Machine Learning (ML)

Backpropagation vs Gradient Descent

Hello everybody, I'll illustrate in this article two important concepts in our journey of neural networks and deep learning. Welcome to Backpropagation and Gradient Descent tutorial and the differences between the two.

EfficientNet [model architecture]

Convnet have hit the memory limit it is time to look for more efficient ways to improve the accuracy. For that, we introduce in this article the EfficientNet model that suggests an efficient way for improving the performance of Convnets.

Machine Learning (ML)

Cost Functions [6 types]

In this article, we will explore different types of cost functions; metrics used to calibrate the model's accuracy iteratively.

Machine Learning (ML)

Self-Supervised Learning [Explained]

For these reasons, it is impossible to further advance the deep learning field by only relying on supervised learning paradigms. We need intelligent systems that can generalize well without the need for labeled datasets, so how are researches evolving toward this goal?

Machine Learning (ML)

What is overfitting? [+ Solutions for it]

In this article, we will explore overfitting and how to avoid it. Overfitting is an important concept for you to learn so that you can develop reliable models for your needs.

Machine Learning (ML)

GELU vs ReLU

In this article, we have explored the differences between GELU (Gaussian Error Linear Unit) and ReLU (Rectified Linear Unit) activation functions in depth.

Machine Learning (ML)

Activation function GELU in BERT

In BERT, GELU is used as the activation function instead of ReLU and ELU. In this article, we have explained (both based on experiments and theoretically) why GELU is used in BERT model.

Machine Learning (ML)

Types of Gradient Descent

In this article, we will explore Gradient Descent Algorithm and it's variants. Gradient Descent is an essential optimization algorithm that helps us finding optimum parameters of our machine learning models.

Machine Learning (ML)

Disadvantages of SVM

In this article, we have presented 5 Disadvantages of Support Vector Machine (SVM) and explained each point in depth.

Machine Learning (ML)

YOLO v5 model architecture [Explained]

Since 2015 the Ultralytics team has been working on improving this model and many versions since then have been released. In this article we will take a look at the fifth version of this algorithm YOLOv5.

Machine Learning (ML)

ReLU6 in ML

ReLU6 uses this same theory but instead limits the positive direction to a maximum size of 6. This is extremely useful when dealing with fixed-point inference.

Machine Learning (ML)

Radial Basis Function Neural Network

Radial Basis Function Neural Network (RBFNN) is one of the shallow yet very effective neural networks. It is widely used in Power Restoration Systems.

Machine Learning (ML)

Boltzmann Machines

Neural Networks are by far the most used connectionist model and one of them is the Boltzmann Machine that we will cover in this article.

Machine Learning (ML)

Deep Belief Network

Deep Belief Networks are unsupervised learning models that overcome these limitations. We will explore them in details in this article.

Algorithms

Sparse matrix multiplication

In this article, we aim to study sparse matrices and the sparse matrix multiplication.

Machine Learning (ML)

Delta Rule in Neural Network

We shall be discussing Delta rule in neural networks that is used to updated weights during training a Neural Network.

Machine Learning (ML)

Kohonen Neural Network

The Kohonen Neural Network (KNN) also known as self organizing maps is a type of unsupervised artificial neural network. This network can be used for clustering analysis and visualization of high-dimension data.

Machine Learning (ML)

Gradient Ascent

Gradient Ascent as a concept transcends machine learning. It is the reverse of Gradient Descent, another common concept used in machine learning. Gradient Ascent (resp. Descent) is an iterative optimization algorithm used for finding a local maximum (resp. minimum) of a function.

Machine Learning (ML)

Out-of-Bag Error in Random Forest [with example]

In this article, we have explored Out-of-Bag Error in Random Forest with an numeric example and sample implementation to plot Out-of-Bag Error.

Machine Learning (ML)

XNet architecture: X-Ray image segmentation

Medical image processing is an important application in Computer Vision,requires segmentation of images into body parts. Joseph Bullock and his partners in Durham University proposed a neuron network called XNet which is suitable for this task.

Machine Learning (ML)

Word Embeddings: GloVe and Word2Vec

Word Embeddings correlates the likeness of the meaning of words with their relative similarity and represent them numerically as a vector.

Machine Learning (ML)

Feature Selector Using LASSO

A technique for reducing the dimensionality of machine learning datasets is the Feature Selector. The selection process of the Feature Selector is based on a logically accurate measurement that determines the importance of each feature present in the data.

Machine Learning (ML)

Seq2seq: Encoder-Decoder Sequence to Sequence Model Explanation

In Deep learning, we all know that Recurrent Neuron Network solves time series data. Sequence to Sequence (or Seq2Seq for short) is a kind of model that was born to solve "Many to many" problem.

Machine Learning (ML)

Questions on Random Forest

In this article, we have presented the most important Interview Questions on Random Forest.

Machine Learning (ML)

Super Resolution GAN: SRGAN

Super Resolution GAN (SRGAN) is generative adversarial network that can generate high resolution images from low resolution images using perceptual loss function that is made of the adversarial loss as well as the content loss.