Tilak Raaj

Recurrent Neural Networks (RNNs): Introduction In the realm of deep learning, Recurrent Neural Networks (RNNs) stand out as a powerful tool for processing sequential data. Unlike traditional feedforward neural networks, RNNs possess a unique ability to capture temporal dependencies within data, making them ideal for tasks such as speech recognition, language modeling, time series prediction, … Read more

Computation Graphs: Introduction Computation graphs play a foundational role in understanding and optimizing deep learning models. They provide a visual representation of the mathematical operations performed during the forward and backward passes of training a neural network. In this blog, we will explore what computation graphs are, their components, how they facilitate automatic differentiation, and … Read more

Accelerating Training with Batch: Introduction Training deep learning models on large datasets can be computationally intensive and time-consuming. To address these challenges, batch processing is a powerful technique used to accelerate training and improve the efficiency of neural network models. In this blog, we will explore what batch processing is, its benefits, how it works … Read more

Image Pre-processing Pipelines: Introduction Image pre-processing is a crucial step in developing robust and accurate computer vision models. A well-designed pre-processing pipeline enhances the quality of input images, standardizes their format, and prepares them for effective feature extraction by the model. In this blog, we will explore the components of an image pre-processing pipeline, techniques … Read more

Working with MNIST Dataset: Introduction The MNIST dataset is a classic benchmark in the field of machine learning and computer vision. It consists of a large collection of handwritten digits that have been extensively used to train and test various machine learning models, particularly for image classification tasks. In this blog, we will explore how … Read more

Architecture of Convolutional Neural Networks (CNNs): Introduction Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision and are widely used for image classification, object detection, and many other tasks. The architecture of CNNs is inspired by the human visual system and designed to process grid-like data such as images. In this blog, we’ll … Read more

Shortcomings of Feature Selection: Introduction Feature selection is a well-known technique in traditional machine learning, used to improve model performance by selecting the most relevant features. However, in deep learning, feature selection presents unique challenges. Deep learning models, especially neural networks, have the ability to learn complex representations from raw data, reducing the necessity for … Read more

Convolution Operation and Pooling: Introduction In deep learning, especially in the field of computer vision, convolutional neural networks (CNNs) have become the cornerstone for tasks such as image classification, object detection, and segmentation. Two fundamental operations that make CNNs powerful are convolution and pooling. These operations enable the network to extract important features from images … Read more

Approximate Second-Order: Introduction In deep learning, optimization is crucial for training neural networks to perform well. While commonly used methods like Gradient Descent (a first-order method) are simple and scalable, they often struggle with slow learning and require careful adjustments. Second-order methods can speed up the learning process, but they are usually too expensive to … Read more

Ensemble Methods and Challenges: Introduction In the realm of machine learning, ensemble methods have emerged as powerful techniques that combine multiple models to produce a superior predictive performance compared to individual models. The principle behind ensemble methods is that a group of weak learners can come together to form a strong learner. This blog will … Read more