Deep Learning for Symbolic Mathematics
Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In
this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations....
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Data Science Articles & Videos
Fiddler raises $10.2 million for AI that explains its reasoning
Explainable AI, which refers to techniques that attempt to bring transparency to traditionally opaque AI models and their predictions, is a burgeoning subfield of machine learning research. It’s no wonder — models sometimes learn undesirable tricks to accomplish goals on training data, or they develop biases with the potential to cause harm if left unaddressed. That’s why Krishna Gade and Amit Paka founded Fiddler...
Distributed Machine Learning on Mobile Devices: A Survey
We survey a number of widely-used mobile distributed machine learning methods. We also present an in-depth discussion on the challenges and future directions in this area. We believe that this survey can demonstrate a clear overview of mobile distributed machine learning and provide guidelines on applying mobile distributed machine learning to real applications...
Google has released a giant database of deepfakes to help fight deepfakes
On Tuesday, Google released an open-source database containing 3,000 original manipulated videos as part of its effort to accelerate the development of deepfake detection tools. It worked with 28 actors to record videos of them speaking, making common expressions, and doing mundane tasks. It then used publicly available deepfake algorithms to alter their faces...
Bi-Tempered Logistic Loss for Training Neural Nets with Noisy Data
The ability of an ML model to deal with noisy training data depends in great part on the loss function used in the training process. For classification tasks, the standard loss function used for training is the logistic loss. However, this particular loss function falls short when handling noisy training examples due to two unfortunate properties...
Conditional Transferring Features: Scaling GANs to Thousands of Classes with 30% Less High-quality Data for Training
Generative adversarial network (GAN) has greatly improved the quality of unsupervised image generation. Previous GAN-based methods often require a large amount of high-quality training data while producing a small number (e.g., tens) of classes. This work aims to scale up GANs to thousands of classes meanwhile reducing the use of high-quality data in training. We propose an image generation method based on conditional transferring features, which can capture pixel-level semantic changes when transforming low-quality images into high-quality ones...
5 Common Pitfalls To Avoid When Crafting Your Data Science Resume
You're spending hours on your resume and still have no job interviews to show for it.
You've gotten a lot of feedback that your resume doesn't "make sense", but you're not sure how best to describe your experiences or what specifically you should be conveying to Hiring Managers. You're not good at "tooting your own horn" but you know that some amount is needed to stand out...
Create D3 Data Visualizations As Fast As You Can Sketch
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Training & Resources
Automation via Reinforcement Learning
Let’s delve a bit more into what it means to automate a task with reinforcement learning. The basic process can be decomposed into two steps: first reduce the problem to RL by writing it as an MDP or POMDP, and then solve for the optimal policy of the MDP or POMDP2. The optimal policy then allows us to fully automate the task, completing it any number of times with no further human effort...
Sudo Write Me a Program: GitHub Releases the ImageNet for Code
GitHub’s CodeSearchNet provides large datasets, tools, and benchmarks via Weights & Biases to inspire and support broader community research on source code as a language (for semantic search, understanding, translation, & more) — you can join this collaboration here...
The Book of R: A First Course in Programming and Statistics
"The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis"...
For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page