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34 articles

Basics of Machine Learning Image Classification Techniques

2019-07-22 25

You will get n idea about What is Image Classification?, pipeline of an image classification task including data preprocessing techniques, performance of different Machine Learning techniques like Artificial Neural Network, CNN, K nearest neighbor, Decision tree and Support Vector Machines
Deep Q-Learning: Combining Deep Learning and Q-Learning

2019-10-03 24

The idea in deep Q networks is that the states and possible outcomes in Q-Learning is replaced with a neural network which tries to approximate Q Values. It is referred to as the approximator
Document Clustering using K Means

2019-08-30 24

In this article, we use ideas from TF IDF and similarity metrics to use K Means clustering algorithm to cluster documents.
Types of Data Formats in Machine Learning

2019-02-24 22

Each data format represents how the input data is represented in memory. This is important as each machine learning application performs well for a particular data format and worse for others. Various data formats are NHWC, NCHW, NCDHW and NDHWC
Model Evaluation: a crucial step in solving a machine learning problem

2019-04-16 21

Models like Googlenet is used across various problems and MobileNet are designed for computational limited resources. It is a challenge to find the best technique or model for a given problem. We evaluate a model based on Test Harness, Performance Measure, Cross validation and Testing Algorithms.
Activation Functions in Machine Learning: A Breakdown

2020-05-17 20

We have covered the basics of Activation functions intuitively, its significance/ importance and its different types like Sigmoid Function, tanh Function and ReLU function.
Differences between Torch and PyTorch deep learning libraries

2019-12-11 20

We have explored some of the differences between two popular frameworks namely Torch and PyTorch from the view of common origin, current development status, source code and implementation, usage, performance and ONNX support. As development of Torch has been paused, you should definitely go with PyTorch
Ensemble methods in Machine Learning: Bagging, Boosting and Stacking

2019-08-28 20

We will see what an ensemble method is, why they are trendy, and what are the different types of ensemble methods and how to implement these methods using scikit-learn and mlxtend in Python.
Fully Connected Layer: The brute force layer of a Machine Learning model

2019-03-02 20

Fully Connected layers in a neural networks are those layers where all the inputs from one layer are connected to every activation unit of the next layer. In most popular machine learning models, the last few layers are full connected layers which compiles the data extracted by previous layers
Convolution Layer: The layer that takes over 70% of time in a Machine Learning model

2019-03-02 20

Convolutional Layer is the most important layer in a Machine Learning model where the important features from the input are extracted and where most of the computational time (>=70% of the total inference time) is spent. Concepts involved are kernel size, padding, feature map and strides
Deep Learning on 2-Dimentional Images

2019-02-02 20

Applying deep learning concepts on images has proved to be one of the important work which has resulted in early detection of diseases resulting in saving millions of life to monitoring activities on the entire Earth We take a look at medical images, Satellite Images and the various Python libraries
A Deep Learning Approach for Native Language Identification (NLI)

2020-08-14 19

Native language identification (NLI) is the task of determining an author's native language based only on their writings or speeches in a second language. In this article, we will implement a model to identify native language of the author.
Popular Datasets in Machine Learning

2019-12-11 19

Data sets are important in Machine learning as the more better data we have, the better the model. The various popular data sets available for machine learning are ImageNet, MNIST, NIST, CIFAR-10 and YouTube 8M.
Importance of Loss Function in Machine Learning

2019-08-20 19

A loss function is a measure of how good your prediction model does in terms of being able to predict the expected outcome(or value).
Use of deep learning in NLP techniques

2019-07-22 19

You will get an idea about What is NLP?, use of deep learning in NLP and 5 impressive applications of deep learning for NLP like image captioning
Bias in Machine learning

2019-05-04 19

Bias is an constant parameter in the Neural Network which is used in adjusting the output. Therefore Bias is a additional parameter which helps the model so that it can perfectly fit for the given data. It is also known as bias nodes, bias neurons, or bias units
Types of Activation Functions used in Machine Learning

2019-01-30 19

We explored the various types of activation functions that are used in Machine Learning including Identity function, Binary Step, Sigmoid, Tanh, ReLU, Leaky ReLU and SoftMax function. Activation function help the network use the useful information and suppress the irrelevant data points
Top deep learning frameworks to explore

2019-12-11 18

In this article, we have explored some of the top Deep Learning frameworks that are out there and you should definitely try out. Some of them are TensorFlow, Keras, Caffe, Caffe2, MXNet, CNTK, BigDL, Torch, PyTorch, deeplearn.js and others
Deep Learning for Medical Imaging and diagnosis

2019-02-02 18

One of the major medical challenges that we face today is the early detection of diseases so that the proper threatment can be applied. This can be solved by applying machine learning to analyse MRI scan, CT Scan, Xray Scans, InfraRed Images, Arthoscopy, UV radiation, Scintigraphy and Ultrasound
Simplifying "Intriguing properties of neural networks"

2020-02-26 17

In this article, we have explored the paper "Intriguing properties of neural networks" by Christian Szegedy in depth as it is an influential paper which introduces two key properties that define Neural Networks.