions? Ever wondered how a machine can understand and process human language with such finesse? At the heart of these remarkable achievements lies a machine learning model architecture that has revolutionized the field of Natural Language Processing (NLP) – the Transformer architecture, a deep learning architecture. But what makes Transformer models so special? How do they manage to encode the subtle nuances of language and context? Can we understand the complex mathematical machinery that operates behind the scenes? Whether you’re a seasoned data scientist, an aspiring machine learning engineer, …
Quiz: BERT & GPT Transformer Models Q&A
Are you fascinated by the world of natural language processing and the cutting-edge generative AI models that have revolutionized the way machines understand human language? Two such large language models (LLMs), BERT and GPT, stand as pillars in the field, each with unique architectures and capabilities. But how well do you know these models? In this quiz blog, we will challenge your knowledge and understanding of these two groundbreaking technologies. Before you dive into the quiz, let’s explore an overview of BERT and GPT. BERT (Bidirectional Encoder Representations from Transformers) BERT is known for its bidirectional processing of text, allowing it to capture context from both sides of a word …
Transfer Learning vs Fine Tuning: Differences
Generative AI is revolutionizing various domains, from natural language processing to image recognition. Two concepts that are fundamental to these advancements are Transfer Learning and Fine Tuning. Despite their interconnected nature, they are distinct methodologies that serve unique purposes when training large language models (LLMs) to achieve different objectives. In this blog, we will explore the differences between Transfer Learning and Fine Tuning, learning about their individual characteristics and how they come into play in real-world scenarios with the help of examples. What is Transfer Learning? Transfer Learning is an AI / ML concept that refers to the utilization of a pre-trained model on a new but related task. It …
Pre-training vs Fine-tuning in LLM: Examples
Are you intrigued by the inner workings of large language models (LLMs) like BERT and GPT series models? Ever wondered how these models manage to understand human language with such precision? What are the critical stages that transform them from simple neural networks into powerful tools capable of text prediction, sentiment analysis, and more? The answer lies in two vital phases: pre-training and fine-tuning. These stages not only make language models adaptable to various tasks but also bring them closer to understanding language the way humans do. In this blog, we’ll dive into the fascinating journey of pre-training and fine-tuning in LLMs, complete with real-world examples. Whether you are a …
BERT vs GPT Models: Differences, Examples
Are you intrigued by the world of natural language processing (NLP) and the cutting-edge machine learning models that power it? Have you ever wondered what sets apart two of the most prominent models in the field, Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT)? These models have revolutionized the way machines understand and generate human language, but what exactly differentiates them? In this blog, we will delve into the core architecture, training objectives, real-world applications, examples and more. By exploring these aspects, we’ll learn about the unique strengths and use cases of both models, providing you with insights that can guide your next project or research endeavor. …
Vanishing Gradient Problem in Deep Learning: Examples
Ever found yourself wondering why your deep learning (deep neural network) model is simply refusing to learn? Or struggled to comprehend why your deep neural network isn’t reaching the accuracy you expected? The culprit behind these issues might very well be the infamous vanishing gradient problem, a common hurdle in the field of deep learning. Understanding and mitigating the vanishing gradient problem is a must-have skill in any data scientist‘s arsenal. This is due to the profound impact it can have on the training and performance of deep neural networks. In this blog post, we will delve into the heart of this issue, learning the calculus behind neural networks and …
DCGAN Architecture Concepts, Real-world Examples
Have you ever wondered how AI can create lifelike images that are virtually indistinguishable from reality? Well, there is a neural network architecture, Deep Convolutional Generative Adversarial Network (DCGAN) that has revolutionized image generation, from medical imaging to video game design. DCGAN’s ability to create high-resolution, visually stunning images has brought it into great usage across numerous real-world applications. From enhancing data augmentation in medical imaging to inspiring artists with novel artworks, DCGAN‘s impact transcends traditional machine learning boundaries. In this blog, we will delve into the fundamental concepts behind the DCGAN architecture, exploring its key components and the ingenious interplay between its generator and discriminator networks. Together, these components …
Large Language Models (LLMs) & Semantic Search
Ever scratched your head wondering how a few typed words can bring up the precise information you need from the sprawling web? That’s the work of something called Large Language Models or LLMs, like the GPT-series from OpenAI. These large language models (LLMs) can be used to search that needle-in-a-haystack piece of information you’re after. So, how do they do it? They use smart techniques like Dense Retrieval, Reranking, and Generative Search. In this blog, you will learn about these great techniques in an easy-to-understand way. Dense Retrieval Dense retrieval is a departure from traditional information retrieval approaches that often rely on sparse features like Bag-of-Words (BoW) and Term Frequency-Inverse …
Generative Adversarial Network (GAN): Concepts, Examples
In this post, you will learn concepts & examples of generative adversarial network (GAN). The idea is to put together key concepts & some of the interesting examples from across the industry to get a perspective on what problems can be solved using GAN. As a data scientist or machine learning engineer, it would be imperative upon us to understand the GAN concepts in a great manner to apply the same to solve real-world problems. This is where GAN network examples will prove to be helpful. What is Generative Adversarial Network (GAN)? We will try and understand the concepts of GAN with the help of a real-life example. Imagine that …
NLP Pre-trained Models: Concepts, Examples
The NLP (Natural Language Processing) is a branch of AI with the goal to make machines capable of understanding and producing human language. NLP has been around for decades, but it has recently seen an explosion in popularity due to pre-trained models (PTMs) which can be implemented with minimal effort and time on the side of NLP developers. This blog post will introduce you to different types of pre-trained machine learning models for NLP and discuss their usage in real-world examples. Before we get into looking at different types of pre-trained models for NLP, let’s understand the concepts related to pre-trained models for NLP. What are pre-trained models for NLP …
Demystifying Encoder Decoder Architecture & Neural Network
In the field of AI / machine learning, the encoder-decoder architecture is a widely-used framework for developing neural networks that can perform natural language processing (NLP) tasks such as language translation, etc which requires sequence to sequence modeling. This architecture involves a two-stage process where the input data is first encoded into a fixed-length numerical representation, which is then decoded to produce an output that matches the desired format. As a data scientist, understanding the encoder-decoder architecture and its underlying neural network principles is crucial for building sophisticated models that can handle complex data sets. By leveraging encoder-decoder neural network architecture, data scientists can design neural networks that can learn …
Nice question to help us