Definition. Deep learning is a class of machine learning algorithms that: 199-200 uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.. Overview. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason Deep learning is one of the hottest up-and-coming job sectors in the world, with a market currently ranging between $3.5 and $5.8 trillion. On average, a Deep Learning Engineer earns $135,878 a year, but salaries can climb even higher.. The job prospects for Deep Learning Engineers are looking good as well, with a projected growth rate of 11% a year between now and 2029 Deep learning is a subset of machine learning that trains a computer to perform human-like tasks, such as speech recognition, image identification and prediction making. It improves the ability to classify, recognize, detect and describe using data. The current interest in deep learning is due, in part, to the buzz surrounding artificial intelligence (AI)

Deep Learning is Large Neural Networks. Andrew Ng from Coursera and Chief Scientist at Baidu Research formally founded Google Brain that eventually resulted in the productization of deep learning technologies across a large number of Google services.. He has spoken and written a lot about what deep learning is and is a good place to start. In early talks on deep learning, Andrew described deep. Deep learning can be considered as a subset of machine learning. It is a field that is based on learning and improving on its own by examining computer algorithms. While machine learning uses simpler concepts, deep learning works with artificial neural networks, which are designed to imitate how humans think and learn Deep Learning: Methods and Applications is a timely and important book for researchers and students with an interest in deep learning methodology and its applications in signal and information processing. This book provides an overview of a sweeping range of up-to-date deep learning ** التعلّم المُتعمّق أو التعلّم العميق (بالإنجليزية: Deep Learning) هو مجال بحث جديد يتناول إيجاد نظريات وخوارزميات تتيح للآلة أن تتعلم بنفسها عن طريق محاكاة الخلايا العصبية في جسم الإنسان**. و أحد فروع العلوم التي تتناول علوم. **Deep** **learning** is a particular kind of machine **learning** that achieves great power and flexibility by **learning** to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones

Deep learning is a collection of statistical techniques of machine learning for learning feature hierarchies that are actually based on artificial neural networks. So basically, deep learning is implemented by the help of deep networks, which are nothing but neural networks with multiple hidden layers What is deep learning? Deep learning is a type of machine learning and artificial intelligence that imitates the way humans gain certain types of knowledge.Deep learning is an important element of data science, which includes statistics and predictive modeling.It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep. Deep Learning Specialization . 5 courses. Intermediate > Andrew Ng, Kian Katanforoosh, Younes Bensouda Mourri . Practical Data Science (PDS) Specialization . 3 courses > Advanced > Antje Barth, Shelbee Eigenbrode, Sireesha Muppala, Chris Fregly > Amazon Web Services (AWS A deep-learning architecture is a mul tilayer stack of simple mod- ules, all (or most) of which are subject to learning, and man y of which compute non-linea r input-outpu t mappings Deep Learning is a computer software that mimics the network of neurons in a brain. It is a subset of machine learning based on artificial neural networks with representation learning. It is called deep learning because it makes use of deep neural networks. This learning can be supervised, semi-supervised or unsupervised

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. The deep learning textbook can now be ordered on Amazon Enroll for FREE Artificial Intelligence Course & Get your Completion Certificate: https://www.simplilearn.com/learn-ai-basics-skillup?utm_campaign=Skill.. A deep-learning architecture is a multilayer stack of simple modules, all (or most) of which are subject to learning, and many of which compute non-linear input-output mappings. Each module in. Though deep learning methods gained immense popularity in the last 10 years or so, the idea has been around since the mid-1950s when Frank Rosenblatt invented the perceptron on an IBM® 704 machine. It was a two-layer-based electronic device that had the ability to detect shapes and do reasoning. Advancements in this field in recent years are.

Deep Learning frameworks allow us to integrate and implement machine learning and AI on a large scale with ease. Get Started With Deep Learning Tutorial Now! Deep Learning is an emerging field based on the principles of learning and improving with the help of sophisticated computer algorithms The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology Deep learning is the one category of machine learning that emphasizes training the computer about the basic instincts of human beings. In deep learning, a computer algorithm learns to perform classification tasks directly on complex data in the form of images, text, or sound

The path to deep learning and reaching full AI Taking machine learning farther. Within artificial intelligence, machine learning (ML) is a sub-field — and deep learning is an advanced sub-field of machine learning. In machine learning, a device is able to process and evaluate information beyond its programming based on context Le deep learning ou apprentissage profond est un sous-domaine de l'intelligence artificielle (IA). Ce terme désigne l'ensemble des techniques d'apprentissage automatique (machine learning. The difference between machine learning and deep learning. In practical terms, deep learning is just a subset of machine learning. In fact, deep learning is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged). However, its capabilities are different. While basic machine learning models do.

