Search Results for: deep learning
Day 7 – What Are GANs? | Generative Adversarial Networks in Deep Learning
In this article, we will explore an important and popular deep learning neural network called Generative Adversarial Networks (GANs). GANs were introduced in 2014 by Ian J. Goodfellow and co-authors and have since become very popular in the field of machine learning. GANs are an unsupervised learning task that consists of two models, the generator…
Read MoreDay 6 – What is Loss Function in Deep Learning | Loss Function in Machine Learning | Loss Function Types
In this blog, we will cover the concept of a loss function and its significance in artificial neural networks. Loss functions play a crucial role in model training, as they are used by stochastic gradient descent to minimize the error during the training process. We will discuss how loss functions are calculated and their importance…
Read MoreResNet(Residual Networks) Explained – Deep Learning
In this blog post, we will explore the concept of residual networks in deep learning. Residual networks, also known as ResNets, have revolutionized the field of deep learning by enabling the training of extremely deep neural networks. We will discuss the motivation behind ResNets, their architecture, and how they address the challenges of training deep…
Read MoreDay 5: Everything you need to know about Activation Functions in Deep learning
Deep learning is a powerful area of artificial intelligence that has received a lot of attention in recent years. One of the main components of deep learning models is the activation function. Activation functions play a crucial role in determining the output of a neural network. In this article, we will dive deep into understanding…
Read MoreDay 3: Deep Learning vs. Machine Learning: Key Differences Explained
In the world of artificial intelligence, two terms often mentioned are “deep learning” and “machine learning.” Both technologies play significant roles in the development of intelligent systems, but what sets them apart? In this article, we will delve into the key differences between deep learning and machine learning, exploring their applications, methodologies, and unique characteristics.…
Read MoreDay 1: Introduction to Deep Learning: Understanding the Basics
Welcome to this Deep Learning Course! Today is the beginning of our course and in this first article we will be talking about basics of Deep Learning. So, Let’s get started. Deep learning, is a subfield of machine learning that focuses on algorithms inspired by the structure of the human brain called which is an…
Read More10 Tips for Training Deep Learning Models
Deep learning models have made significant impact in fields ranging from computer vision to natural language processing. However, training these models can be a daunting task that requires a lot of knowledge and expertise. In this blog, we will see 10 tips for training sustainable deep learning models. 1 – Start with a small dataset:…
Read MoreImportant Deep learning Concept Explained Part – 2
Converge Algorithm that converges will eventually reach an optimal answer, even if very slowly. An algorithm that doesn’t converge may never reach an optimal answer. Learning Rate Rate at which optimizers change weights and biases. High learning rate generally trains faster but risks not converging whereas a lower rate trains slower. Numerical instability Issues with…
Read MoreImportant Deep learning Concept Explained Part – 1
Neuron Node is a NN, typically taking in multiple input values and generating one output value by applying an activation function (nonlinear transformation) to weighted sum of input values. Weights Edges is a NN, the goal of training is to determine the optimal weight for each feature; if weight = 0, corresponding feature does not…
Read MoreTop 8 Deep Learning Algorithms
Convolutional Neural Networks CNN’s popularly known as ConvNets majority consists of several layers and are specifically used for image processing and detection of objects. It was developed in 1998 by Yann LeCun. CNNs have wide usage in identifying the image of the satellites, medical image processing, series forecasting, and anomaly detection. CNNs process the data…
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