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Deep Learning Algorithms - The Complete Guide

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Deep Learning is eating the world. The hype began around 2012 when a Neural Network achieved super human performance on Image Recognition tasks and only a few people could predict what was about to happen. During the past decade, more and more algorithms are coming to life. More and more companies are starting to add them in their daily business. Here, I tried to cover all the most important Deep Learning algorithms and architectures concieved over the years for use in a variety of applications such as Computer Vision and Natural Language Processing. Some of them are used more frequently than others and each one has its own streghth and weeknesses. My main goal is to give you a general idea of the field and help you understand what algorithm should you use in each specific case. Because I know it seems chaotic for someone who wants to start from scratch.


A Guide to Real World Artificial Intelligence & Machine Learning Use Cases

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Machine learning and artificial intelligence are driving major changes in the global economy. This article looks at the ways in which firms across the various sectors of the economy adopt Artificial Intelligence (AI) techniques. However, before we review the sectors affected it is important to note the underlying drivers that are fuelling the growth in the influence and reach of Machine Learning across the sectors of the economy will only grow as we move forwards. This is because Big Data is only getting larger, velocity of data faster, plus the availability of cheaper data storage plus the arrival of powerful Graphical Processing Units (GPUs) to enable Deep Learning algorithms to be deployed. Furthermore, new research in areas of Deep Learning and other Machine Learning areas will continue to emerge into real world production over the next few years leading to new opportunities and applications.


The Complete Neural Networks Bootcamp: Theory, Applications

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In this section, we will introduce the deep learning framework we'll be using through this course, which is PyTorch. We will show you how to install it, how it works and why it's special, and then we will code some PyTorch tensors and show you some operations on tensors, as well as show you Autograd in code!


GitHub - lucidrains/vit-pytorch: Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

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Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch. There's really not much to code here, but may as well lay it out for everyone so we expedite the attention revolution. For a Pytorch implementation with pretrained models, please see Ross Wightman's repository here. The official Jax repository is here. A recent paper has shown that use of a distillation token for distilling knowledge from convolutional nets to vision transformer can yield small and efficient vision transformers.


The Complete Neural Networks Bootcamp: Theory, Applications

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Including NLP and Transformers Students also bought Recommender Systems and Deep Learning in Python Machine Learning A-Z: Become Kaggle Master Unsupervised Deep Learning in Python Deep Learning: Recurrent Neural Networks in Python Unsupervised Machine Learning Hidden Markov Models in Python Deep Learning: Convolutional Neural Networks in Python Preview this Udemy Course GET COUPON CODE Description This course is a comprehensive guide to Deep Learning and Neural Networks. The theories are explained in depth and in a friendly manner. After that, we'll have the hands-on session, where we will be learning how to code Neural Networks in PyTorch, a very advanced and powerful deep learning framework! We will walk through an example and do the calculations step-by-step. We will also discuss the activation functions used in Neural Networks, with their advantages and disadvantages!


Top Machine Learning Research Papers Released In 2021

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Advances in machine learning and deep learning research are reshaping our technology. Machine learning and deep learning have accomplished various astounding feats this year in 2021, and key research articles have resulted in technical advances used by billions of people. The research in this sector is advancing at a breakneck pace and assisting you to keep up. Here is a collection of the most important recent scientific study papers. The authors of this work examined why ACGAN training becomes unstable as the number of classes in the dataset grows.


Mobile Price Classification - Projects Based Learning

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Bob has started his own mobile company. He wants to give a tough fight to big companies like Apple, Samsung etc. He does not know how to estimate the price of mobiles his company creates. In this competitive mobile phone market, you cannot simply assume things. To solve this problem he collects sales data of mobile phones of various companies.


The Complete Neural Networks Bootcamp: Theory, Applications

#artificialintelligence

In this section, we will introduce the deep learning framework we'll be using through this course, which is PyTorch. We will show you how to install it, how it works and why it's special, and then we will code some PyTorch tensors and show you some operations on tensors, as well as show you Autograd in code!


Comprehensive Guide to Transformers

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You have a piece of paper with text on it, and you want to build a model that can translate this text to another language. How do you approach this? The first problem is the variable size of the text. There's no linear algebra model that can deal with vectors with varying dimensions. The default way of dealing with such problems is to use the bag-of-words Model ( 1).


Artificial intelligence sheds light on how the brain processes language

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In the past few years, artificial intelligence models of language have become very good at certain tasks. Most notably, they excel at predicting the next word in a string of text; this technology helps search engines and texting apps predict the next word you are going to type. The most recent generation of predictive language models also appears to learn something about the underlying meaning of language. These models can not only predict the word that comes next, but also perform tasks that seem to require some degree of genuine understanding, such as question answering, document summarization, and story completion. Such models were designed to optimize performance for the specific function of predicting text, without attempting to mimic anything about how the human brain performs this task or understands language.