Deep learning é um tipo de machine learning que treina computadores para realizar tarefas como seres humanos, o que inclui reconhecimento de fala, identificação de imagem e previsões. Em vez de organizar os dados para serem executados através de equações predefinidas, o deep learning configura parâmetros básicos sobre os dados e treina o computador para aprender sozinho através do. Deep Learning is a subset of Machine Learning, which on the other hand is a subset of Artificial Intelligence. Artificial Intelligence is a general term that refers to techniques that enable computers to mimic human behavior. Machine Learning represents a set of algorithms trained on data that make all of this possible DEEP LEARNING Deep learning is a subset of AI and machine learning that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation, and others. Deep learning differs from traditional machine learning techniques in that they can automatically learn representations from data suc

Seasonal products have a limited shelf life. At the end of the season, these products are typically scrapped or sold at deep discounts. Airlines, hotels and others with perishable products typically adjust prices dynamically to meet demand. By using analytic software, similar forecasting techniques can improve margins, even for hard goods 위키백과, 우리 모두의 백과사전. 심층 학습 (深層學習) 또는 딥 러닝 ( 영어: deep structured learning, deep learning 또는 hierarchical learning )은 여러 비선형 변환기법의 조합을 통해 높은 수준의 추상화 (abstractions, 다량의 데이터나 복잡한 자료들 속에서 핵심적인 내용.

Deep learning has gained significant attention in the industry by achieving state of the art results in computer vision and natural language processing. Fundamental neural network architectures, feedforward networks, convolutional networks, and recurrent networks. The course is structured around 12 weeks of lectures and exercises * Deep Learning is one of the most highly sought after skills in AI*. We will help you become good at Deep Learning. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam. Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs

- Deep Learning continues to fascinate us with its endless possibilities such as fraud detection and pixel restoration. Deep learning is an ever-growing industry, uilling with the help of a free introduction to deep learning course can help you understand the basic concepts clearly and power ahead your career
- Deep Learning is a collection of those artificial neural network algorithms that are inspired by how a human brain is structured and is functioning. Human brain is one the powerful tools that is good at learning. And these deep learning techniques try to mimic the human brain with what we currently know about it
- Deep Learning is one of the most highly sought after skills in AI. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more
- Deep Learning • Deep learning is a sub field of Machine Learning that very closely tries to mimic human brain's working using neurons. • These techniques focus on building Artificial Neural Networks (ANN) using several hidden layers
- Le Deep learning ou apprentissage profond est l'une des technologies principales du Machine learning.Avec le Deep Learning, nous parlons d'algorithmes capables de mimer les actions du cerveau humain grâce à des réseaux de neurones artificielles.. Les réseaux sont composés de dizaines voire de centaines de « couches » de neurones, chacune recevant et interprétant les informations de.
- Deep Learning définition simple et origines de l'apprentissage profond. Le concept de Machine Learning date du milieu du 20ème siècle. Dans les années 1950, le mathématicien britannique Alan Turing imagine une machine capable d'apprendre, une « Learning Machine »

- Deep Learning Competencies. The Deep Learning Competencies, better known as the 6 C's, are the skill sets each and every student needs to achieve and excel in, in order to flourish in today's complex world. These competencies form the foundation for the New Measures and NPDL teachers use the Deep Learning Progressions to assess students.
- What is Deep Learning? Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. Artificial Intelligence Machine Learning Deep Learning Deep Learning by Y. LeCun et al. Nature 201
- This option provides a docker image which has Caffe2 installed. Users can launch the docker container and train/run deep learning models directly. This docker image will run on both gfx900(Vega10-type GPU - MI25, Vega56, Vega64,) and gfx906(Vega20-type GPU - MI50, MI60) Launch the docker containe
- Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many ot
- Deep learning is powered by layers of neural networks, which are algorithms loosely modeled on the way human brains work. Training with large amounts of data is what configures the neurons in the neural network. The result is a deep learning model which, once trained, processes new data. Deep learning models take in information from multiple.

