If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
There are many types of python libraries available. Here I am mentioning some popular libraries for Machine Learning. NumPy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays. It is the fundamental package for scientific computing with Python.
Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve.
Data Science has been a big deal for quite some time now. In the rapidly expanding technological world of today, when humans tend to generate a lot of data, it is quintessential that we know how to analyze, process, and use that data for further knowledgable business insights. There has been enough said on Python vs R for Data Science but I am not talking about it here. We need both of them and that's about it. The languages made to the list on the basis of their popularity, number of Github mentions, the pros and the cons, and their relevancy to Data Science in 2020.
Baidu has released the toolkit for its quantum machine learning platform, Paddle Quantum, which it says will enable developers to build and train quantum neural network models. Built on the Chinese tech giant's deep learning platform PaddlePaddle, the toolkit also includes quantum computing applications. Paddle Quantum, currently available on GitHub, comprises a set of quantum machine learning toolkits, including a quantum chemistry library and optimisation tools, as well as three quantum applications: quantum machine learning, quantum chemical simulation, and quantum combinatorial optimisation. Several underlying functions of PaddlePaddle, including matrix multiplications, also enable Paddle Quantum to support quantum circuit models and general quantum computing research, Baidu said in a statement on Wednesday. Asian country has began investing in quantum technology and is at a similar starting point with other economic powers in this field, says Shanghai-born Turing Award winner Andrew Yao.
Smart speakers sit in our homes, quietly listening to everything we say and feeding what they learn back to the corporations that spawned them, like sinister Elves on Shelves. Still, some of us can't resist the shiny allure of being able to yell random questions into the void like a medieval despot and get an answer back. This raises the difficult question of exactly which servant/spy to employ in your smart home. The Google Home and the Amazon Echo are two of the most prolific smart speakers on the market, but at first glance there's little separating them. Both allow you to control your smart home, play music, and set timers by speaking.
"Just as electricity transformed almost every industry 100 years ago, today I actually have hard time thinking of an industry that I don't think AI (Artificial Intelligence) will transform in the next several years" -- Andrew NG I have long been fascinated with these algorithms, capable of something that we can as humans barely begin to comprehend. However, even with all these resources one of the biggest setbacks any ML practitioner has ever faced would be tuning the model's hyperparameters. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. The same kind of machine learning model can be trained on different constraints, learning rates or kernels and other such parameters to generalize to different datasets, and hence these instructions have to be tuned so that the model can optimally solve the machine learning problem.
It contains lots of pre-trained machine learning models that data scientists use rather than creating their own models. Obviously, it depends on what ML model you need to use. If you are looking for something very specific for your intent, maybe it's better to create your own model. Theano uses NumPy's syntax to optimize and evaluate mathematical expressions. It uses the GPU to speed up its processes.
"Artificial Intelligence, deep learning, machine learning -- whatever you're doing if you don't understand it -- learn it. Because otherwise, you're going to be a dinosaur within 3 years." How will You benefit from this Free Course? This course has one goal:Teaching you how Artificial Neural Networks work at a low level and how to implement them from scratch using TensorFlow. How are We going to do that?
It's a new week, and what better time to get your hands on another free eBook? We have been highlighting a new such installment weekly for the better part of the past few months, doing our best to single out and share top learning materials for those stuck at home right now, or really for anyone interested in learning a new concept or brushing up on what they already know. This week we turn our attention to the topic of automated machine learning (AutoML), a personal favorite of mine. What is automated machine learning? It is a wide (and widening) concept, but I've previously tried to capture its essence as such: If, as Sebastian Raschka has described it, computer programming is about automation, and machine learning is "all about automating automation," then automated machine learning is "the automation of automating automation."