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Machine Learning (ML)

Machine Learning is the fastest growing and most potential field that enables a computer to perform specific tasks better than humans. It is actively used in companies like Apple, Tesla, Google and Facebook. We are covering the latest developments in the field

Deep Learning

Self-attention in Transformer

Today we will discuss one of the revolutionary concepts in the artificial intelligence sector not only in Natural Language Processing but also nowadays in the Computer Vision, which is the Transformers and the heart of it Self-Attention.

Ahmed Mandour Ahmed Mandour
Python

2D Convolution in Python

In this article, we will understand the concept of 2D Convolution and implement it using different approaches in Python Programming Language.

Ahmed Mandour Ahmed Mandour
Deep Learning

Single Image Super Resolution

We will explain to you about a very important and strong application in image processing and of course in Machine Learning which is "Single Image Super Resolution". We have listed different model architectures, datasets and research papers for this application (Single Image Super Resolution).

Ahmed Mandour Ahmed Mandour
Deep Learning

Calibration in Machine and Deep Learning

In this article, I introduce calibration in Machine Learning and Deep Learning, an useful concept that not many people know.

Nguyen Quoc Trung
Deep Learning

Instance Segmentation

In this article, we will dive deeper in a very important concept in computer vision which considered a great progress in deep learning field which is Instance Segmentation. what is it?, the most popular algorithm for it, and its evaluation metric.

Ahmed Mandour Ahmed Mandour
Machine Learning (ML)

Panoptic quality (PQ), segmentation quality (SQ) and recognition quality (RQ)

In this article, we will dive deeper in evaluation metrics for computer vision tasks especially for Panoptic segmentation namely Panoptic quality (PQ), segmentation quality (SQ) and recognition quality (RQ).

Ahmed Mandour Ahmed Mandour
Deep Learning

Fault Detection System: Predict defective solar module cells

In this article we will build a fault detection system using a deep learning model : EfficientNet to distinguish between defective and non-defective cells that are extracted from solar modules.

CHERIFI Imane
Algorithms

Perlin Noise (with implementation in Python)

One of the most important algorithms in computer graphics and procedural generation is Perlin Noise. Perlin Noise is an algorithm that generates textures and terrain-like images procedurally (without the need for an artist to manually create the images).

Akanksha Singh
Deep Learning

Wake Sleep Algorithm For Neural Network

In this article, we have explored the Wake Sleep Algorithm For Neural Network along with examples of using it and limitations of it.

Muhsina Munfa Muhsina Munfa
Deep Learning

Types of Gradient Optimizers in Deep Learning

In this article, we will explore the concept of Gradient optimization and the different types of Gradient Optimizers present in Deep Learning such as Mini-batch Gradient Descent Optimizer.

Muhsina Munfa Muhsina Munfa
Deep Learning

VGG54 and VGG22

VGG54 and VGG22 are loss metrics to compare high and low resolution images by considering the feature maps generated by VGG19 neural network model.

Jonathan Buss Jonathan Buss
Deep Learning

Gradient Accumulation [+ code in PyTorch]

Gradient Accumulation is an optimization technique that is used for training large Neural Networks on GPU and help reduce memory requirements and resolve Out-of-Memory OOM errors while training. We have explained the concept along with Pytorch code.

Jonathan Buss Jonathan Buss
Machine Learning (ML)

Evaluation metrics for object detection and segmentation

In this article, we will go through the Evaluation metrics for Object Detection and Segmentation that is Image segmentation, semantic segmentation and instance segmentation in depth.

Ahmed Mandour Ahmed Mandour
Deep Learning

Calculate mean and std of Image Dataset

In this article, we have explained how to calculate the mean and standard deviation (std) of an image dataset which can be used to normalize images in the dataset for effective training of Neural Networks.

Jonathan Buss Jonathan Buss
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.

Ahmed Mandour Ahmed Mandour
Machine Learning (ML)

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.

CHERIFI Imane
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.

Mert Cihangiroglu
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?

CHERIFI Imane
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.

Mert Cihangiroglu
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.

Jonathan Buss Jonathan Buss
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.

Jonathan Buss Jonathan Buss
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.

Mert Cihangiroglu
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.

Aditi Patil
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.

CHERIFI Imane
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.

Troy Martin Troy Martin
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