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Deep Reinforcement Learning 2.0

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Online Courses Udemy - Deep Reinforcement Learning 2.0, The smartest combination of Deep Q-Learning, Policy Gradient, Actor Critic, and DDPG Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team English [Auto] Students also bought Unsupervised Deep Learning in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Data Science: Natural Language Processing (NLP) in Python Recommender Systems and Deep Learning in Python Cutting-Edge AI: Deep Reinforcement Learning in Python Ensemble Machine Learning in Python: Random Forest, AdaBoost Preview this course GET COUPON CODE Description Welcome to Deep Reinforcement Learning 2.0! In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field). To approach this model the right way, we structured the course in three parts: Part 1: Fundamentals In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more.


The Acclimation and Legality of Superior Machines

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We live in a world where we are constantly in contact with Artificial Intelligence, perhaps without even being aware. We live in a world where we are constantly in contact with Artificial Intelligence, perhaps without even being aware. It may not seem that way due to the stigma that Hollywood has put into our mind about what exactly Artificial Intelligence is (killer robots, omniscient software, etc.) but it's really a lot simpler than that. John McCarthy (2007) defined Artificial Intelligence as the science and engineering of making intelligent [having the computational ability to achieve goals in the world] machines. Right now, the main way in which these machines "learn" is through rote learning (trail and error) and drawing inferences. It is widely believed that "AI [artificial intelligence] will drive the human race" (Prime Minister Navendra Modi) and there is not true evidence for or against the contrary, but it is widely accepted that A.I. does and will have a extreme influence on day to day life.


Machine Learning Algorithms For Beginners with Code Examples in Python

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Machine learning (ML) is rapidly changing the world, from diverse types of applications and research pursued in industry and academia. Machine learning is affecting every part of our daily lives. From voice assistants using NLP and machine learning to make appointments, check our calendar and play music, to programmatic advertisements -- that are so accurate that they can predict what we will need before we even think of it. More often than not, the complexity of the scientific field of machine learning can be overwhelming, making keeping up with "what is important" a very challenging task. However, to make sure that we provide a learning path to those who seek to learn machine learning, but are new to these concepts.


Natural Language Processing (NLP) with Python: 2020

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Bestseller Created by Ankit Mistry, Vijay Gadhave, Data Science & Machine Learning Academy English [Auto] Students also bought Unsupervised Deep Learning in Python Recommender Systems and Deep Learning in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Deep Learning: GANs and Variational Autoencoders Unsupervised Machine Learning Hidden Markov Models in Python Machine Learning and AI: Support Vector Machines in Python Preview this course GET COUPON CODE Description Recent reviews: "Very practical and interesting, Loved the course material, organization and presentation. Thank you so much" "This is the best course to learn NLP from the basic. According to statista dot com which field of AI is predicted to reach $43 billion by 2025? If answer is'Natural Language Processing', You are at right place. How Android speech recognition recognize your voice with such high accuracy.


The evolution of work--seven new realities

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Getting work done is a fundamental concern for any business. But today, paradigm-shifting forces seem to be driving significant changes in both work and the workforce. New digital and communications technologies are changing how work gets done. The growth of the gig economy and advances in artificial intelligence are changing who does the work. Even the question of what work looks like is coming under examination as a continually evolving marketplace drives organizations to explore new business models. In the face of these technological and social forces, it could be imperative for businesses to rethink their approaches to the how, who, and what of work in fundamental, perhaps even transformative ways. And as usual, there seem to be no easy answers.


Don't Forget what 'Deep' & 'Learning' Actually Mean

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Think critically about whether you need to apply deep-learning to your datasets. Deep Learning, one of the "hottest" things in AI, has a way of seeping into popular culture as this mysterious, software that can make seemingly amazing classifications at human-level accuracy in Computer Vision, speech recognition, or play games like Go, recommend our favorite movies, and the like. But deep learning has crucial pitfalls, when it drives cars that sadly, more than once, have injured or killed their drivers or pedestrians because of silly image-recognition mistakes. Or, when deep learning is used for face-recognition ––something that clearly has adverse effects on people of color, LGBT, and other marginalized groups –– and if deep learning's face-prediction is used by institutions of power with a history of racism, LGBT-phobia, and tossed back and forth between private companies and governments –– deep-learning's pitfalls become frighteningly magnified. Another example is when Facebook's deep-learning neural translation machine led to the illegal arrest of a Palestinian man because of a post he made, at the end of 2017.


Unsupervised Deep Learning in Python

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Online Courses Udemy Unsupervised Deep Learning in Python, Theano / Tensorflow: Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA Created by Lazy Programmer Inc. Students also bought Advanced AI: Deep Reinforcement Learning in Python Deep Learning: Recurrent Neural Networks in Python Ensemble Machine Learning in Python: Random Forest, AdaBoost Deep Learning: GANs and Variational Autoencoders Deep Learning Prerequisites: Linear Regression in Python Machine Learning and AI: Support Vector Machines in Python Preview this course GET COUPON CODE Description This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.


Continual AI [Newsletter]

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Connectionist networks in which information is stored in weights on connections among simple processing units have attracted considerable interest in cognitive science. First, the weights on connections between units need not be prewired by the model builder but rather may be established through training in which items to be learned are presented repeatedly to the network and the connection weights are adjusted in small increments according to a learning algorithm. Second, the networks may represent information in a distributed fashion. Distributed representations established through the application of learning algorithms have several properties that are claimed to be desirable from the standpoint of modeling human cognition. These properties include content-addressable memory and so-called automatic generalization in which a network trained on a set of items responds correctly to other untrained items within the same domain.


Facebook & Inria Propose High-Performance Self-Supervised Technique for CV Tasks

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Researchers from Facebook and the French National Institute for Research in Digital Science and Technology (Inria) have developed a new technique for self-supervised training of convolutional networks used for image classification and other computer vision tasks. The proposed method surpasses supervised techniques on most transfer tasks and outperforms previous self-supervised approaches. "Our approach allows researchers to train efficient, high-performance image classification models with no annotations or metadata," the researchers write in a Facebook blog post. "More broadly, we believe that self-supervised learning is key to building more flexible and useful AI." Recent improvements in self-supervised training methods have established them as a serious alternative to traditional supervised training. Self-supervised approaches however are significantly slower to train compared to their supervised counterparts.


Deep Learning in Finance: Is This The Future of the Financial Industry?

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Is Deep Learning now leading the charge for innovation in finance? Computational Finance, Machine Learning, and Deep Learning have been essential components of the finance sector for many years. The development of these techniques, technologies, and skills have enabled the financial industry to achieve explosive growth over the decades and become more efficient, sharp, and lucrative for its participants. Will this continue to be what drives the future of the financial industry? With the newer deep learning focus, people driving the financial industry have had to adapt by branching out from an understanding of theoretical financial knowledge.