* Deep learning is an emerging area of machine learning (ML) research*. It comprises multiple hidden layers of artificial neural networks. The deep learning methodology applies nonlinear. Deep Learning. By now, you might already know machine learning, a branch in computer science that studies the design of algorithms that can learn. Today, you're going to focus on deep learning, a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain

- 其实目前做deep learning，尤其是cv方面的，10-fold cross-validation这样的处理其实不太多见，大多数只是普通的val挑出模型，然后放在test-dev上验证。只不过今天很多人有点忽视test-dev的重要性，所以产生了一些误解
- Deep learning algorithms learn progressively more about the image as it goes through each neural network layer. Early layers learn how to detect low-level features like edges, and subsequent layers combine features from earlier layers into a more holistic representation. For example, a middle layer might identify edges to detect parts of an.
- g language, in which there are many packages for neural networks
- Deep learning can be defined as the method of machine learning and artificial intelligence that is intended to intimidate humans and their actions based on certain human brain functions to make effective decisions. It is a very important data science element that channels its modeling based on data-driven techniques under predictive modeling.
- Deep Learning Tutorial Brains, Minds, and Machines Summer Course 2018 TA: Eugenio Piasini & Yen-Ling Kuo. Roadmap Supervised Learning with Neural Nets Convolutional Neural Networks for Object Recognition Recurrent Neural Network Other Deep Learning Models
- This page is a collection of lectures on deep learning, deep reinforcement learning, autonomous vehicles, and AI given at MIT by Lex Fridman and others. Artificial Intelligence (2022) Announcement: Lectures will not be held in-person this year due to the high number of registered attendees and concerns of MIT COVID safety protocols

- Deep learning is a subset of machine learning that's based on artificial neural networks. The learning process is deep because the structure of artificial neural networks consists of multiple input, output, and hidden layers. Each layer contains units that transform the input data into information that the next layer can use for a certain.
- ate between classes
- CONTENTS 6.3 HiddenUnits. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 6.4 ArchitectureDesign. . . . . . . . . . . . . . . . . . . . . . . . . . 19

Deep learning artificial intelligence training Bangalore - ExcelR is the fastest growing company is providing Deep learning artificial intelligence training Bangalore. We got the best experienced Faculty for the training and after successful completion of artificial intelligence training ExcelR will provide you certification from Malaysian. Deep Learning is a machine learning technique that constructs artificial neural networks to mimic the structure and function of the human brain. In practice, deep learning, also known as deep structured learning or hierarchical learning, uses a large number hidden layers -typically more than 6 but often much higher - of nonlinear processing to extract features from data and transform the data. Deep Learning (deutsch: mehrschichtiges Lernen, tiefes Lernen oder tiefgehendes Lernen) bezeichnet eine Methode des maschinellen Lernens, die künstliche neuronale Netze (KNN) mit zahlreichen Zwischenschichten (englisch hidden layers) zwischen Eingabeschicht und Ausgabeschicht einsetzt und dadurch eine umfangreiche innere Struktur herausbildet. Es ist eine spezielle Methode der. A world in which medical devices become smarter with each new patient they see. We operate at the intersections of medicine, engineering, and machine learning with a collective goal to change the world. We envision, finance, support and mentor a select number of startups utilizing deep learning focused on making our vision a reality. Read more

Deep learning requires a large amount of data to minimize overfitting and improve the performances, whereas it is difficult to achieve these big datasets with medical images of low-incidence serious diseases in general practice. Thus, a better classification strategy is needed for these small datasets Deep Learning是机器学习中一个非常接近AI的领域，其动机在于建立、模拟人脑进行分析学习的神经网络，最近研究了机器学习中一些深度学习的相关知识，本文给出一些很有用的资料和心得。Key Words：有监督学习与无监督学习，分类、回归，密度估计、聚类，深度学习，Sparse DBN，1 Python Deep Learning Tutorial. Python is a general-purpose high level programming language that is widely used in data science and for producing deep learning algorithms. This brief tutorial introduces Python and its libraries like Numpy, Scipy, Pandas, Matplotlib; frameworks like Theano, TensorFlow, Keras

* Introduction to Deep Learning Algorithms*. Before we move on to the list of deep learning algorithms in machine learning, let's understand the structure and working of deep learning algorithms with the popular MNIST dataset.The human brain is a network of billions of neurons that help in representing a tremendous amount of knowledge Deep learning super sampling (DLSS) is a machine-learning and spatial image upscaling technology developed by Nvidia and exclusive to its graphics cards for real-time use in select video games, using deep learning to upscale lower-resolution images to a higher resolution for display on higher-resolution computer monitors. Nvidia claims this technology upscales images with quality similar to. Deep learning is a subset of machine learning, a branch of artificial intelligence that configures computers to perform tasks through experience. Contrary to classic, rule-based AI systems.

