The crux of deep learning algorithms. Neural networks mimic the way that biological neurons signal to one another in the human brain.
While the vast majority of developments in AI technology have centered around practical solutions such as self-driving cars and facial recognition, there's a growing number of artists using AI systems to develop new ideas for artistic projects and generate entirely unique pieces of work.
Machine Learning (ML) in its literal terms implies, writing algorithms to help Machines learn better than human. ML is an aspect of Artificial Intelligence (AI) that deals with the development of a mathematical model which is fed with training data to identify patterns in that data and produce an output.
Whether you’re a beginner looking for introductory articles or an intermediate looking for datasets or papers about new AI models, this list of machine learning resources has something for everyone interested in or working in data science. In this article, we will introduce guides, papers, tools and datasets for both computer vision and natural language processing.
Quick introduction of LightOn, aka how photonic processors will save machine learning
This article looks at the Best Keras Datasets for Building and Training Deep Learning Models, accessible to developers and researchers worldwide.
Amazon introduced the DeepComposer music synthesizer and the eponymous cloud-based music creation service based on generative adversarial neural networks. Using them, the user can set the main melody on the synthesizer and get a full song, in which the original part is supplemented with drums, guitar and other instruments.
We may be on the verge of a deeper connection with our computers. What used to be mere science fiction has become the very stuff of today’s news, with Elon Musk on the headlines.
For newbies, machine learning algorithms may seem too boring and complicated. Well, to some extent, this is true. In most cases, you stumble upon a few-page description for each algorithm and yes, it’s hard to find time and energy to deal with each and every detail. However, if you truly, madly, deeply want to be an ML-expert, you have to brush up your knowledge regarding it and there is no other way to be. But relax, today I will try to simplify this task and explain core principles of 10 most common algorithms in simple words (each includes a brief description, guides, and useful links). So, breath in, breath out, and let’s get started!
There are still areas where AI lacks and causes problems and frustration to end-users, and these areas pose a great challenge for researchers right now.
There is a trend in neural networks that has existed since the beginning of the deep learning revolution which is succinctly captured in one word: scale.
There is a common belief among techies these days that with the arrival of AI and algorithms, professions such as those that of artists are becoming extinct. This is a misconception.
The machines have been trying to learn to recognize and identify the photos they have seen for years. In 2013, it succeeded in reaching the human level. Machine learning systems have provided simple output from a complex input. It can detect almost all details of a photos and display users exactly want they want.
Some days ago, I read an article on arXiv from Vitaly Vanchurin about ‘The world as a neural network’.
In machine learning, each type of artificial neural network is tailored to certain tasks. This article will introduce two types of neural networks: convolutional neural networks (CNN) and recurrent neural networks (RNN). Using popular Youtube videos and visual aids, we will explain the difference between CNN and RNN and how they are used in computer vision and natural language processing.
The list of industries and use cases where NeRF technology is the most awaited.
How does AI help to fight fake news? The use case that we created during the war.
I built a simple Neural Network using Python that outputs a target number given a specific input number.
RNN is one of the popular neural networks that is commonly used to solve natural language processing tasks.
I’ve been working with massive data sets for several years at companies like Facebook to analyze and address operational challenges, from inventory to customer lifetime value. But I hadn’t worked yet on something this ambitious.
Developers of AI systems can create complex algorithms for a wide range of use cases, including in investing and trading.
This is an example of an audio data analysis by 2D CNN
The plethora of knowledge involved in Machine Learning is the most fabulous thing about the subject. The theoretical and coding balance requires a steady and disciplined approach. In this five series tutorial, we saw CNNs, where we saw various approaches to different scenarios, and then worked on word embeddings, which was our gateway to Natural Language Processing, and finally ended with Support Vector Machines(SVMs) which were as powerful as Artificial Neural Networks, during the time of their inception.
Major companies using AI and machine learning now use federated learning – a form of machine learning that trains algorithms on a distributed set of devices.
Most word embeddings used are glaringly sexist, let us look at some ways to de-bias such embeddings.
…And where is the blockchain in it?
Style transfer is a computer vision-based technique combined with image processing. Learn about style transfer with Tensorflow, a prominent framework in AI & ML
In a conversation with HackerNoon CEO, David Smooke, he identified artificial intelligence as an area of technology in which he anticipates vast growth. He pointed out, somewhat cheekily, that it seems like AI could be further along in figuring out how to alleviate some of our most basic electronic tasks—coordinating and scheduling meetings, for instance. This got me reflecting on the state of artificial intelligence. And mostly why my targeted ads suck so much...
