Visitors watch a robot dance at the World Robot Conference 2017 in Beijing, capital of China, Aug. 25, 2017. Over 300 artificial intelligent (AI) specialists and representatives of over 150 robot enterprises attended the conference. BEIJING, April 7 (Xinhua) -- A government program is training 500 university teachers and 5,000 students in artificial intelligence (AI). The program organized by the Ministry of Education, Peking University and Innovation Works (a Beijing incubator) will continue for five years. Last year, China's cabinet issued an AI development outline, stressing the need to train AI talent and attract the world's leading professionals to China.

In this course, you will learn what hyperparameters are, what Genetic Algorithm is, and what hyperparameter optimization is. In this course, you will apply Genetic Algorithm to optimize the performance of Support Vector Machines and Multilayer Perceptron Neural Networks. Hyperparameter optimization will be done on a regression dataset for the prediction of cooling and heating loads of buildings. The SVM and MLP will be applied on the dataset without optimization and compare their results to after their optimization. By the end of this course, you will have learnt how to code Genetic Algorithm in Python and how to optimize your Machine Learning algorithms for maximal performance.

Welcome to Serverless Data Analysis with Big Query on Google's Cloud This is the second course in a series of courses designed to help you attain the coveted Google Certified Data Engineer. Additionally, the series of courses is going to show you the role of the data engineer on the Google Cloud Platform. At this juncture the Google Certified Data Engineer is the only real world certification for data and machine learning engineers. Note: This is not a programmers course on BigQuery. The goal of this course and the entire series of courses is to provide students with the foundation of the services you'll need to know for the Google Certified Data Engineering Exam.

This video course presents deep learning architectures coded in Python using Keras, a modular neural network library that runs on top of either Google's TensorFlow or Lisa Lab's Theano backends. This video course introduces Generative Adversarial Networks (GANs) that are used to reproduce synthetic data that looks like data generated by humans, and then teach how to forge the MNIST and CIFAR-10 dataset with the help of Keras Adversarial GANs. Practical applications include code for predicting the surrounding words given the current word, sentiment analysis, and synthetic generation of texts. We will learn about a specific form of word embedding word2vec. This embedding has proven more effective and has been widely adopted in the deep learning and NLP communities.

Really well explained, and precisely the right amount of information. Mike provides clear and concise explanations and has a deep subject knowledge of Google's Cloud. Welcome to An Introduction to Machine Learning for Data Engineers. This course is part of my series for data engineering. The course is a prerequisite for my course titled Tensorflow on the Google Cloud Platform for Data Engineers.

TensorFlow is an open source software library for Machine Intelligence. The independent solutions in this video course will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through solutions on training models, model evaluation and sentiment analysis – each using Google's machine learning library TensorFlow.This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow and Caesar's Entertainment.

Created by industry thought leader Ajit Jaokar in 1999, our research is based on two courses conducted by Ajit: Big Data for Telecoms at Oxford University and the newly launched citysciences program program at UPM ( Technical University of Madrid) which apply machine learning techniques to IoT and Smart cities applications. "Great course with many interactions, either group or one to one that helps in the learning. In addition, tailored curriculum to the need of each student and interaction with companies involved in this field makes it even more impactful. As for myself, it allowed me to go into topics of interests that help me in reshaping my career. "This DSIOT course is a great way to get up-to-speed.

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars to spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this course, you'll be introduced to the Natural Processing Language and Recommendation Systems, which help you run multiple algorithms simultaneously. Also, you'll learn about Deep learning and TensorFlow.

In this course we learn that stand alone data analysis is fine but what most companies these days are looking for is to do Predictive analysis using their data. In this advanced course, we will make you ready to start doing Predictive Analysis on your data by showing you how to build Machine Learning models with scikit-learn and pandas. In this course, you will be training models and be making data based predictions using scikit-learn.The user will like this as a standalone product as Making Predictions data using Machine Learning is an absolute minimum skill for any Data Analyst \ Data Scientist these days. We will teach users how to use scikit-learn to make data based predictions. User will learn how to bring in their data using pandas, apply some machine learning models and take out the predictions.

We address the problem of learning hierarchical deep neural network policies for reinforcement learning. Our aim is to design a hierarchical reinforcement learning algorithm that can construct hierarchical representations in bottom-up layerwise fashion. In contrast to methods that explicitly restrict or cripple lower layers of a hierarchy to force them to use higher-level modulating signals, each layer in our framework is trained to directly solve the task, but acquires a range of diverse strategies via a maximum entropy reinforcement learning objective. Each layer is also augmented with latent random variables, which are sampled from a prior distribution during the training of that layer. The maximum entropy objective causes these latent variables to be incorporated into the layer's policy, and the higher level layer can directly control the behavior of the lower layer through this latent space. Furthermore, by constraining the mapping from latent variables to actions to be invertible, higher layers retain full expressivity: neither the higher layers nor the lower layers are constrained in their behavior. Our experimental evaluation demonstrates that we can improve on the performance of single-layer policies on standard benchmark tasks simply by adding additional layers, and that our method can solve more complex sparse-reward tasks by learning higher-level policies on top of high-entropy skills optimized for simple low-level objectives.