"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).
Readers of this blog already know what loss functions are in AI but for people starting into the field let me define it again. The loss function is a mathematical equation that all the deep learning algorithm tries to minimize or optimize. As we all know that Deep learning takes an iterative process to learn things, in every step, it calculates some metric that tells it how close it is to the original label and based upon that it optimizes its parameters. So the metrics that we minimize or optimize are called loss functions. There are a lot of famous loss functions like Mean square error, categorical cross-entropy, Dice loss, and many more.
Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. Pronouns: He/hi (show all) Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. The Holy Grail of AI research is called "general artificial intelligence," or GAI. A machine imbued with general intelligence would be capable of performing just about any task a typical adult human could. The opposite of general AI is narrow AI – the kind we have today.
Deep Learning and Computer Vision A-Z: OpenCV, SSD & GANs, Become a Wizard of all the latest Computer Vision tools that exist out there. Detect anything and create powerful apps. You've definitely heard of AI and Deep Learning. But when you ask yourself, what is my position with respect to this new industrial revolution, that might lead you to another fundamental question: am I a consumer or a creator? For most people nowadays, the answer would be, a consumer.
AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What type of #AI generates something new from data it is fed? It might be the third wave of Artificial Human Intelligence, dubbed as Neuro-Symbolic AI using #DeepLearning to boost the Symbolic AI approach, and vice versa, by combining logic and learning to transcend both limitations. In terms of Deep Learning, some of the issues are as follows, #Machinelearning requires a massive amount of data to train neural networks, which is not easy to get every time. Selecting the right algorithm is crucial as the results may be biased and lead to a bad prediction. They lack the ability to generalize and are bound by their training data i.e. there is a lack of creativity and they are only efficient at what they already know.
Deep neural networks are machine learning systems that automatically learn a task if provided with necessary data. An artificial neural network (ANN) having numerous layers between the input and output layers is known as a deep neural network (DNN). Neural networks are made available in various shapes and sizes. However, they all include the same essential components: neurons, synapses, weights, biases, and functions. Recently, scientists have added a total of 301 validated exoplanets to the already existing exoplanet tally. The cluster of planets is the most recent addition to the 4,569 confirmed planets orbiting various faraway stars.
Interpreting a Machine learning model helps in not only understanding what is going inside the black box but also explaining the predictions of the model. Generally, Machine learning or Deep learning models are black boxes which means it is very difficult to interpret whatever is going in inside the model.
In the previous post we have seen how to build one Shallow Neural Network and tested it on a dataset of random points. In this post we will demonstrate how to build efficient Neural Networks using the nn module. That means that we are going to use a fully-connected ReLU network with one hidden layer, trained to predict the output \(y \) from given \(x \) by minimizing squared Euclidean distance. You will find that simpler and powerful. For demonstration purposes we will use the MNIST dataset.
"We have an AI-driven algorithm that automatically designs AI algorithms to be more accurate and run faster in a production environment," said Yonatan Geifman, Deci AI co-founder and CEO. "Our technology automatically designs new structures of neural networks, optimizing them for the data and machine learning problems we are trying to solve and to run faster on the production hardware." "Deci is a company that was founded a little more than two years ago with a goal of making AI more accessible and scalable, with a technology that improves the way people develop, build, optimize and deploy AI," Geifman asserted. "So basically, we help data scientists to solve their problems faster with automated tools." Geifman founded the company in 2019 along with its chief scientist, Prof. Ran El-Yaniv, who was Geifman's professor at the Technion, and COO Jonathan Eliel, who served with Geifman in a top air force intelligence unit.
AI in gaming means adaptive as well as responsive video game experiences facilitated through non-playable characters behaving creatively as if they are being controlled by a human game player. From the software that controlled a Pong paddle or a Pac-Man ghost to the universe-constructing algorithms of the space exploration Elite, Artificial intelligence (AI) in gaming isn't a recent innovation. It was as early as 1949, when a cryptographer Claude Shannon pondered the one-player chess game, on a computer. Gaming has been an important key for the development of AI. Researchers have been employing its technology in unique and interesting ways for decades.
It's Friday night and you started Training your Deep Learning Model. You spent your Weekends checking the Model progress and BAM!!! its done on Monday morning. Excited about checking the Model Performance you quickly run your Jupyter Notebook cells and OOPS!!! This is the'point of no-return' and it happens a lot of times, where you just wonder'what if' I had not done this and that and most of the times it ends with acceptance. Here is what you should do!!!!