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Udemy Machine Learning: Decent course, excellent community

#artificialintelligence

This post is part of "AI education", a series of posts that review and explore educational content on data science and machine learning. When it comes to software development education, I'm a classical type: I prefer books over video tutorials, and I like to manually write every single line of code instead of copy-pasting from sample files and Stack Exchange. My early experience with online artificial intelligence and machine learning courses had mostly left me disappointed. So, when Udemy gave me access to their online course "Machine Learning A-Z: Hands-On Python & R In Data Science," I was a bit skeptical. But after going through the course, I must say that the instructors, Kirill Eremenko and Hadelin de Ponteves, have done a great job to make machine learning, a fairly complicated topic, accessible to a wide audience.


Udemy Free Deep Learning Prerequisites: The Numpy Stack in Python V2

#artificialintelligence

This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2). The reason I made this course is because there is a huge gap for many students between machine learning "theory" and writing actual code. As I've always said: "If you can't implement it, then you don't understand it". Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer. This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.


Deep Learning Prerequisites: The Numpy Stack in Python

#artificialintelligence

Online Courses Udemy - The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence HIGHEST RATED Created by Lazy Programmer Inc English [Auto-generated] Students also bought Data Science: Natural Language Processing (NLP) in Python Recommender Systems and Deep Learning in Python Natural Language Processing with Deep Learning in Python Bayesian Machine Learning in Python: A/B Testing Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Preview this course GET COUPON CODE Description Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python. One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don't know enough about the Numpy stack in order to turn those concepts into code. Even if I write the code in full, if you don't know Numpy, then it's still very hard to read. This course is designed to remove that obstacle - to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science.


48 Best Development Courses Online To Become An Industry Expert JA Directives

#artificialintelligence

Are you craving for the Best Development Courses Online, Tutorial, Training, and Certification? We are here to assist you to grab top courses at a lower price. These online tutorials will help you to get started right now. Get all best online web development courses, app development course, game development course, programming languages like SQL, Java, JavaScript, C, Python, PHP, IOS, Ruby on Rails, full stack web development, and other development courses from one places. If pricing was the bar to learn, this is no more an issue because Udemy is offering new coupons and deals with huge discounts in almost every month.


Use nvidia-docker to create awesome Deep Learning Environments for R (or Python) PT I

#artificialintelligence

How long does it take you to install your complete GPU-enabled deep learning environment including RStudio or jupyter and all your packages? And do you have to do that on multiple systems? In this blog post series I'm going to show you how and why I manage my data science environment with GPU enabled docker containers. How are you managing your data science stack? I was never really satisfied in how I did it.


Ranking Popular Deep Learning Libraries for Data Science

#artificialintelligence

Much of our curriculum is based on feedback from corporate and government partners about the technologies they are using and learning. In addition to their feedback we wanted to develop a data-driven approach for determining what we should be teaching in our data science corporate training and our free fellowship for masters and PhDs looking to enter data science careers in industry. Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The table shows standardized scores, where a value of 1 means one standard deviation above average (average score of 0). For example, Caffe is one standard deviation above average in Github activity, while deeplearning4j is close to average.


LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations

arXiv.org Machine Learning

Reinforcement learning approaches have long appealed to the data management community due to their ability to learn to control dynamic behavior from raw system performance. Recent successes in combining deep neural networks with reinforcement learning have sparked significant new interest in this domain. However, practical solutions remain elusive due to large training data requirements, algorithmic instability, and lack of standard tools. In this work, we introduce LIFT, an end-to-end software stack for applying deep reinforcement learning to data management tasks. While prior work has frequently explored applications in simulations, LIFT centers on utilizing human expertise to learn from demonstrations, thus lowering online training times. We further introduce TensorForce, a TensorFlow library for applied deep reinforcement learning exposing a unified declarative interface to common RL algorithms, thus providing a backend to LIFT. We demonstrate the utility of LIFT in two case studies in database compound indexing and resource management in stream processing. Results show LIFT controllers initialized from demonstrations can outperform human baselines and heuristics across latency metrics and space usage by up to 70%.


Complete iOS 11 Machine Learning Masterclass Udemy

@machinelearnbot

If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you'll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We're approaching a new era where only apps and games that are considered "smart" will survive.


Ranking Popular Deep Learning Libraries for Data Science

@machinelearnbot

Much of our curriculum is based on feedback from corporate and government partners about the technologies they are using and learning. In addition to their feedback we wanted to develop a data-driven approach for determining what we should be teaching in our data science corporate training and our free fellowship for masters and PhDs looking to enter data science careers in industry. Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The table shows standardized scores, where a value of 1 means one standard deviation above average (average score of 0). For example, Caffe is one standard deviation above average in Github activity, while deeplearning4j is close to average.


Deep Learning: GANs and Variational Autoencoders

@machinelearnbot

I am a data scientist, big data engineer, and full stack software engineer. I have a masters degree in computer engineering with a specialization in machine learning and pattern recognition. I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.