# Category Archives: Deep Learning

## 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 …

## 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 & Multi-layer Perceptron Examples

Neural networks are an important part of machine learning, so it is essential to understand how they work. A neural network is a computer system that has been modeled based on a biological neural network comprising neurons connected with each other. It can be built to solve machine learning tasks, like classification and regression problems. The perceptron algorithm is a representation of how neural networks work. The artificial neurons were first proposed by Frank Rosenblatt in 1957 as models for the human brain’s perception mechanism. This post will explain the basics of neural networks with a perceptron example. You will understand how a neural network is built using perceptrons. This …

## Free Datasets for Machine Learning & Deep Learning

Are you looking for free / popular datasets to use for your machine learning or deep learning project? Look no further! In this blog post, we will provide an overview of some of the best free datasets available for machine learning and deep learning. These datasets can be used to train and evaluate your models, and many of them contain a wealth of valuable information that can be used to address a wide range of real-world problems. So, let’s dive in and take a look at some of the top free datasets for machine learning and deep learning! Here is the list of free data sets for machine learning & …

## Checklist for Training Deep Learning Models

Deep learning is a powerful tool for solving complex problems, but it can be difficult to get started. In this blog post, we’ll provide a checklist of things to keep in mind when training and evaluating the deep learning models and deciding whether they are suitable to deploy in production. By following this checklist, you can ensure that your models are well-trained and ready to tackle real-world tasks. Validation of data distribution The distribution of data can have a significant impact on the performance of deep learning models. When training a model, it is important to ensure that the training data is representative of the distribution of the data that …

## 100 Interview Questions for Deep Learning

If you’re looking for a job in deep learning, you’ll need to be prepared to answer some tough questions. In order to help you get started, we’ve put together a list of 100 interview questions for deep learning. While many of these questions are related to deep learning concepts, we have also listed several frameworks (Tensorflow, Pytorch, etc) related questions. By being prepared for these questions, you’ll be able to demonstrate your knowledge and expertise in this area, and increase your chances of landing the job! What is deep learning? How does machine learning differ from deep learning? What are the differences between shallow and deep learning? How does deep …

## 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 …

## Tensor Explained with Python Numpy Examples

Tensors are a hot topic in the world of data science and machine learning. But what are tensors, and why are they so important? In this post, we will explain the concepts of Tensor using Python Numpy examples with the help of simple explanation. We will also discuss some of the ways that tensors can be used in data science and machine learning. When starting to learn deep learning, you must get a good understanding of the data structure namely tensor as it is used widely as the basic data structure in frameworks such as tensorflow, PyTorch, Keras etc. Stay tuned for more information on tensors! What are tensors, and why are …

## Tensor Broadcasting Explained with Examples

In this post, you will learn about the concepts of Tensor Broadcasting with the help of Python Numpy examples. Recall that Tensor is defined as the container of data (primarily numerical) most fundamental data structure used in Keras and Tensorflow. You may want to check out a related article on Tensor – Tensor explained with Python Numpy examples. Broadcasting of tensor is borrowed from Numpy broadcasting. Broadcasting is a technique used for performing arithmetic operations between Numpy arrays / Tensors having different shapes. In this technique, the following is done: As a first step, expand one or both arrays by copying elements appropriately so that after this transformation, the two tensors have the …

## Regularization in Machine Learning: Concepts & Examples

In machine learning, regularization is a technique used to avoid overfitting. This occurs when a model learns the training data too well and therefore performs poorly on new data. Regularization helps to reduce overfitting by adding constraints to the model-building process. As data scientists, it is of utmost importance that we learn thoroughly about the regularization concepts to build better machine learning models. In this blog post, we will discuss the concept of regularization and provide examples of how it can be used in practice. What is regularization and how does it work? Regularization in machine learning represents strategies that are used to reduce the generalization or test error of …

I found it very helpful. However the differences are not too understandable for me