Digital forensic plays a major role in forensic science. It’s a combination of people, process, technology, and law.
Human Visual System is a marvel of the world. People can readily recognise digits. But it is not as simple as it looks like. The human brain has a million neurons and billions of connections between them, which makes this exceptionally complex task of image processing easier. People can effortlessly recognize digits.
Today, with open source machine learning software libraries such as TensorFlow, Keras or PyTorch we can create neural network, even with a high structural complexity, with just a few lines of code. Having said that, the Math behind neural networks is still a mystery to some of us and having the Math knowledge behind neural networks and deep learning can help us understand what’s happening inside a neural network. It is also helpful in architecture selection, fine-tuning of Deep Learning models, hyperparameters tuning and optimization.
Retraining Machine Learning Model, Model Drift, Different ways to identify model drift, Performance Degradation
Classify open/closed eyes using Variational Autoencoders (VAE).
Recently, I attended a virtual conference on the use of neuro technology and BCI (Brain Computer Interfaces or BMI, Brain Machine Interfaces) in gaming, put on by NeurotechX.
The purpose of this post is to implement and understand Google Deepmind’s paper DRAW: A Recurrent Neural Network For Image Generation. The code is based on the work of Eric Jang, who in his original code was able to achieve the implementation in only 158 lines of Python code.
The FPGA market continues to boom. According to the global forecast, over the next few years, its CAGR will be at an average of 8.6%. But the most interesting are new appliances of the tech, which are sometimes more akin to science fiction than to real life.
Karate Club is an unsupervised machine learning extension library for the NetworkX Python package. See the documentation here.
Computer vision techniques are developed to enable computers to “see” and draw analysis from digital images or streaming videos.
Universal Approximation Theorem says that Feed-Forward Neural Network (also known as Multi-layered Network of Neurons) can act as powerful approximation to learn the non-linear relationship between the input and output. But the problem with the Feed-Forward Neural Network is that the network is prone to over-fitting due to the presence of many parameters within the network to learn.
This is actually an assignment from Jeremy Howard’s fast.ai course, lesson 5. I’ve showcased how easy it is to build a Convolutional Neural Networks from scratch using PyTorch. Today, let’s try to delve down even deeper and see if we could write our own nn.Linear module. Why waste your time writing your own PyTorch module while it’s already been written by the devs over at Facebook?
Artificial intelligence (AI) has reached a tipping point, leveraging the massive pools of data gathered by every app, website, and device in our lives to make increasingly sophisticated decisions on our behalf. AI is at work in our inboxes sorting and blocking emails. It takes and processes our increasingly complex requests through voice assistants. It supplements customer support through chatbots, and heavily automates complex processes to reduce the workload for knowledge workers. Evidently, devices can adapt on the fly to human behavior.
August 29 might possibly be remembered as the day when technology took over the limitations of human life. In one of the most appealing events of technological history, the tech entrepreneur Elon Musk presented a live demonstration of his brain hacking device LINK V0.9.
Tips and tricks to build an autonomous grasping Kuka robot
Transformer models have become the defacto standard for NLP tasks. As an example, I’m sure you’ve already seen the awesome GPT3 Transformer demos and articles detailing how much time and money it took to train.
Why are GPT-3 and all the other transformer models so exciting? Let's find out!
In August 2019, a group of researchers from lululab Inc propose the state-of-the-art concept using a semantic segmentation method to detect the most common facial skin problems accurately. The work is accepted to ICCV 2019 Workshop.
Convolutional Neural Networks became really popular after 2010 because they outperformed any other network architecture on visual data, but the concept behind CNN is not new. In fact, it is very much inspired by the human visual system. In this article, I aim to explain in very details how researchers came up with the idea of CNN, how they are structured, how the math behind them works and what techniques are applied to improve their performance.
"AI isn't just creating new kinds of art; it's creating new kinds of artists.” - Douglas Eck, Magenta Project
In this article, I would like to share my own experience of developing a smart camera for cyclists with an advanced computer vision algorithm
While improvements in AI and Deep Learning move forward at an ever increasingly rapid rate, people have started to ask questions. Questions about jobs being made obsolete, questions about the inherent biases programmed into the neural networks, questions about whether or not AI will eventually consider humans as dead-weight and unnecessary to achieve the goals they've been tasked programmed with.
