"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
We recommend these YouTube channels regardless of your machine learning experience, whether you have a computer science degree or just a passing interest in AI. You'll soon be on the way toward mastering the basics of AI, machine learning, and computer science in no time, through easy-to-follow demos and tutorial videos. The official Deep Learning AI YouTube channel has video tutorials from the deep learning specialization on Coursera. Artificial Intelligence -- All in One: This YouTube channel has tutorial videos related to science, technology, and artificial intelligence. Andrew Ng: Andrew Ng is a computer scientist and entrepreneur, co-founder of Google Brain, former VP & Chief Scientist at Baidu, adjunct professor at Stanford University.
As deep learning has grown in popularity over the last two decades, more and more companies and developers have created frameworks to make deep learning more accessible. Now there are so many deep learning frameworks available that the average deep learning practitioner probably isn't even aware of all of them. With so many options available, which framework should you pick? In this article, I will give you a tour of some of the most common Python deep learning frameworks and compare them in a way that allows you to decide which framework is the right one to use in your projects. I have purposely bundled these two frameworks together because the latest versions of TensorFlow are tightly integrated with Keras.
Deep learning models (aka neural nets) now power everything from self-driving cars to video recommendations on a YouTube feed, having grown very popular over the last couple of years. Despite their popularity, the technology is known to have some drawbacks, such as the deep learning "reproducibility crisis"-- as it is very common for researchers at one to be unable to recreate a set of results published by another, even on the same data set. Additionally, the steep costs of deep learning would give any company pause, as the FAANG companies have spent over $30,000 to train just a single (very) deep net. Even the largest tech companies on the planet struggle with the scale, depth, and complexity of venturing into neural nets, while the same problems are even more pronounced for smaller data science organizations as neural nets can be both time-and cost-prohibitive. Also, there is no guarantee that neural nets will be able to outperform benchmark models like logistic regression or gradient-boosted ones, as neural nets are finicky and typically require added data and engineering complexities.
In machine learning (ML), if the situation when the model does not generalize well from the training data to unseen data is called overfitting. As you might know, it is one of the trickiest obstacles in applied machine learning. The first step in tackling this problem is to actually know that your model is overfitting. That is where proper cross-validation comes in. After identifying the problem you can prevent it from happening by applying regularization or training with more data. Still, sometimes you might not have additional data to add to your initial dataset. Acquiring and labeling additional data points may also be the wrong path. Of course, in many cases, it will deliver better results, but in terms of work, it is time-consuming and expensive a lot of the time.
If you're worried about facial recognition firms or stalkers mining your online photos, a new tool called Anonymizer could help you escape their clutches. The app was created by Generated Media, a startup that provides AI-generated pictures to customers ranging from video game developers creating new characters to journalists protecting the identities of sources. The company says it built Anonymizer as "a useful way to showcase the utility of synthetic media." The system was trained on tens of thousands of photos taken in the Generated Media studio. The pictures are fed to generative adversarial networks (GANs), which create new images by pitting two neural networks against each other: a generator that creates new samples and a discriminator that examines whether they look real. The process creates a feedback loop that eventually produces lifelike profile photos.
There has been considerable recent progress in protein structure prediction using deep neural networks to infer distance constraints from amino acid residue co-evolution1–3. We investigated whether the information captured by such networks is sufficiently rich to generate new folded proteins with sequences unrelated to those of the naturally occuring proteins used in training the models. We generated random amino acid sequences, and input them into the trRosetta structure prediction network to predict starting distance maps, which as expected are quite featureless. We then carried out Monte Carlo sampling in amino acid sequence space, optimizing the contrast (KL-divergence) between the distance distributions predicted by the network and the background distribution. Optimization from different random starting points resulted in a wide range of proteins with diverse sequences and all alpha, all beta sheet, and mixed alpha-beta structures.
When you hear the term "AI," many people would think that this is a super robot that is going to destroy the world. Although this is a part of AI, that isn't what AI is. Artificial Intelligence is intelligence demonstrated by machines, which is the opposite of our intelligence, Natural Intelligence. How were we able to create an intelligence inside of code? The answer is pretty simple.
COVID-19 virus hit us hard. Warnings from Nicolas Taleb that our interconnectedness could cause wide pandemic were true. Schools are closed and most of us are working from home, spending time in isolation and trying not to spread the virus. At the moment when I am writing this, all the borders in my home country are closed, all bars and malls are closed and you can not go out after 5 PM. Apart from that, this pandemic has a huge impact on the economy.
Researchers at Google have developed a new AI tool called Chimera Painter that turns doodles into unusual creatures. This tool uses machine learning to create representation based on the user's rough sketches. Before this, Nvidia has used a similar concept with landscapes, and MIT and IBM have produced a similar idea with buildings. A high level of technical knowledge and artistic creativity is required to create art for digital video games. Game artists need to promptly iterate on ideas and develop many assets to meet tight deadlines.
Deep Learning is now powering numerous AI technologies in daily life, and convolutional neural networks (CNNs) can apply complex treatments to images at high speeds. At Unity, we aim to propose seamless integration of CNN inference in the 3D rendering pipeline. Unity Labs, therefore, works on improving state-of-the-art research and developing an efficient neural inference engine called Barracuda. Deep learning has long been confined to supercomputers and offline computation, but their usability at real-time on consumer hardware is fast approaching thanks to ever-increasing compute capability. With Barracuda, Unity Labs hopes to accelerate its arrival in creators' hands.