Machine Learning
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30 Must-Know TensorFlow Interview Questions and Answers
Prepare for interviews by understanding the fundamental concepts, practicing coding, and exploring real-world use cases of TensorFlow. These TensorFlow interview questions and...
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20 Commonly Asked Data Science Interview Questions and Answers
Here are 20 commonly asked data science interview questions and answers. 1. What is the role of a data scientist in a business...
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How is Machine Learning transforming and revolutionizing the automobile sector?
Machine learning is revolutionizing the automobile sector in various ways. Machine learning is driving innovation and transformation in the automobile industry, paving...
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How is Machine Learning transforming and revolutionizing the healthcare sector?
Machine learning is having a transformative impact on the healthcare sector. Machine learning is revolutionizing healthcare by enhancing diagnosis accuracy, personalizing treatments, improving...
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13Preprocessing of raw data in NLP: Tokenization, Stemming, Lemmatization and Vectorization
NLP (Natural Language Processing) is a very interesting branch of Artificial Intelligence. Natural language is a language which we human use to...
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19Image Recognition: Text Detection (Optical Character Recognition) using Google Cloud Vision API
Google Cloud Vision API helps in label detection, face detection, logo detection, landmark detection and text detection (OCR: Optical Character Recognition). In this...
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15Image Recognition: Label Detection using Google Cloud Vision API
Google Cloud Vision API helps in label detection, face detection, logo detection, landmark detection and text detection. In this article, we will see...
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16Image Recognition using Pre-trained VGG16 model in Keras
Lets use a pre-trained VGG16 model to predict an image from ImageNet database. We will load an image, convert that image to...
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13eBook – Deep Learning Objective Type Questions and Answers
This book contains 205 objective type questions and answers covering various basic concepts of deep learning. It contains 19 chapters. Each chapter...
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15Solving a regression problem using a Sequential Neural Network Model in Keras
Lets solve a regression problem using neural networks. We will build a sequential model in Keras to predict house prices based on...
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10Fine-tune VGG16 model for image classification in Keras
Keras framework provides us a lot of pre-trained general purpose deep learning models which we can fine-tune as per our requirements. We...
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17Building a CNN model in Keras using MNIST dataset
We will implement CNN in Keras using MNIST dataset. To know more about CNN, you can visit my this post. We can download the MNIST dataset through...
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All about Keras Framework in Deep Learning
Keras is a widely used framework to implement neural networks in deep learning. Keras is very easy to use and understand and...
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Building a simple sequential neural network with dense layers in Keras
Lets understand how can we create a simple neural network in Keras. We will create a simple sequential model with dense layers (fully...
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100+ Basic Deep Learning Interview Questions and Answers
I have listed down some basic deep learning interview questions with answers. These deep learning interview questions cover many concepts like perceptrons,...
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Xavier Weight Initialization Technique in Neural Networks
Weight initialization is the most important step while training the neural network. If weights are high, it may lead to exploding gradient. If...
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Batch Normalization in Neural Networks in Deep Learning
Batch normalization (batchnorm) is a technique to improve performance and accuracy of a neural network. Many times, normalization and standardization terms are...
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Transfer Learning and Fine Tuning a model in Deep Learning
Transfer learning and fine-tuning terms are very similar in many ways and widely used almost interchangeably. Fine-tuning: Suppose you already have an...
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Capsule Neural Networks: An enhancement of Convolutional Neural Networks (ConvNets vs CapsNets)
Capsule Neural Networks can be seen as an enhancement of Convolutional Neural Networks. In order to understand capsule neural networks, lets first...
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Difference between Sigmoid and Softmax function in deep learning
Softmax function can be understood as a generalized version of a sigmoid function or an extension of a sigmoid function. Softmax function is...
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Regularization Techniques used in Neural Networks in Deep Learning
Ideally, the neural networks should never underfit and overfit and maintain good generalization capabilities. For this purpose, we use various regularization techniques...
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Hyperparameter Tuning in Neural Networks in Deep Learning
In order to minimize the loss and determine optimal values of weight and bias, we need to tune our neural network hyper-parameters....
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Global and Local Minima in Gradient Descent in Deep Learning
Task of a Gradient Descent optimizer is to find out optimal weights for the parameters. But sometimes, it may end up in...