Sponsor: Scraper API's 5 Tips for Web Scraping
Data drift occurs when a model sees production data that differs from its training data. If a model is asked to make a prediction based upon drifted data
Introducing PeerVest: A free ML app to help you pick the best loan pool on a risk-reward basis
Recurrent Neural Networks (RNN) have played a major role in sequence modeling in Natural Language Processing (NLP) . Let’s see what are the pros and cons of RNN
Luiz Guilherme Fonseca Rosa from Brazil has been nominated for a 2020 Noonie as Hacker Noon Contributor of the Year - ALGORITHMS. The Noonies are Hacker Noon’s way of getting to know — from a community perspective — what matters in tech today. So, we asked our Noonie Nominees to tell us. Here’s what Luiz had to share.
— All the images (plots) are generated and modified by the Author.
Artificial intelligence can mean a lot of things. It’s been used as a catch-all for various disciplines in computer science including robotics, natural language processing, or artificial neural networks. That’s because, generally speaking, when we talk about artificial intelligence we’re always talking about the simulation of human thought by a mechanical process.
Keras is a deep learning framework for Python for building neural networks and training them on datasets. It can leverage GPUs and CPUs for training algorithms.
Before you can code neural networks in any language or toolkit, first, you must understand what they are.
Russian doomer neural network creates paintings and music videos. Tutorial. Stylegan2 was trained on thousands of images of soviet architecture.
How to use edge machine learning in browser for privacy-first location detection. Turn your users invisible while building location-based websites.
Neural networks are rapidly improving thanks to advancements in computational power.
Every day we are facing AI and neural network in some ways: from common phone use through face detection, speech or image recognition to more sophisticated — self-driving cars, gene-disease predictions, etc. We think it is time to finally sort out what AI consists of, what neural network is and how it works.
Year of the Graph Newsletter, September 2019
Artificial neural networks mimic the functioning of neurons in the human brain. They can learn from their original training and future runs.
COVID-19 has impacted every other industry and has made people adopt newer norms. The traditional translation industry is no different. Several disruptions have been introduced to keep things moving, thanks to Big data and machine translation technologies that have enabled the world to do business as usual.
By 2020, the total number of Internet-connected devices will be between 25-50 billion.
When a human sees an object, certain neurons in our brain’s visual cortex light up with activity, but when we take hallucinogenic drugs, these drugs overwhelm our serotonin receptors and lead to the distorted visual perception of colours and shapes. Similarly, deep neural networks that are modelled on structures in our brain, stores data in huge tables of numeric coefficients, which defy direct human comprehension. But when these neural network’s activation is overstimulated (virtual drugs), we get phenomenons like neural dreams and neural hallucinations. Dreams are the mental conjectures that are produced by our brain when the perceptual apparatus shuts down, whereas hallucinations are produced when this perceptual apparatus becomes hyperactive. In this blog, we will discuss how this phenomenon of hallucination in neural networks can be utilized to perform the task of image inpainting.
While building a machine learning model, data scaling in machine learning is the most significant element through data pre-processing. Scaling may recognize the difference between a model of poor machine learning and a stronger one.
The Age of Exciting Opportunities
Pretrained Artificial Neural Networks used to work like a Blackbox: You hand them an input and they predict an output with a certain probability — but without us knowing the internal processes of how they came up with their prediction. A Neural Network to recognize images usually consists of around 20 neuron layers, trained with millions of images to tweak the network parameters to give high quality classifications.
In this article, we are going to learn about the grayscale image, colour image and the process of convolution.
You can apply any design, lighting, or graphics style to your 4K image in real-time using this new machine learning-based approach
Most of us in data science have seen a lot of AI-generated people in recent times, whether it be in papers, blogs, or videos. We’ve reached a stage where it’s becoming increasingly difficult to distinguish between actual human faces and faces generated by artificial intelligence. However, with the current available machine learning toolkits, creating these images yourself is not as difficult as you might think.
By: Comet.ml and Niko Laskaris, customer facing data scientist, Comet.ml
CDC officially recommends wearing face masks (even though not everyone complies). Meanwhile, governments in European countries like Spain, Ukraine, or certain regions in Italy require everyone, big or small, to wear masks all the time, when shopping, walking a dog, or plainly going outside. Breaking the requirements could result in a hefty fine.
In case you missed it, I built a neural network to predict loan risk using a public dataset from LendingClub. Then I built a public API to serve the model’s predictions. That’s nice and all, but… how good is my model?
In this article, I will share with you some useful tips and guidelines that you can use to better build better deep learning models.
Idea / inspiration
PyTorch has sort of became one of the de facto standard for creating Neural Networks now, and I love its interface. Yet, it is somehow a little difficult for beginners to get a hold of.
A Step-by-Step Guide (With a Healthy Dose of Data Cleaning)
