"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).
The ability to automate human sight is opening up massive opportunities for value creation across ... [ ] every sector of the economy. Computer vision is the most technologically mature field in modern artificial intelligence. This is about to translate into enormous commercial value creation. The deep learning revolution has its roots in computer vision. At the now-historic 2012 ImageNet competition, Geoff Hinton and team debuted a neural network--a novel architecture at the time--whose performance eclipsed all previous efforts at computer-based image recognition. The era of deep learning was born, with computer vision as its original use case.
In order to understand the importance of activation functions, we must first recap how a neural network computes a prediction/output. This is generally referred to as Forward Propagation. During forward propagation, the neural network receives an input vector x and outputs a prediction vector y. Each layer of the network is connected via a so-called weight matrix with the next layer. In total, we have 4 weight matrices W1, W2, W3, and W4.
As part of their AI for Good Global Summit, the UN has explored the role of artificial intelligence in the creation of art, putting forward a call for "AI-powered" artwork from creators across the globe. Really, almost anyone with enough patience, basic IT capacity and the desire to learn could scribble off some machine learning-based art. WIRED's Tom Simonite did it with open source tools and the same machine learning software used by researchers at Facebook and IBM. You can try it too, with premium and free programs, like Runaway ML, GANBreeder, Magenta and Processing. In truth, it's the machine learning programs, such as the popular generative adversarial network (GAN), that do the art, not you.
The seemingly simple task of grasping an object from a large cluster of different kinds of objects is "one of the most significant open problems in robotics," according to Sergey Levine and collaborators. Grasping is a good example of problems that bedevil real-world machine learning, including latency that throws off the expected order of events, and goals that may be difficult to specify. The vast majority of artificial intelligence has been developed in an idealized environment: a computer simulation that dodges the bumps of the real world. Be it DeepMind's AlphaMu program for Go and chess and Atari or OpenAI's GPT-3 for language generation, the most sophisticated deep learning programs have all benefitted from a pruned set of constraints by which software is improved. For that reason, the hardest and perhaps the most promising work of deep learning may lie in the realm of robotics, where the real world introduces constraints that cannot be fully anticipated.
I recently wrote a book on deep learning - Mastering PyTorch which is now available on Amazon. It is an applied deep learning book with tons of exercises on training, testing, deploying, interpreting .. various kinds of deep learning models, using PyTorch. If you want to get hands-on proficiency in deep learning, this book can be a good resource. I have tried to keep the contents easy to grasp while retaining all the essential technical concepts.If you do get a copy, please let me know how you found it, and possibly leave an Amazon review. You can also read a synopsis of the book here.
Deep learning is an Artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network. Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled.
Amazon on Monday announced the general availability of Alexa Conversations, a deep learning-based dialog manager for the Alexa Skills Kit. The tool, first introduced in preview in 2019, helps developers create more natural conversations with customers. "Natural language is actually a very difficult thing to emulate," Nedim Fresko, Amazon's VP of Alexa Devices and Developer Technologies, told ZDNet last year. "When people speak naturally, they change direction, they make contextual references to things they said. Sometimes they over-supply information, sometimes they under-supply it -- when that happens, consumers revert to robotic language and simple phrases, and developers just give up."
If you are someone like me who does not want to setup an at home server to train your Deep Learning model, this article is for you. Likely, cloud-based Machine Learning infrastructures are your options. I will go over the step-by-step process of how to do this in AWS SageMaker. Amazon SageMaker comes with a good number of pre-trained models. These models are prebuilt docker images in AWS.
Text detection and recognition (also known as Text Spotting) from an image is a very useful and challenging problem that deep learning researchers have been working on since many years because of its practical applications in fields like document scanning, robot navigation and image retrieval, etc. Almost all the methods consisted of two separate stages so far: 1) Text detection 2) Text recognition. Text detection just finds out where the text is located in the given image and on these results, text recognition actually recognizes the characters from the text. Because of these two stages, two separate models were required to be trained and hence prediction time was a bit higher. Because of higher test time, the models were not suitable for real time applications. Contrary to this, FOTS solves this two stage problem using a unified end to end trainable model/network by detecting and recognizing text simultaneously.