Machine Learning (ML)

BERT Large Model

BERT large model is a pretrained model on English language which uses a Masked Language Modeling (MLM for short). It has 24 encoder layers.

Find set of faces when combined results in face of person A

We have demonstrated a mini-project where we Find a set of faces when combined results in face of person A. We do this using Machine Learning techniques and will give you a good idea of applications of ML.

Machine Learning (ML)

Inception V4 architecture

We have explored about a neural network architecture called Inception and understand in great detail its fourth version, the Inception V4 along with the architecture of InceptionV4 model.

Machine Learning (ML)

Introduction to Pandas

This article is for those people who are going to willing to build their career in python and data analysis. Most of you reading this post are probably familiar and heard about Pandas, and have probably used it in many projects.

Machine Learning (ML)

Embeddings in BERT

We will see what is BERT (bi-directional Encoder Representations from Transformers). How the BERT actually works and what are the embeddings in BERT that make it so special and functional compared to other NLP learning techniques.

Machine Learning (ML)

Single Shot Detector (SSD) + Architecture of SSD

In this article, we will be discussing Single Shot Detector (SSD), an object detection model that is widely used in our day to day life. And we will also see how the SSD works and what makes the SSD better than other object detection models out there.

Machine Learning (ML)

Architecture of YOLOv3

We have presented the Architecture of YOLOv3 model along with the changes in YOLOv3 compared to YOLOv1 and YOLOv2, how YOLOv3 maintains its accuracy and much more.

Machine Learning (ML)

Dense Layer in Tensorflow

We have explained Dense Layer in Tensorflow with code examples and the use of Dense Layer in Neural Networks.

Machine Learning (ML)

Applications of Machine Learning

We have covered common ML tasks/ applications with respect to image and textual data along with the models used and examples of its use in various domains.

Machine Learning (ML)

Early Exit in ML models

How does the addition of early exits improve the performance of the neural networks and what are the other additional advantages it provides to the model.

Machine Learning (ML)

The ShuffleNet Series (Part 1)

The breakthrough CNN architecture for object classification in mobile devices, ShuffleNet has been explained in-depth in this article. Its newer and better versions ShuffleNet V2, V2+, Large & X-Large has also been elucidated.

Natural Language Processing (NLP)

Word Embedding [Complete Guide]

We have explained the idea behind Word Embedding, why it is important, different Word Embedding algorithms like Embedding layers, word2Vec and other algorithms.

Machine Learning (ML)

Inception V3 Model Architecture

We will learn about what is Inception V3 model Architecture and its working. How it is better than its previous versions like the Inception V1 model and other Models like ResNet. What are its advantages and disadvantages?

Machine Learning (ML)

SSD MobileNetV1 architecture

We have dived deep into what is MobileNet, what makes it special amongst other convolution neural network architectures, Single-Shot multibox Detection (SSD) how MobileNet V1 SSD came into being and its architecture.

Machine Learning (ML)

Boosting, an Ensemble Method

We have covered the idea of Boosting in depth along with different types of Boosting algorithms, benefits and challenges of Boosting.

Machine Learning (ML)

Architecture of DenseNet-121

We have explored the architecture of a Densely Connected CNN (DenseNet-121) and how it differs from that of a standard CNN. DenseNet-121 has 120 Convolutions and 4 AvgPool.

Machine Learning (ML)

Basics of Gradient descent + Stochastic Gradient descent

We have explained the Basics of Gradient descent and Stochastic Gradient descent along with a simple implementation for SGD using Linear Regression.

Machine Learning (ML)

Dilated Convolution [explained]

Dilated convolution, is also known as Atrous Convolution or convolution with holes. The idea behind dilated convolution is to "inflate" the kernel which in turn skips some of the points. We can see the difference in the general formula and some visualization.

Machine Learning (ML)

Separable convolution in Machine Learning

In MobileNet architecture, you must have stumbled across the term "Separable convolution". What is separable convolution and how is it different from regular convolution? We have explained everything in this article.

Machine Learning (ML)

Questions on Regression [with answers]

Practice multiple choice questions on Regression with answers. This is one of the fundamental techniques in Machine Learning which is widely used in basic problems.

Machine Learning (ML)

Principal Component Analysis (PCA) questions [with answers]

Practice multiple choice questions on Principal Component Analysis (PCA) with answers. This is a fundamental technique in Machine Learning applications.

Machine Learning (ML)

Recurrent Neural Network (RNN) questions [with answers]

Practice multiple choice questions on Recurrent Neural Network (RNN) with answers. It is an important Machine Learning model and is a significant alternative to Convolution Neural Network (CNN).

Machine Learning (ML)

Convolution Layer questions [with answers]

Practice multiple choice questions on Convolutional Layers with answers. This is the most important layer in a Machine Learning model in terms of both functionality and computation.

Machine Learning (ML)

Questions on Fully Connected (FC) Layer

Practice multiple choice questions on Fully Connected Layers with answers. These are the most important layer in a Machine Learning model in terms of both functionality and computation.

TensorFlow

Advanced Interview Questions on TensorFlow

We have presented advanced interview questions on TensorFlow with multiple options to choose from. Select an answer to find out if you got it right and get explanation for the answer.