An article on a Deep Learning Architecture Inspired By An Internet Meme — and its technical information and details. — Introduction Back in 2014, researchers at Google (and other research institutions) published a paper that introduced a novel deep learning convolutional neural network architecture that was, at the time, the largest and most efficient deep neural network architecture. The novel architecture was an Inception Network, and a variant of this Network…

In this article, I will try to give a fairly simple and understandable explanation of one really fascinating type of neural network. Introduced by Cho, et al. in 2014, GRU (Gated Recurrent Unit) aims to solve the vanishing gradient problem which comes with a standard recurrent neural network. GRU can…

Comparable Performance With LSTM, Faster Convergence than Vanilla RNN — In this story, Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling, (GRU), by Université de Montréal, is briefly reviewed. This is a paper from Prof. Bengio’s group. In this paper: GRU is introduced with performance comparable with LSTM. This is a paper in 2014 NeurIPS with over 7800…

Table of Contents ∘ ⭐️ U-Net Recap ∘ ⭐️ UNet++ Innovations ∘ ⭐️ Loss function ∘ ⭐️ Performance · Citations In this article, we are going to introduce you to UNet++, essentially an upgraded version of U-Net. This article is designed to help you understand it intuitively…

The Neural Network used by Open AI and DeepMind — If you liked this post and want to learn how machine learning algorithms work, how did they arise, and where are they going, I recommend the following: Making Things Think: How AI and Deep Learning Power the Products We Use - Holloway It is the obvious which is so difficult to see most of the time. People say 'It's as plain as the nose on your face.'…www.holloway.com

In their 2015 publication “Deep Residual Learning for Image Recognition,” Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun created a particular kind of neural network known as ResNet or Residual Network. — Every consecutive winning architecture uses more layers in a deep neural network to lower the error rate after the first CNN-based architecture (AlexNet) that won the ImageNet 2012 competition. This is effective for smaller numbers of layers, but when we add more layers, a typical deep learning issue known as…

Just a few weeks ago, YOLO v7 came into the limelight by beating all the existing object detection models to date. Anyone who has worked in Object detection has heard about YOLO. It’s been here for a while now, and to date, we have seen a lot of YOLO versions…

When beginning to build your first neural network, the process seems akin to feeling around for a needle in a haystack, blindfolded. …

Reinforcement Learning is one of the most intricate fields of Machine Learning, due to its mathematical complexity, as well as the ambitious tasks it tries to achieve. Simply put, the final goal of a RL algorithm is to make an agent capable of acting in an environment as to maximize…

How to use big data on a small computer using Tensorflow, Python and iterators — The amount of data in the current business world is increasing every day. There are new data sources to merge, more rows to append and new columns to concatenate. Unfortunately, for a typical data scientist or a machine learning engineer, the pace at which one can buy a new laptop…

Explaining their complex mathematical formula with working diagrams — Table of Contents · Recurrent Neural Networks (RNN) ∘ Vanilla RNN ∘ Long Short-term Memory (LSTM) ∘ Gated Recurrent Unit (GRU) · RNN Architectures · Attention ∘ Seq2seq with Attention ∘ Self-attention ∘ Multi-head Attention · Transformer ∘ Step 1. Adding Positional Encoding to Word Embeddings ∘ Step…

Overview of how object detection works, and where to get started — Note: This assumes you have a general idea of what convolutional neural networks are. If you need a refresher, this IBM post is excellent. You know how convolutional neural networks (or CNNs) detect and classify images. But you can expand on that CNN to detect objects within that image. How…

Overcoming the problem of batch size and available GPU memory in training neural networks — In this article, we’ll talk about batch sizing issues one may encounter while training neural networks using large batch sizes and being limited by GPU memory. The problem: batch size being limited by available GPU memory When building deep learning models, we have to choose batch size — along with other hyperparameters. Batch size plays a major role in the…

Once upon a Tweet, I came across this conversation from Jeremy Howard quoting Yann LeCun about batch sizes : As this subject was something that has always been in some part of my mind since I came across the very nice learning rate finder from Fastai, I always wondered if…

Learn how Convolutional Neural Networks understand images. — In this article, we will visualize the intermediate feature representations across different CNN layers to understand what happens inside CNN’s to classify images. Prerequisites: Convolutional Neural Network Basics, Building Powerful Image Classification Convolutional Neural Network using Keras Building powerful image classification CNN using Keras

Hello everyone! Welcome back to my series on Neural Network Intuitions. Today I will be talking about an elegant concept introduced in object detectors — Anchors, how they help in detecting objects in an image and how they differ from the traditional two-stage detectors. As always let us look at…

Hey folks! It’s good to be back again :-) — It has been a while since I published my last article. In this sixth installment of my series “Neural Networks Intuitions”, I will be talking about one of the most widely used scene text detector — EAST(Efficient and Accurate Scene Text Detection) and as the name suggests it is not…

What is really going on when you use a convolutional layer vs a fully connected layer? — The design of a Neural Network is quite a difficult thing to get your head around at first. Designing a neural network involves choosing many design features like the input and output sizes of each layer, where and when to apply batch normalization layers, dropout layers, what activation functions to…

An Introduction to Transformers and Sequence-to-Sequence Learning for Machine Learning New deep learning models are introduced at an increasing rate and sometimes it’s hard to keep track of all the novelties. That said, one particular neural network model has proven to be especially effective for common natural language processing tasks. The model is called a Transformer and it makes use…

Define your own rules or let the data do the talking? — Let’s compare and contrast differential equations (DE) to data-driven approaches like machine learning (ML). In a nutshell — albeit with caveats — they can be thought of different approaches to modelling various phenomena. Do you make the rules? Or should you let your juicy data learn the rules for you? …