Original Source Here Transfer Learning for Guitar Effects How difficult would life be if every new thing had to be learned from scratch? If once you learned the word “dog”, you had to once again learn that letters are symbols with sounds and you can put them together to make words and that “c-a-t” is a different four-legged animal with a tail? Thankfully, our brains are great at recognizin g patterns. If you already know that the word “dog” means one animal, then it’s not a huge stretch to understand that the word “cat” represents a different animal. Using what we already know to solve a similar problem is the concept behind transfer learning in artificial neural networks. Transfer learning is a technique in machine learning where information gained from solving one problem is applied to a similar but different problem. Practically speaking, this technique can be used to attain faster convergence or lower loss when training a neural network. It can also be used when there ...

Original Source Here Image Based Ingredient Prediction for an Indian Food Dataset Introduction In online food delivery, while ordering food, customers rely on information provided which is usually limited to name and a high-level description of the dish, an image and if the dish is vegetarian. To further improve the food ordering experience, more information on a dish can help a customer in decision making like what all ingredients are involved in the dish or how the food is prepared. Curating this additional information typically requires non-trivial manual effort. Multiple deep learning based work has been per- formed on food images which we discuss in detail in the related work section. We observed that there are existing models which provide ingredient information given a dish image but the challenge is in the data distribution. This paper explains the differences in ingredients across regions and countries, which implies difficulties in using pre-trained models on a dif...

Original Source Here An intuitive walkthrough of Machine Learning, Neural Networks, and Deep Learning Continue reading on Medium » AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/10/20/machine-learning-ml-and-neural-networks-nn-an-intuitive-walkthrough/

Original Source Here Nearly 150,000 Tesla cars are using the company’s new “safety score,” a tool rolled out last month to determine whether owners can access the beta version of its “Full Self-Driving” software, executives said during its third quarter earnings call . While 150,000 cars are now part of the Full-Self Driving (FSD) beta enrollment program, a fraction of drivers have been given access to the software. Only 2,000 drivers have been able to test the FSD program over the past year. Earlier this month, Tesla rolled out version 10.2 to around 1,000 additional owners with perfect safety scores . Tesla charges $10,000 for the FSD software, which CEO Elon Musk has promised for years will one day deliver full autonomous driving capabilities. However, Tesla vehicles equipped with FSD are not self-driving. FSD is an advanced driver assistance system that includes a number of automated features, including its parking tool Summon as well as Navigate on Autopilot, an ...

Original Source Here Do You Need the 2021 MacBook Pro for Data Science? Insights from a person using both an M1 and Intel Mac daily Hello there friends! On October 18, Apple finally unveiled their refreshed MacBook Pro line , and wow do those devices look gorgeous! It’s no secret that I’m a big fan of Apple, and while I’d love to be able to own all their devices, I — like you — have to be conscientious on which devices I choose to own and how often I upgrade them. One of the huge things Apple touted in today’s keynote was the astounding power of the new M1 Pro and M1 Max chipsets, and you might be wondering, is it worth me upgrading to this new hardware for my data science work? Obviously, I haven’t used those devices myself, but I can provide insight from a perspective of somebody who daily uses both an M1 Mac and an Intel Mac. More specifically, I use both an M1 Mac mini and M1 iPad Pro for my personal data science work, and for my regular job as machine learning engineer,...

Original Source Here Vision Transformers in PyTorch When convolutions stopped being a trend? Image from Unsplash. Convolutional neural networks (CNNs) have been the pre-dominant backbone for almost all networks used in computer vision and image-related tasks due to the advantages they have in 2D neighbourhood awareness and translation equivariance compared to traditional multi-layer perceptrons (MLPs). However, with the recent shift in the language… AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/10/20/vision-transformers-in-pytorch/

https://miro.medium.com/max/1200/0*UuWQSZLhG7BvlXjq Original Source Here Why Gradient Descent Works? Everybody knows what Gradient Descent is and how it works. Ever wondered why it works? Here’s a mathematical explanation Photo by Yuriy Chemerys on Unsplash What is Gradient Descent? Gradient descent is an iterative optimization algorithm that is used to optimize the weights of a machine learning model (linear regression, neural networks, etc.) by minimizing the cost function of that model. The intuition behind gradient descent is this: Picture the cost function (denoted by f( Θ̅ ) where Θ̅ =[Θ₁, … Θₙ ] ) plotted in n dimensions as a bowl. Imagine a randomly placed point on that bowl represented by n coordinates (this is the initial value of your cost function). The minimum of this “function” then will be the bottom of the bowl. The goa l is then to reach to the bottom of the bowl (or minimize the cost) by progressively moving downwards on the bowl. For this to hap...