First Lenovo learned to make cheap gaming laptops. Now it's learned to make cheap gaming laptops that also look good. I've spent the last few weeks with the Lenovo Legion Y7000, a stopgap release that sticks with Nvidia's GTX 10-series graphics cards as Lenovo's flagship Y500 and Y700 models start to transition to higher-end (and higher-priced) RTX 20-series GPUs. Is it a performance monster? The type of laptop you buy to impress your friends?

After being kicked to the curb in favor of a new "RTX" brand that signifies the inclusion of dedicated RT and tensor cores that enable real-time ray tracing and AI-enhanced gaming, Nvidia's tried-and-true mainstay returns for the release of the GeForce GTX 1660 Ti graphics card. Yes, that means this $280-plus GPU lacks the cutting-edge capabilities of its bigger siblings, like the GeForce RTX 2060. But by ditching all the extra hardware, Nvidia was able to focus the GTX 1660 Ti's efforts on just plain kicking ass in games. The GeForce GTX 1660 Ti delivers outstanding 1080p and solid 1440p gaming performance on a par with last-gen's $380 GTX 1070, without the massive price increase witnessed in its RTX-laden cousins--it's only $20 more than what the GTX 1060 launched at. This card beats the snot out of AMD's Radeon RX 590, even though its starting price is $10 lower.

The Dell G3 15 gaming laptop delivers solid performance in a package that's a little less than an inch thick. It's very affordable, too, at just $850 from Dell (at this writing) for the Model 3579 configuration we tested. The G3 15 has its downsides, including a Full-HD display that isn't as bright as we'd like, frame rates that struggle to reach 60 fps on top-tier games, and a weight exceeding five pounds (although it's not as massive as some gaming laptops). But when we compared it to the Acer Nitro 5, another budget gaming laptop we like, with an even lower price point, there was no contest. The G3 15 posted stronger benchmarks and battery life.

Deep learning is a field with intense computational requirements and the choice of your GPU will fundamentally determine your deep learning experience. With no GPU this might look like months of waiting for an experiment to finish, or running an experiment for a day or more only to see that the chosen parameters were off. With a good, solid GPU, one can quickly iterate over deep learning networks, and run experiments in days instead of months, hours instead of days, minutes instead of hours. So making the right choice when it comes to buying a GPU is critical. So how do you select the GPU which is right for you? This blog post will delve into that question and will lend you advice which will help you to make choice that is right for you. TL;DR Having a fast GPU is a very important aspect when one begins to learn deep learning as this allows for rapid gain in practical experience which is key to building the expertise with which you will be able to apply deep learning to new problems.

Deep learning is a field with intense computational requirements and the choice of your GPU will fundamentally determine your deep learning experience. With no GPU this might look like months of waiting for an experiment to finish, or running an experiment for a day or more only to see that the chosen parameters were off. With a good, solid GPU, one can quickly iterate over deep learning networks, and run experiments in days instead of months, hours instead of days, minutes instead of hours. So making the right choice when it comes to buying a GPU is critical. So how do you select the GPU which is right for you? This blog post will delve into that question and will lend you advice which will help you to make choice that is right for you. TL;DR Having a fast GPU is a very important aspect when one begins to learn deep learning as this allows for rapid gain in practical experience which is key to building the expertise with which you will be able to apply deep learning to new problems.

NVIDIA is coming off the best year in the company's history thanks to the growth of AI and NVIDIA's position in that market. However, if you look at NVIDIA's latest earnings, over 60% of their revenue still comes from gaming. NVIDIA's gaming division had a fantastic quarter with 67% growth which I attribute a lot to the transformation of GeForce from a graphics chip to a full-blown gaming platform, competing directly with consoles, handhelds and mobile. The GeForce platform, combines hardware, software and services and NVIDIA designed it to enable the best PC gaming experience, everywhere. With the Game Developer's Conference looming large next week it's a good time to take a closer look at the three pillars of this gaming platform.

Nvidia has been a leader in producing the technology behind high-quality graphics for years, but the company is now betting on a different future. With the rapid advances in self-driving vehicles, warehouse robots, diagnostic assistants, and speech and facial recognition, there's plenty of reasons for companies to be excited about deep-learning-based artificial intelligence (AI). Nvidia is one such technology firm that is extremely bullish in this space. In a recent conversation with CXOtoday, Vishal Dhupar, Managing Director-South Asia, Nvidia said that the company is moving ahead with its AI-focused hardware, software, and solutions for the enterprise. As such data scientists in both industry and academia have been using graphics processing units (GPUs) for machine learning to make groundbreaking innovations across a variety of applications including image classification, video analytics, speech recognition and natural language processing or NLP. In particular, Deep Learning, the use of sophisticated, multi-level "deep" neural networks to create systems that can perform feature detection from massive amounts of unlabeled training data, is an area where Nvidia is significantly investing in recent quarters.