About this course: This course provides an unique opportunity for you to learn key components of text mining and analytics aided by the real world datasets and the text mining toolkit written in Java. Hands-on experience in core text mining techniques including text preprocessing, sentiment analysis, and topic modeling help learners be trained to be a competent data scientists. Empowered by bringing lecture notes together with lab sessions based on the y-TextMiner toolkit developed for the class, learners will be able to develop interesting text mining applications.
Much like the rise of electricity, which started about 100 years ago, AI will revolutionize every major industry. Andrew Ng explains how AI can transform your business, shares major technology trends and thoughts on where your biggest future opportunities may lie, and explores best practices for incorporating AI, machine learning, and deep learning into your organization. Follow O'Reilly on: Twitter: http://twitter.com/oreillymedia
Deep learning refers to artificial neural networks that are composed of many layers. You will start by understanding the basics of Deep Learning and Artificial neural Networks and move on to exploring advanced ANN's and RNN's. Starting out at a basic level, users will be learning how to develop and implement Deep Learning algorithms using R in real world scenarios. Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna.
Learn the basics of Deep Learning with hands on exercises using the Caffe deep learning framework and the DIGITS visual interface. Caffe framework is free, open sourced, continuously improved, has good documentation and even has an entire zoo of pre trained deep neural network models for image classification and other computer vision tasks. DIGITS is NVIDIA's tool to help improve the process of designing, debugging and visualizing the inner workings of a deep neural network and works perfectly with Caffe. Students completing the course will have the knowledge and courage to experiment and create amazing, useful and functional Convolutional Deep Learning Networks.
This video aims to help you leverage the power of TensorFlow to perform image processing. Then you will delve into more advanced stuff such as semantic segmentation, Neural Image Caption Generation, and so on, taking advantage of TensorFlow's Deep Neural Networks. Marvin has worked at a deep learning start-up developing neural network architectures. At the forefront of next generation DNA sequencing, he builds intelligent applications with Machine Learning and Deep Learning for precision medicine.
After covering the basics of classification based machine learning using logistic regression, we then move on to more advanced topics covering other classification machine learning algorithms such as Linear Discriminant Analysis, Quadratic Discriminant Analysis, Stochastic Gradient Descent classifier, Nearest Neighbors, Gaussian Naive Bayes and many more. We follow the foundations that we started in the first regression based machine learning course covering cross-validation, model validation, back test, professional Quant work flow, and much more. This course is the second of the Machine Learning for Finance and Algorithmic Trading & Investing Series. If you are looking for a course on applying machine learning to investing, the Machine Learning for Finance and Algorithmic Trading & Investing Series is for you.
Do you want to build complex deep learning models in Keras? Do you want to use neural networks for classifying images, predicting prices, and classifying samples in several categories? Keras is the most powerful library for building neural networks models in Python. After taking this course, you should feel comfortable building neural nets for time sequences, images classification, pure classification and/or regression.
'Our students will develop the software skills and conceptual understanding necessary to build a flight system for an autonomous flight vehicle that can reliably complete complex missions in urban environments,' the firm said. 'Our students will develop the software skills and conceptual understanding necessary to build a flight system for an autonomous flight vehicle that can reliably complete complex missions in urban environments,' the firm wrote. Thrun, who used to work at Google before leaving to set up his flying-vehicle firm, Kitty Hawk, said he envisions a world where he can fly the 34-mile (55 km) journey from Palo Alto to San Francisco in just ten minutes. Thrun, who used to work at Google before leaving to set up his flying-vehicle firm, Kitty Hawk, said he envisions a world where he can fly the 34-mile (55 km) journey from Palo Alto to San Francisco in just ten minutes.
This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We show you how one might code their own logistic regression module in Python. If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you want more than just a superficial look at machine learning models, this course is for you.