- Deep Learning systems, typified by deep neural networks, are increasingly taking over all AI tasks, ranging from language understanding, and speech and image recognition, to machine translation, planning, and even game playing and autonomous driving. As a result, expertise in deep learning is fast changing from an esoteric desirable to a.
- Basically, Deep learning is a sub-field of Machine Learning and Machine Learning is a sub-field of Artificial Intelligence as shown in the image below: When we look at something like AlphaGo, it's often portrayed as a big success for deep learning, but it's actually a combination of ideas from several different fields of AI and machine.
- Deep Learning 中文翻译. 在众多网友的帮助和校对下，中文版终于出版了。尽管还有很多问题，但至少90%的内容是可读的，并且是准确的。 我们尽可能地保留了原书Deep Learning中的意思并保留原书的语句。 然而我们水平有限，我们无法消除众多读者的方差
- Test deep learning models by including them into system-level Simulink simulations. Test edge-case scenarios that are difficult to test on hardware. Understand how your deep learning models impact the performance of the overall system. Watch video (3:45) Deploy Trained Networks

Deep Learning is a subset of Artificial Intelligence, which directs a computer to perform classification tasks directly from texts, images, or sounds. Deep Learning is also a specialized form of Machine Learning. It is one of the most popular domains in the AI space, allowing you to develop multi-layered models of varying complexities * Deep learning benefits someone passionate about working in the AI fields which can create types of deep learning networks that help machines perform human functions*. A person best suited to learn about deep learning has a vested interest in understanding how the intelligence is built to run everything from driverless cars, mobile devices, stock. Deep Learning requires high-end machines contrary to traditional Machine Learning algorithms. GPU has become a integral part now to execute any Deep Learning algorithm.. In traditional Machine learning techniques, most of the applied features need to be identified by an domain expert in order to reduce the complexity of the data and make patterns more visible to learning algorithms to work Deep Learning (DL) focuses on a subset of machine learning that goes even further to solve problems, inspired by how the human brain recognizes and recalls information without outside expert input to guide the process. DL applications need access to massive amounts of data from which to learn The Deep Learning Summit was one of the best-organized conferences I'd been to and I cover dozens every year. There was a diverse range of very inspiring speakers, and the event facilitated meaningful connections between attendees Data Scientis

Google, Facebook & Amazon all use deep learning methods, but how does it work? Research Fellow & Deep Learning Expert Brais Martinez explains.EXTRA BITS fro.. Description. This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition **Deep** **learning**, also known as hierarchical **learning** or **deep** structured **learning**, is a type of machine **learning** that uses a layered algorithmic architecture to analyze data. In **deep** **learning** models, data is filtered through a cascade of multiple layers, with each successive layer using the output from the previous one to inform its results

* Deep Learning ist eine Machine-Learning-Technik, mit der Computer eine Fähigkeit erwerben, die Menschen von Natur aus haben: aus Beispielen zu lernen*. Deep Learning ist eine wichtige Technologie in fahrerlosen Autos, die es diesen ermöglicht, ein Stoppschild zu erkennen oder einen Fußgänger von einer Straßenlaterne zu unterscheiden L'apprentissage profond [1], [2] ou apprentissage en profondeur [1] (en anglais : deep learning, deep structured learning, hierarchical learning) est un ensemble de méthodes d'apprentissage automatique tentant de modéliser avec un haut niveau d'abstraction des données grâce à des architectures articulées de différentes transformations non linéaires [3] Deep Learning, also known as deep neural learning or deep neural network, is an aspect of artificial intelligence that depends on data representations rather than task-specific algorithms. It allows the user to run supervised, semi-supervised, and unsupervised learning. Deep Learning is inspired by the ways humans process information and then. A Guide to Deep Learning and Neural Networks. As a subset of artificial intelligence, deep learning lies at the heart of various innovations: self-driving cars, natural language processing, image recognition and so on. Companies that deliver DL solutions (such as Amazon, Tesla, Salesforce) are at the forefront of stock markets and attract. Deep Learning networks are the mathematical models that are used to mimic the human brains as it is meant to solve the problems using unstructured data, these mathematical models are created in form of neural network that consists of neurons. The neural network is divided into three major layers that are input layer( first layer of neural.

Learn Intro to Deep Learning Tutorials. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site Deep Learning Architecture - Conclusion. As you can see, although deep learning architectures are, generally speaking, based on the same idea, there are various ways to achieve a goal. That's why it's so important to choose deep learning architecture correctly. If you want to find out more about this tremendous technology, get in touch. Deep Learning is the subset of Artificial Intelligence (AI) and it mimics the neuron of the human brain. Deep Learning Models create a network that is similar to the biological nervous system. It imitates the human thinking process. Deep learning is the collection of nodes where each node acts as a neuron. The hype about deep learning is pretty.

