The Localization Problem (LP) is a glaring dark cloud hanging over the state of affairs in applied deep learning. And acknowledging this problem, I believe, will enable us make better use of applied AI and expand our knowledge in how the business market will form. Defining LP: There is a limit to how much large centralized language models can generalize at scale given: 1) that different users inherently have varying definitions of ground-truths due to inter-dependencies to their unique real-world environment and 2) depending whether or not model performance is mission-critical. In other words, in certain conditions, in order for a model to be optimized for accuracy for a given user, the model needs to be "localized" to its user's ground truth in their data assuming that a model can't afford to be wrong too many times. Example: Imagine there is a kazillion parameter encoder transformer called Hal9000.

Word Embeddings is the most fundamental concept in Deep Natural Language Processing. And word2vec is one of the earliest algorithms used to train word embeddings. In this post, I want to go deeper into the first paper on word2vec -- Efficient Estimation of Word Representations in Vector Space (2013), which as of now has 24k citations, and this number is still growing. I am attaching my Github project with word2vec training. We will go through it in this post.

I receive questions on content that explains machine learning, statistics or data science on a daily basis. I usually learn from books, so I wanted to write a post about the resources I used, some of them e-books. I've finished some of the books, and some of them are in queue. I've categorized the books according to topic it covers, and it's important to note that each book suits you according to your background, e.g. Without further ado, let's review!

This dataset contains 8732 labeled sound excerpts ( 4s) of urban sounds from 10 classes: air_conditioner, car_horn, children_playing, dog_bark, drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music. The classes are drawn from the urban sound taxonomy. All excerpts are taken from field recordings uploaded to www.freesound.org. The files are pre-sorted into ten folds (folders named fold1-fold10) to help in the reproduction of and comparison with the automatic classification results reported in the article above. AUDIO FILES INCLUDED 8732 audio files of urban sounds (see description above) in WAV format.

All about Agile, Ansible, DevOps, Docker, EXIN, Git, ICT, Jenkins, Kubernetes, Puppet, Selenium, Python, etc Today, We'll look after something very big that you might have never seen or rarely seen on the web. We have researched for more than 35 days to find out all the cheatsheets on machine learning, deep learning, data mining, neural networks, big data, artificial intelligence, python, Tensorflow, scikit-learn, etc from all over the web. To make it easy for all learners, We have zipped over 100 machine learning cheat sheet, data science cheat sheet, artificial intelligence cheat sheets and more. You can also download the pdf version of this cheat sheets (links are already provided below every images). How do you discover content from around the web related to AI, ML and Data Science?

Growing up as a child, we all at some point in time must have wanted to learn to play musical instruments, be it piano, violin, guitar, ukelele, drums, or saxophone. However, I was not good at playing any of the instruments, and playing a musical instrument remained a dream for me until now. So I decided to make an Artificial Intelligence model which could generate unique and unlimited music for me. Yes! Now, I would never get tired of hearing the same songs again. The Artificial Intelligence model would generate each time a unique and melodious song that I can listen to.

To assess whether octree representation and octree-based convolutional neural networks (CNNs) improve segmentation accuracy of three-dimensional images. Cardiac CT angiographic examinations from 100 patients (mean age, 67 years 17 [standard deviation]; 60 men) performed between June 2012 and June 2018 with semantic segmentations of the left ventricular (LV) and left atrial (LA) blood pools at the end-diastolic and end-systolic cardiac phases were retrospectively evaluated. Image quality (root mean square error [RMSE]) and segmentation fidelity (global Dice and border Dice coefficients) metrics of the octree representation were compared with spatial downsampling for a range of memory footprints. Fivefold cross-validation was used to train an octree-based CNN and CNNs with spatial downsampling at four levels of image compression or spatial downsampling. The semantic segmentation performance of octree-based CNN (OctNet) was compared with the performance of U-Nets with spatial downsampling.

AI is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.

In my previous blogs I have talked about different types of Artificial Neural Networks (ANN) like Perceptron, Restricted Boltzmann Machine (RBM). In this blog I am going to discuss another type of Neural Network called Autoencoders. But we can say autoencoders are self-supervised. As we do not need separate target data, they can use the input labels as the target label. Autoencoders are one type of feedforward Neural Network.

Stock price prediction is definitely not an easy task as there are many factors that need to be taken into consideration. Overall market conditions, competitors' performance, new product releases, temper of global relations are just some key factors that have potential to increase or decrease stock prices. In addition to these, unexpected events may occur such as the corona virus situation we are currently experiencing. The factors we have listed up to now are hard to predict. Thus, we will put them aside throughout this post.