Category 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 …
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 …
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 …
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 …
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 …
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 …
How does Dall-E 2 Work? Concepts, Examples
Have you ever wondered how generative AI is converting words into images? Or how generative AI models create a picture of something you’ve only described in words? Creating high-quality images from textual descriptions has long been a challenge for artificial intelligence (AI) researchers. That’s where DALL-E and DALL-E 2 comes in. In this blog, we will look into the details related to Dall-E 2. Developed by OpenAI, DALL-E 2 is a cutting-edge AI model that can generate highly realistic images from textual descriptions. So how does DALL-E 2 work, and what makes it so special? In this blog post, we’ll explore the key concepts and techniques behind DALL-E 2, including …
Self-Supervised Learning: Concepts, Examples
Self-supervised learning is a hot topic in the world of data science and machine learning. It is an approach to training machine learning models using unlabeled data, which has recently gained significant traction due to its effectiveness in various applications. Self-supervised learning differs from supervised learning, where models are trained using labeled data, and unsupervised learning, where models are trained using unlabeled data without any pre-defined objectives. Instead, self-supervised learning defines pretext tasks as training models to extract useful features from the data that can be later fine-tuned for specific downstream tasks. The potential of self-supervised learning has already been demonstrated in many real-world applications, such as image classification, natural …
Sklearn Neural Network Example – MLPRegressor
Are you interested in using neural networks to solve complex regression problems, but not sure where to start? Sklearn’s MLPRegressor can help you get started with building neural network models for regression tasks. While the packages from Keras, Tensorflow or PyTorch are powerful and widely used in deep learning, Sklearn’s MLPRegressor is still an excellent choice for building neural network models for regression tasks when you are starting on. Recall that Python Sklearn library is one of the most popular machine learning libraries, and it provides a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. In this blog post, we will be focusing on training a …
Backpropagation Algorithm in Neural Network: Examples
Artificial Neural Networks (ANN) are a powerful machine learning / deep learning technique inspired by the workings of the human brain. Neural networks comprise multiple interconnected nodes or neurons that process and transmit information. They are widely used in various fields such as finance, healthcare, and image processing. One of the most critical components of an ANN is the backpropagation algorithm. Backpropagation algorithm is a supervised learning technique used to adjust the weights of a Neural Network to minimize the difference between the predicted output and the actual output. In this post, you will learn about the concepts of backpropagation algorithm used in training neural network models, along with Python …
Generative AI: Scaling Techniques for LLM Models
In the rapidly evolving world of artificial intelligence, large language models (LLMs) have emerged as a game-changing force, revolutionizing the way we interact with technology and transforming countless industries. These powerful models can perform a vast array of tasks, from text generation and translation to question-answering and summarization. However, unlocking the full potential of these LLMs requires a deep understanding of how to effectively scale these LLMs, ensuring optimal performance and capabilities. In this blog post, we will delve into the crucial concept of scaling techniques for LLM models and explore why mastering this aspect is essential for anyone working in the AI domain. As the complexity and size of …
Meta Unveils SAM and Massive SA-1B Dataset to Advance Computer Vision Research
Meta Researchers have, yesterday, unveiled a groundbreaking new model, namely Segment Anything Model (SAM), alongside an immense dataset, the Segment Anything Dataset (SA-1B), which together promise to revolutionize the field of computer vision. SAM’s unique architecture and design make it efficient and effective, while the SA-1B dataset provides a powerful resource to fuel future research and applications. The Segment Anything Model is an innovative approach to promptable segmentation that combines an image encoder, a flexible prompt encoder, and a fast mask decoder. Its design allows for real-time, interactive prompting in a web browser on a CPU, opening up new possibilities for computer vision applications. One of the key challenges SAM …
Autoencoder vs Variational Autoencoder (VAE): Differences
In the world of generative AI models, autoencoders (AE) and variational autoencoders (VAEs) have emerged as powerful unsupervised learning techniques for data representation, compression, and generation. While they share some similarities, these algorithms have unique properties and applications that distinguish them from each other. This blog post aims to help machine learning / deep learning enthusiasts gain a deeper understanding of these two methods, their key differences, and how they can be utilized in various data-driven tasks. We will learn about autoencoders and VAEs, understanding their core components, working mechanisms, and common use-cases. We will also try and understand their differences in terms of architecture, objectives, and outcomes. What are …
I found it very helpful. However the differences are not too understandable for me