The objective of this course is to provide a complete introduction to deep machine learning. How to design a neural network, how to train it, and what are the modern techniques that specifically handle very large networks Deep learning based image processing methods can be a great solution in identifying various rice plant diseases accurately and precisely. For this research, we have collected a total 12 different types of rice disease images. The images has been pre-processed and augmented using different algorithms Synopsis. This course is a continuition of Math 6380o, Spring 2018, inspired by Stanford Stats 385, Theories of Deep Learning, taught by Prof. Dave Donoho, Dr. Hatef Monajemi, and Dr. Vardan Papyan, as well as the Simons Institute program on Foundations of Deep Learning in the summer of 2019 and IAS@HKUST workshop on Mathematics of Deep Learning during Jan 8-12, 2018 Discussion 9: Policy Gradients & Q-Learning. Homework 3: Natural Language Processing. Homework 4: Deep Reinforcement Learning. Lecture 17: Autoencoders & Latent Variable Models. Lecture 18: Variational Autoencoders & Invertible Models Deep Learning is the closest concept that has helped machines become autonomous. Being a top career for the decade, Deep Learning has caused a lot of stir in the market as it has created thousands of jobs

The present survey, however, will focus on the narrower, but now commercially important, subfield of Deep Learning (DL) in Artificial Neural Networks (NNs). A standard neural network (NN) consists of many simple, connected processors called neurons, each producing a sequence of real-valued activations An MIT Press book Ian Goodfellow, Yoshua Bengio and Aaron Courville The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free Deep Learning: A Quick Explanation. Deep learning (sometimes known as deep structured learning) is a subset of machine learning, where machines employ artificial neural networks to process information. Inspired by biological nodes in the human body, deep learning helps computers to quickly recognize and process images and speech DEEP LEARNING LIBRARY FREE ONLINE BOOKS 1. Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville 2. Neural Networks and Deep Learning by Michael Nielsen 3. Deep Learning by Microsoft Research 4. Deep Learning Tutorial by LISA lab, University of Montreal COURSES 1. Machine Learning by Andrew Ng in Coursera 2 Deep Learning vs Machine Learning: A Simple Explanation of the difference between deep learning vs machine learning and deep learning for dummies. Deep learning is a subset of artificial intelligence involved with the creation of algorithms which can modify itself without human intervention to produce desired output- by feeding itself through.

Dive into Deep Learning is a hands-on guide that provides a road map for building capacity in teachers, schools, districts and systems to design Deep Learning, measure progress, and assess conditions needed to activate and sustain innovation. Order Now. Join us at the DLL - Register Now Bayesian deep reinforcement learning, Deep learning with small data, Deep learning in Bayesian modelling, Probabilistic semi-supervised learning techniques, Active learning and Bayesian optimisation for experimental design, Kernel methods in Bayesian deep learning, Implicit inference, Applying non-parametric methods, one-shot learning, and. Dive into Deep Learning. Interactive deep learning book with code, math, and discussions. Implemented with NumPy/MXNet, PyTorch, and TensorFlow. Adopted at 175 universities from 40 countries Deep Learning has recently found a number of useful applications. Deep learning is already changing a number of organizations and is projected to bring about a revolution in practically all industries, from Netflix's well-known movie recommendation system to Google's self-driving automobiles This works for less number of layers, but when we increase the number of layers, there is a common problem in deep learning associated with that called Vanishing/Exploding gradient. This causes the gradient to become 0 or too large

Deep Learning, as a branch of Machine Learning, employs algorithms to process data and imitate the thinking process, or to develop abstractions. Deep Learning (DL) uses layers of algorithms to process data, understand human speech, and visually recognize objects Deep Learning is a subset of machine learning where artificial neural networks, algorithms based on the structure and functioning of the human brain, learn from large amounts of data to create patterns for decision-making. Neural networks with various (deep) layers enable learning through performing tasks repeatedly and tweaking them a little. Deep learning is a subfield of machine learning where concerned algorithms are inspired by the structure and function of the brain called artificial neural networks. All the value today of deep learning is through supervised learning or learning from labelled data and algorithms. Each algorithm in deep learning goes through the same process Deep Learning can also learn from the mistakes that occur, thanks to its hierarchy structure of neural networks, but it needs high-quality data. Machine Learning needs less computing resources, data, and time. Deep learning needs more of them due to the level of complexity and mathematical calculations used, especially for GPUs The Deep learning book is more of a handbook to refer back to for deeper understanding and reliable information from a mathematical perspective. Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, Aaron Courville. This book introduces a broad range of topics in deep learning theory El Deep Learning es una técnica de aprendizaje automático que enseña a los ordenadores a hacer lo que resulta natural para las personas: aprender mediante ejemplos. El Deep Learning es una tecnología clave presente en los vehículos sin conductor que les permite reconocer una señal de stop o distinguir entre un peatón y una farola