Fake news is a major concern in our society right now. It has gone hand-in-hand with the rise of the data-driven era – not a coincidence when you consider the sheer volume of data we are generating every second! Fake news is such a widespread issue that even the world's leading dictionaries are trying to combat it in their own way. So what role has Machine Learning played in this? I'm sure you must have heard about a machine learning technique that generates fake videos mimicking famous personalities. Similarly, Natural Language Processing (NLP) techniques are being used to generate fake articles – a concept called "Neural Fake News". I've been working in the Natural Language Processing (NLP) space for the last few years and while I love the pace at which breakthroughs are happening, I'm also deeply concerned about the way these NLP frameworks are being used to create and spread false information.

Data is everywhere, and with data being so prevalent in all aspects of the world, it was only a matter of time that data, along with machine learning and artificial intelligence, would help basketball players hone their skills and better their game. The new technologies can specifically help players improve their shooting, as well as help coaches draw up plays for their team. One major technology that is aiding basketball players is Noah Basketball. It tracks shots as well as produces data on those shots, which help improve both player and team shooting statistics. It gives players data on their shot -- such as the arc and trajectory of their shot -- and gives coaches access to data that allows them to figure out from where, how often and how well their players shoot on the court.

NATICK, Mass., July 15, 2020 -- MathWorks announced that a comprehensive "Deep Learning with MATLAB" course is now available, developed in …

Machine learning is enhancing at a promising rate. It becomes challenging when it comes to supporting game design and development and personalization of the gaming experience based on the data collected related to the player's behavior. Whereas, AI developers are trying to use AI to make the game look and feel more realistic where players can interact naturally with other players and the environment. While on the other hand, the developer can achieve the intended player experience. The motive is to enhance an individual player's experience during the game, and even after.

That's according to researchers at the Massachusetts Institute of Technology, Underwood International College, and the University of Brasilia, who found in a recent study that progress in deep learning has been "strongly reliant" on increases in compute. It's their assertion that continued progress will require "dramatically" more computationally efficient deep learning methods, either through changes to existing techniques or via new as-yet-undiscovered methods. "We show deep learning is not computationally expensive by accident, but by design. The same flexibility that makes it excellent at modeling diverse phenomena and outperforming expert models also makes it dramatically more computationally expensive," the coauthors wrote. "Despite this, we find that the actual computational burden of deep learning models is scaling more rapidly than (known) lower bounds from theory, suggesting that substantial improvements might be possible."

All of the below papers are free to access and cover a range of topics from Hypergradients to modeling yield response for CNNs. Each expert also included a reason as to why the paper was picked as well as a short bio. We spoke to Jeff back in January, and at that time, he couldn't pick just one paper as a must-read, so we let him pick two. This paper unpacks two key talking points, the limitations of sparse training data, and also if recurrent networks can support meta-learning in a fully supervised context. These points are addressed in seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.

Chest radiography is an important diagnostic tool for chest-related diseases. Medical imaging research is currently embracing the automatic detection techniques used in computer vision. Over the past decade, Deep Learning techniques have shown an enormous breakthrough in the field of medical diagnostics. Various automated systems have been proposed for the rapid detection of pneumonia on chest x-rays images Although such detection algorithms are many and varied, they have not been summarized into a review that would assist practitioners in selecting the best methods from a real-time perspective, perceiving the available datasets, and understanding the currently achieved results in this domain. After summarizing the topic, the review analyzes the usability, goodness factors, and computational complexities of the algorithms that implement these techniques.

Before I had used Paperspace Gradient, Colaboratory was my go-to option to make and run Jupyter notebooks over a cloud GPU. Colab was developed by Google and had always been a free resource for machine/deep learning enthusiasts and beginners alike. Recently they've released Colab Pro earlier this year, which gives some convenient perks to its purchasers we'll discuss later. Colab has its fair share of advantages over the rest. For example, it is insanely easy to get started with making notebooks and running models on a GPU.

We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs. In particular, we introduce a neural-network control (NNC) framework, which represents dynamical systems by neural ordinary different equations (neural ODEs), and find that NNC can learn control signals that drive networked dynamical systems into desired target states. To identify the influence of different target states on the NNC performance, we study two types of control: (i) microscopic control and (ii) macroscopic control. Microscopic control minimizes the L2 norm between the current and target state and macroscopic control minimizes the corresponding Wasserstein distance. We find that the proposed NNC framework produces low-energy control signals that are highly correlated with those of optimal control. Our results are robust for a wide range of graph structures and (non-)linear dynamical systems.

Inspired by the work done by Andrychowicz et al. in [7], they modeled an I. INTRODUCTION LSTM as a meta-learner, which helped to train another neural Today, most deep reinforcement learning techniques require network "learner" classifier using a few-shot framework. Unlike models to be trained on a large number of training samples. In common deep learning optimizers such as Momentum, contrast, Model-Agnostic Meta-Learning (MAML) proposed ADAM, and Adagrad, this method is able to train a model by Finn et.