Tag Archives: Deep Learning
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 …
Different Types of CNN Architectures Explained: Examples
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 remarkable success in tackling the …
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 …
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 …
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 …
Machine Learning: Identify New Features for Disease Diagnosis
When diagnosing diseases that require X-rays and image-based scans, such as cancer, one of the most important steps is analyzing the images to determine the disease stage and to characterize the affected area. This information is central to understanding clinical prognosis and for determining the most appropriate treatment. Developing machine learning (ML) / deep learning (DL) based solutions to assist with the image analysis represents a compelling research area with many potential applications. Traditional modeling techniques have shown that deep learning models can accurately identify and classify diseases in X-rays and image-based scans and can even predict patient prognosis using known features, such as the size or shape of the …
Transposed Convolution vs Convolution Layer: Examples
In the field of computer vision and deep learning, convolutional neural networks (CNNs) are widely used for image recognition tasks. A fundamental building block of CNNs is the convolutional layer, which extracts features from the input image by convolving it with a set of learnable filters. However, another type of layer called transposed convolution, also known as deconvolution, has gained popularity in recent years. In this blog post, we will compare and contrast these two types of layers, provide examples of their usage, and discuss their strengths and weaknesses. What are Convolutional Layer? What’s their purpose? A convolutional layer is a fundamental building block of a convolutional neural network (CNN). …
CNN Basic Architecture for Classification & Segmentation
As data scientists, we are constantly exploring new techniques and algorithms to improve the accuracy and efficiency of our models. When it comes to image-related problems, convolutional neural networks (CNNs) are an essential tool in our arsenal. CNNs have proven to be highly effective for tasks such as image classification and segmentation, and have even been used in cutting-edge applications such as self-driving cars and medical imaging. Convolutional neural networks (CNNs) are deep neural networks that have the capability to classify and segment images. CNNs can be trained using supervised or unsupervised machine learning methods, depending on what you want them to do. CNN architectures for classification and segmentation include …
Keras: Multilayer Perceptron (MLP) Example
Artificial Neural Networks (ANN) have emerged as a powerful tool in machine learning, and Multilayer Perceptron (MLP) is a popular type of ANN that is widely used in various domains such as image recognition, natural language processing, and predictive analytics. Keras is a high-level API that makes it easy to build and train neural networks, including MLPs. In this blog, we will dive into the world of MLPs and explore how to build and train an MLP model using Keras. We will build a simple MLP model using Keras and train it on a dataset. We will explain different aspects of training MLP model using Keras. By the end of …
Neural Network Types & Real-life Examples
Neural networks are a powerful tool for data scientists, machine learning engineers, and statisticians. They have revolutionized the field of machine learning and have become an integral part of many real-world applications such as image and speech recognition, natural language processing, and autonomous vehicles. ChatGPT is a classic example how AI / neural network applications has taken world by storm. But what exactly are they and what are their different types? There are various types of neural networks, each with their own unique architecture and learning algorithm. Understanding the different types of neural networks and their real-life examples is crucial for anyone interested in machine learning and artificial intelligence. In …
Sequence to Sequence Models: Types, Examples
Sequence to sequence (Seq2Seq) modeling is a powerful machine learning technique that has revolutionized the way we do natural language processing (NLP). It allows us to process input sequences of varying lengths and produce output sequences of varying lengths, making it particularly useful for tasks such as language translation, speech recognition, and chatbot development. Sequence to sequence modeling also provides a great foundation for creating text summarizers, question answering systems, sentiment analysis systems, and more. With its wide range of applications, learning about sequence to sequence modeling concepts is essential for anyone who wants to work in the field of natural language processing. This blog post will discuss types of …
Perceptron Explained using Python Example
In this post, you will learn about the concepts of Perceptron with the help of Python example. It is very important for data scientists to understand the concepts related to Perceptron as a good understanding lays the foundation of learning advanced concepts of neural networks including deep neural networks (deep learning). What is Perceptron? Perceptron is a machine learning algorithm which mimics how a neuron in the brain works. It is also called as single layer neural network consisting of a single neuron. The output of this neural network is decided based on the outcome of just one activation function associated with the single neuron. In perceptron, the forward propagation of information happens. Deep …
85+ Free Online Books, Courses – Machine Learning & Data Science
This post represents a comprehensive list of 85+ free books/ebooks and courses on machine learning, deep learning, data science, optimization, etc which are available online for self-paced learning. This would be very helpful for data scientists starting to learn or gain expertise in the field of machine learning / deep learning. Please feel free to comment/suggest if I missed mentioning one or more important books that you like and would like to share. Also, sorry for the typos. Following are the key areas under which books are categorized: Data science Pattern Recognition & Machine Learning Probability & Statistics Neural Networks & Deep Learning Optimization Data mining Mathematics Here is my post …
Activation Functions in Neural Networks: Concepts
The activation functions are critical to understanding neural networks. It is important to use the activation function in order to train the neural network. There are many activation functions available for data scientists to choose from, 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 …