The Planet dataset has become a standard computer vision benchmark that involves classifying or tagging the contents satellite photos of Amazon tropical rainforest. The dataset was the basis of a data science competition on the Kaggle website and was effectively solved. Nevertheless, it can be used as the basis for learning and practicing how to develop, evaluate, and use convolutional deep learning neural networks for image classification from scratch. This includes how to develop a robust test harness for estimating the performance of the model, how to explore improvements to the model, and how to save the model and later load it to make predictions on new data. In this tutorial, you will discover how to develop a convolutional neural network to classify satellite photos of the Amazon tropical rainforest. How to Develop a Convolutional Neural Network to Classify Satellite Photos of the Amazon Rainforest Photo by Anna & Michal, some rights reserved. The "Planet: Understanding the Amazon from Space" competition was held on Kaggle in 2017. The competition involved classifying small squares of satellite images taken from space of the Amazon rainforest in Brazil in terms of 17 classes, such as "agriculture", "clear", and "water". Given the name of the competition, the dataset is often referred to simply as the "Planet dataset". The color images were provided in both TIFF and JPEG format with the size 256 256 pixels. A total of 40,779 images were provided in the training dataset and 40,669 images were provided in the test set for which predictions were required. The problem is an example of a multi-label image classification task, where one or more class labels must be predicted for each label. This is different from multi-class classification, where each image is assigned one from among many classes.
I recently was a guest speaker at the Stanford AI Salon on the topic of accessiblity in AI, which included a free-ranging discussion among assembled members of the Stanford AI Lab. There were a number of interesting questions and topics, so I thought I would share a few of my answers here. Q: What 3 things would you most like the general public to know about AI? AI is easier to use than the hype would lead you to believe. In my recent talk at the MIT Technology Review conference, I debunked several common myths that you must have a PhD, a giant data set, or expensive computational power to use AI. Most AI researchers are not working on getting computers to achieve human consciousness.
A free online course in artificial intelligence (AI) created by the University of Helsinki and technology consultancy Reaktor has drawn 140,000 students from around the world. Launched in spring 2018, the Elements of AI is available in English and Finnish. It was originally envisioned with the ambitious goal of training one percent of the Finnish population -- 55,000 people -- in the fundamentals of AI. Inspired by the Finnish model, Sweden and the Netherlands have created similar courses, with 15 other countries interested in developing comparable course for their citizens. Part of the course's popularity is the fact that it's available online for free and doesn't require any prerequisite technology skills.
Udemy Online Course - Deep learning Calculus - Data Science - Machine Learning AI Mastering Calculus for Deep learning / Machine learning / Data Science / Data Analysis / AI using Python You start by learning the definition of function and move your way up for fitting the data to the function which is the core for any Machine learning, Deep Learning, Artificial intelligence, Data Science Application. Once you have mastered the concepts of this course, you will never be blind while applying the algorithm to your data, instead you have the intuition as how each code is working in background. What you'll learn Build Mathematical intuition especially Calculus required for Deep learning, Data Science and Machine Learning The Calculus intuition required to become a Data Scientist / Machine Learning / Deep learning Practitioner How to take their Data Science / Machine Learning / Deep learning career to the next level Hacks, tips & tricks for their Data Science / Machine Learning / Deep learning career Implement Machine Learning / Deep learning Algorithms better Learn core concept to Implement in Machine Learning / Deep learning Who this course is for: Data Scientists who wish to improve their career in Data Science. Deep learning / Machine learning practitioner who wants to take the career to next level Any one who wants to understand the underpinnings of Maths in Data Science, Machine Learning, Deep Learning and Artificial intelligence Any Data Science / Machine Learning / Deep learning enthusiast Any student or professional who wants to start or transition to a career in Data Science / Machine Learning / Deep learning Students who want to refresh and learn important maths concepts required for Machine Learning, Deep Learning & Data Science. Data Scientists who wish to improve their career in Data Science.
Deriving insights from data is central to problem solving, innovation and growth. But without an understanding of which approaches to use, and how to interpret and communicate results, the best opportunities will remain undiscovered. Statistical Thinking for Industrial Problem Solving (STIPS) is a free, online course for anyone interested in building practical skills in using data to solve problems better.
Supervised machine learning is often described as the problem of approximating a target function that maps inputs to outputs. This description is characterized as searching through and evaluating candidate hypothesis from hypothesis spaces. The discussion of hypotheses in machine learning can be confusing for a beginner, especially when "hypothesis" has a distinct, but related meaning in statistics (e.g. In this post, you will discover the difference between a hypothesis in science, in statistics, and in machine learning. A Gentle Introduction to Hypotheses in Machine Learning Photo by Bernd Thaller, some rights reserved.
Coursera Machine Learning Course is offered by Stanford University with a rating of 4.9 out of 5. More than 2.2 million students are already enrolled in this course. This online course has over 25K reviews. After doing this course, 40% started a new career and 37% got a tangible career benefit from this course. You can complete this course 100% online with your flexible schedule.
It is challenging to know how to best prepare image data when training a convolutional neural network. This involves both scaling the pixel values and use of augmentation techniques during both the training and evaluation of the model. Instead of testing a wide range of options, a useful shortcut is to consider the types of data preparation, train-time augmentation, and test-time augmentation used by state-of-the-art models that notably achieve the best performance on a challenging computer vision dataset, namely the Large Scale Visual Recognition Challenge, or ILSVRC, that uses the ImageNet dataset. In this tutorial, you will discover best practices for preparing and augmenting photographs for image classification tasks with convolutional neural networks. Best Practices for Preparing and Augmenting Image Data for Convolutional Neural Networks Photo by Mark in New Zealand, some rights reserved.
Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered in several books and surveys. This book provides a more introductory, textbook-like treatment of the subject. Each chapter tackles a particular line of work, providing a self-contained, teachable technical introduction and a review of the more advanced results. The chapters are as follows: Stochastic bandits; Lower bounds; Bayesian Bandits and Thompson Sampling; Lipschitz Bandits; Full Feedback and Adversarial Costs; Adversarial Bandits; Linear Costs and Semi-bandits; Contextual Bandits; Bandits and Zero-Sum Games; Bandits with Knapsacks; Incentivized Exploration and Connections to Mechanism Design.