Accelerated Automatic Differentiation with JAX is a great choice, but how does it compare to Autograd, Tensorflow, and PyTorch? Learn more... | Continue reading
Learn about Autograd for deep learning, along with a tutorial to dive into this alternative machine learning library. | Continue reading
Read our latest blog post that explores OpenAI's massive GPT-3 model, the newest breakthrough in language generation made up of 175 billion parameters. | Continue reading
Transformer architectures have upended language tasks in machine learning and artificial intelligence. Here's the information & resources you need to know. | Continue reading
Deep learning has caused explosive growth in the industry, but is it the future of finance? | Continue reading
Recurrent Neural Networks (RNN) are a class of Artificial Neural Networks that can process a sequence of inputs in deep learning and retain its state. | Continue reading
Hopefully, you had a chance to attend CVPR Virtual this year, and even if you did, there were a lot of interesting things you may have missed. | Continue reading
NVIDIA just released CUDA 11 to accelerate HPC, genomics, 5G, rendering, deep learning, data analytics, data science, robotics, and more. | Continue reading
Natural language processing (NLP) with deep neural networks follows deep learning for the vision of the future. Here's the progress being made. | Continue reading
Since 2017, PyTorch has become a highly popular and widely used Deep Learning (DL) framework. Here's the most important fundamentals you need to know. | Continue reading
Check out our new Deep Learning Workstations featuring the new 3rd Generation AMD Ryzen Threadripper CPUs. They are pretty awesome to say the least. | Continue reading
Artificial intelligence researchers have teams devoted to video games: Deepmind with Starcraft II & OpenAI on Dota 2. Here's Deepmind's gaming streak. | Continue reading
In drug discovery, deep learning can be used to create a vast cellular models of disease. Other researchers skip over assays and do virtual drug screening. | Continue reading
PyTorch is a popular, open source deep learning platform used for easily writing neural network layers in Python. Check out the newest release v1.5.0! | Continue reading
LAMMPS is a highly flexible and scalable software suite for molecular dynamics. Learn more about its newest patch release on 15 April 2020! | Continue reading
What kind of information is accurate about deep reinforcement learning? Well here's all you need to know about deep reinforcement learning. Learn more! | Continue reading
Ansys structural mechanics products have long supported parallel processing, and with it, leading to fast solution turnaround times. | Continue reading
Deep learning is making major inroads in drug discovery in the pharmaceutical industry. Here’s how deep learning can disrupt established industries. | Continue reading
Random Forest is a better choice than neural networks because of a few main reasons. Here’s what you need to know for machine learning vs deep learning. | Continue reading
Reinforcement learning (RL) is important for decision making, and here’s what you need to know about computational efficiency and sample efficiency. | Continue reading
From Saturn Cloud to AI Google Cloud, and Google Colaboratory to MatrixDS, here's the 4 best Jupyter notebook environments for deep learning. Learn more! | Continue reading
GROMACS 2020 is now available. Read our blog to learn more about the new features, bug fixes and performance improvements. | Continue reading
Did you know deep learning and machine learning are used for cosmology & the study of the universe? We rate these various projects on hype and impact. | Continue reading
The following documentation is intended to explain the procedure for deploying Dynamic NFS Provisioning in Kubernetes. Read our blog to learn more! | Continue reading
The following tutorial is intended to explain the procedure for deploying VirtualBox on a System running Centos 7. Read our blog to learn more! | Continue reading
or testing we used an Exxact Valence Workstation was fitted with 4x Quadro RTX 6000 GPUs with NVLink, for training BERT Large on GPU. | Continue reading
The following tutorial is intended to explain the procedure for deploying Prometheus and Grafana in a Kubernetes Cluster. | Continue reading
For this post, we measured fine-tuning performance (training and inference) for the BERT implementaiton of TensorFlow on NVIDIA Quadro RTX 8000 GPUs. | Continue reading
The following tutorial is intended to explain the procedure for deploying Kubernetes using Vagrant. Read our blog to learn more! | Continue reading
Solving Rubik’s Cubes in Pursuit of Generalized Robotic Manipulation. An impossible scramble. This scramble is impossible to solve using any known Rubik’s Cube algorithms without employing disassembly methods. Cube state... | Continue reading
Deep Learning is gaining more momentum and notoriety. The following deep learning courses are great for knowledge on the new wave of Deep Learning and AI. | Continue reading
You want to create the best LSTM for your project that is optimized and bug-free. Here’s 5 types of LSTM Neural Networks and what to do with them. | Continue reading
Here’s what you need to know about performance measure optimization for classification and regression models in machine learning. Check it out. | Continue reading
Generative Adversarial Networks, or GANs for short, are one of the most exciting areas of deep learning to arise in the last 10 years. Learn more now. | Continue reading
Transfer learning wins where deep learning fails. Transfer learning can be setup in minutes using a MNIST dataset. Here's how… | Continue reading
GROMACS can take advantage of the higher core count of the 2nd Gen EPYC 7742, with an impressive ~156% average performance boost over the 1st gen EPYC 7601 | Continue reading
Movement of materials, goods, and parts is at the heart of any manufacturing system. Deep learning offers major improvements in the field of manufacturing. | Continue reading
Recently, BeeGFS has grabbed the attention of the research community for many reasons including performance, scalability ease of use, and cost. | Continue reading
With advancements in automation, compute power, and visual technology, the scope and complexity of datasets used in typical single particle cryo-EM have grown substantially. | Continue reading
Data is a driving force in our society. Privacy preserving deep learning tools like PySyft and TF-Encrypted are becoming more popular. Learn more. | Continue reading
Deep Learning is everywhere, if your company hasn't considered using deep learning, you may be feeling like you're missing out. Stay ahead of the curve! | Continue reading
Synthetic datasets provide an excellent testing ground for judging and comparing ML algorithms. Learn how to generate and use such datasets for ML experiments using Scikit Learn and other tools. | Continue reading
We run a typical machine learning workflow with Scikit-Learn and with MLR. Which is better for machine learning? Follow along as we break it down. | Continue reading
For this post, we examine deep learning benchmarks for TensorFlow on an Exxact TensorEX HGX-2 Server has 16 NVIDIA Tesla V100 GPUs. | Continue reading
Megatron is a 8.3 billion parameter transformer language model with trained on 512 V100 GPUs, making it the largest transformer model ever trained. | Continue reading
Considering learning a new Python framework? You no longer have to switch to PyTorch--Tensorflow 2.0 is coming with upgrades and backwards compatibility. | Continue reading
The long awaited 'AMD EPYC Rome' has finally arrived, Announced August 7th, 2019, AMD's New 7002 Series server processors feature best in class performance. | Continue reading
In our blog article, we discuss the differences between popular deep learning frameworks TensorFlow, PyTorch and Keras for NLP. Read more now! | Continue reading