Neural Networks: Instructional Materials


Regression with Keras - PyImageSearch

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In this tutorial, you will learn how to perform regression using Keras and Deep Learning. You will learn how to train a Keras neural network for regression and continuous value prediction, specifically in the context of house price prediction. Today's post kicks off a 3-part series on deep learning, regression, and continuous value prediction. We'll be studying Keras regression prediction in the context of house price prediction: Unlike classification (which predicts labels), regression enables us to predict continuous values. For example, classification may be able to predict one of the following values: {cheap, affordable, expensive}.


Deep Learning : Plunge into Deep Learning

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Then this course is for you! This course is designed in a very simple and easily understandable content. You might have seen lots of buzz on deep learning and you want to figure out where to start and explore. This course is designed exactly for people like you! If basics are strong, we can do bigger things with ease.


Learn the secrets of AI on the cheap with this online course sale

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The robots are coming, if not already here: A 2017 study conducted by the McKinsey Global Institute revealed that up to 800 million jobs worldwide are at risk of being replaced by automation by 2030. According to our calculations, you have two options as far as the machine apocalypse goes: One, you could wait anxiously until your job is inevitably stolen by a C-3PO wannabe, or two, you could one-up the robots by training to work in the field of Artificial Intelligence. Let's suppose you go with the second option (probably the wiser choice, all things considered). Now, how do you go about getting said AI training? A good place to start is -- where else -- online.


Machine Learning: Build a neural network in 77 lines of code

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How to build a neural network in 77 lines of Python code. From Google Translate to Netflix recommendations, neural networks are increasingly being used in our everyday lives. One day neural networks may operate self driving cars or even reach the level of artificial consciousness. As the machine learning revolution grows, demand for machine learning engineers grows with it. Machine learning is a lucrative field to develop your career.


Introduction to Artificial Neural Network and Deep Learning

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This course is an introduction to Neural Networks, so you need absolutely no prior knowledge in Artificial Intelligence, Machine Learning, and AI. However, you need to have basic understanding of programming specially in Java to easily follow the coding video. If you just want to lean the mathematical model and the problem solving process using Neural Networks, you can then skip the coding videos. Machine learning is an extremely hot area in Artificial Intelligence and Data Science. There is no doubt that Neural Networks are the most well-regarded and widely used machine learning techniques.


How to Accelerate Learning of Deep Neural Networks With Batch Normalization

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Batch normalization is a technique designed to automatically standardize the inputs to a layer in a deep learning neural network. Once implemented, batch normalization has the effect of dramatically accelerating the training process of a neural network, and in some cases improves the performance of the model via a modest regularization effect. In this tutorial, you will discover how to use batch normalization to accelerate the training of deep learning neural networks in Python with Keras. How to Accelerate Learning of Deep Neural Networks With Batch Normalization Photo by Angela and Andrew, some rights reserved. Keras provides support for batch normalization via the BatchNormalization layer.


Autonomous Cars: Deep Learning and Computer Vision in Python

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The automotive industry is experiencing a paradigm shift from conventional, human-driven vehicles into self-driving, artificial intelligence-powered vehicles. Self-driving vehicles offer a safe, efficient, and cost effective solution that will dramatically redefine the future of human mobility. Self-driving cars are expected to save over half a million lives and generate enormous economic opportunities in excess of $1 trillion dollars by 2035. The automotive industry is on a billion-dollar quest to deploy the most technologically advanced vehicles on the road. As the world advances towards a driverless future, the need for experienced engineers and researchers in this emerging new field has never been more crucial.


Learning to Employment: Best Resources for Data Science, Machine Learning, Deep Learning & AI

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Buy the guide with confidence and without fear. This guide was written to help you. If you're not happy, email team@datascienceweekly.org and you'll receive a refund.


Deep Learning - 3 Steps to Create Your CNN with KERAS in Python 2019!

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Deep Learning - 3 Steps to Create Your CNN with KERAS in Python 2019! Do you want to create your convolutional neural network with keras? This keras tutorial show you how to create a keras cnn.


Introduction to Machine Learning with Python

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This is a practical introduction to Machine Learning using Python programming language. Machine Learning allows you to create systems and models that understand large amounts of data. These models support our decision making in a range of fields, including market prediction, within scientific research and statistical analysis. This course will teach you how to use statistical techniques and machine learning algorithms that enable a computer system to learn from different types of data. This is a ten week introductory course in Machine Learning using Python, which is a widely used programming language in the field of Machine Learning.