Over the years, accelerating neural networks with quantization has been widely studied. Unfortunately, prior efforts with diverse precisions (e.g., 1-bit weights and 2-bit activations) are usually restricted by limited precision support on GPUs (e.g., int1 and int4). To break such restrictions, we introduce the first Arbitrary Precision Neural Network framework (APNN-TC) to fully exploit quantization benefits on Ampere GPU Tensor Cores. Specifically, APNN-TC first incorporates a novel emulation algorithm to support arbitrary short bit-width computation with int1 compute primitives and XOR/AND Boolean operations. Second, APNN-TC integrates arbitrary precision layer designs to efficiently map our emulation algorithm to Tensor Cores with novel batching strategies and specialized memory organization. Third, APNN-TC embodies a novel arbitrary precision NN design to minimize memory access across layers and further improve performance. Extensive evaluations show that APNN-TC can achieve significant speedup over CUTLASS kernels and various NN models, such as ResNet and VGG.

The Recurrent Neural Network (RNN) has been used to obtain state-of-the-art results in sequence modeling. This includes time series analysis, forecasting and natural language processing (NLP). Learn about why RNNs beat old-school machine learning algorithms like Hidden Markov Models. The basics of machine learning and neurons (just a review to get you warmed up!) Neural networks for classification and regression (just a review to get you warmed up!) How to predict stock prices and stock returns with LSTMs in Tensorflow 2 (hint: it's not what you think!) All of the materials required for this course can be downloaded and installed for FREE.

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 use, etc. In this article, I want to discuss what is really going on behind fully connected layers and convolutions, and how the output size of convolutional layers can be calculated. Deep learning is a field of research that has skyrocketed in the past few years with the increase in computational power and advances in the architecture of models. Two kinds of networks you'll often hear when reading about deep learning are fully connected neural nets (FCNN), and convolutional neural nets (CNNs).

Venture Capitalists are hoping to find the next superstar tech unicorn, AI startup founders dreaming of creating the next unicorn, and corporates adopting AI need to consider their data growth strategy in order to be able to scale their AI-enabled services or products. The past decade has been one of explosive growth in digital data and AI capabilities across the digital media and e-commerce space. And it is no accident that the strongest AI capabilities reside in the Tech majors. The author argues that there will be no AI winter in the 2020s as there was in 1974 and 1987 as the internet (social media and e-commerce) are so dependent upon AI capabilities and so too with being the Metaverse, and the era of 5G enabled Edge Computing with the Internet of Things (IoT). Furthermore, the following infographics illustrate how many people globally use social media and hence how central these channels have become to the everyday lives of people. Likewise, the size of the e-commerce market is vast. Although the era of standalone 5G networks may enable a window of opportunity for a new wave of consumer-facing applications in the business to consumer (B2C) in relation to e-commerce and perhaps even new digital media platforms that may challenge the current incumbents, after all the arrival of 4G provided a window for the likes of Airbnb, Uber, and leading social media platforms such as Facebook, Instagram, etc. to scale.

At just one year old, a baby is more dexterous than a robot. Sure, machines can do more than just pick up and put down objects, but we're not quite there as far as replicating a natural pull toward exploratory or sophisticated dexterous manipulation goes. Artificial intelligence firm OpenAI gave it a try with Dactyl (meaning "finger," from the Greek word "daktylos"), using their humanoid robot hand to solve a Rubik's cube with software that's a step toward more general AI, and a step away from the common single-task mentality. DeepMind created "RGB-Stacking," a vision-based system that challenges a robot to learn how to grab items and stack them. Scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), in the ever-present quest to get machines to replicate human abilities, created a framework that's more scaled up: a system that can reorient over 2,000 different objects, with the robotic hand facing both upwards and downwards.

Despite being quite effective in a variety of tasks across industries, deep learning is constantly evolving, proposing new neural network (NN) architectures, deep learning (DL) tasks, and even brand new concepts of the next generation of NNs, such as the Spiking Neural Network (SNN). SNN was introduced by the researchers at Heidelberg University and the University of Bern developing as a fast and energy-efficient technique for computing using spiking neuromorphic substrates. In this article, we will mostly discuss Spiking Neural Network as a variant of neural network. We will also try to understand how is it different from the traditional neural networks. Below is a list of the important topics to be tackled.

Artificial intelligence (AI) has been the hot topic of discussion for the past few years as people finally started believing in its value about 70 decades after its inception. The first work related to the field of AI was done in 1943 with Walter Pitts and Warren McCullough as they proposed the first artificial neuron model, then came the well-known Turing test in 1950. Although the term'Artificial Intelligence' was coined over a decade later in 1956 by John McCarthy. The field went through many different periods with barely anyone wanting to invest in this crazy idea of making machines intelligent. Funding stopped and started again and again but finally boomed again when huge companies like Facebook, Netflix, and Twitter started using it.

To begin with, let's dig into the basics of Machine Learning and Deep Learning. ML is a subset of artificial intelligence that serves to provide the machines with the ability to automatically learn and act based on previous experience. Machine learning involves the "implementation" of different algorithms including neural networks that help to solve the problems. DL, in its turn, is a subset of machine learning. Deep learning uses the only algorithm-neural network similar to the human neural system to data mining and analyze various factors.

Honeywell's global businesses have a strong legacy in India, built over the last eight decades. It delivers industry-specific solutions like aerospace products and services, control technologies for buildings and performance materials. Currently, it employs close to 13,000 people across different locations, including Bengaluru, Chennai, Gurugram, Dehradun, Hyderabad, Madurai, and Pune. At the centre of everything that Honeywell does, lies its data science team, which adds value by transforming products and services. They work on a wide array of key functional areas, including product development, advanced image, speech and audio recognition, text and video analytics, regression analytics and face detection.

Automated speech-recognition technology has become more common with the popularity of virtual assistants like Siri, but many of these systems only perform well with the most widely spoken of the world's roughly 7,000 languages. Because these systems largely don't exist for less common languages, the millions of people who speak them are cut off from many technologies that rely on speech, from smart home devices to assistive technologies and translation services. Recent advances have enabled machine learning models that can learn the world's uncommon languages, which lack the large amount of transcribed speech needed to train algorithms. However, these solutions are often too complex and expensive to be applied widely. Researchers at MIT and elsewhere have now tackled this problem by developing a simple technique that reduces the complexity of an advanced speech-learning model, enabling it to run more efficiently and achieve higher performance.