Ping An Insurance (Group) Company of China, Ltd is the first joint-stock insurance enterprise in China. It has developed into an integrated services conglomerate that blends its financial services such as finance and insurance, banking, and investment management to create an integrated, compact, and diversified business profile. In 2017, Ping An ranked first among the world's 100 most valuable insurance brands. Ping An Technology was founded in 2008 as a wholly-owned subsidiary of Ping An Group. It is mainly responsible for developing and operating critical platforms and services that support the efficient development of insurance, banking, investment and internet businesses of the Group.

Your smartphone, smartwatch, and automobile (if it is a newer model) have AI (Artificial Intelligence) inside serving you every day. In the near future, more advanced "self-learning" capable DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of your business and industry. So now is the right time to learn what DL and ML is and how to use it in advantage of your company. This course has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open source DL software, which are the future enablers. The second part focuses on the core technologies of DL and ML systems, which include NN (Neural Network), CNN (Convolutional NN), and RNN (Recurrent NN) systems.

In this thesis we study a perception problem in the context of autonomous driving. Specifically, we study the computer vision problem of 3D object detection, in which objects should be detected from various sensor data and their position in the 3D world should be estimated. We also study the application of Generative Adversarial Networks in domain adaptation techniques, aiming to improve the 3D object detection model's ability to transfer between different domains. The state-of-the-art Frustum-PointNet architecture for LiDAR-based 3D object detection was implemented and found to closely match its reported performance when trained and evaluated on the KITTI dataset. The architecture was also found to transfer reasonably well from the synthetic SYN dataset to KITTI, and is thus believed to be usable in a semi-automatic 3D bounding box annotation process.

Hybrid approach: Semantic AI is the combination of methods derived from symbolic AI and statistical AI. Virtuously playing the AI piano means that for a given use case various stakeholders, not only data scientists, but also process owners or subject matter experts, choose from available methods and tools, and collaboratively develop workflows that are most likely a good fit to tackle the underlying problem. For example, one can combine entity extraction based on machine learning with text mining methods based on semantic knowledge graphs and related reasoning capabilities to achieve the optimal results. Data Quality: Semantically enriched data serves as a basis for better data quality and provides more options for feature extraction. This results in higher precision of prediction & classification calculated by machine learning algorithms.

In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE. After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a spam detector.

Word2Vec model is used for learning vector representations of words called "word embeddings". This is typically done as a preprocessing step, after which the learned vectors are fed into a discriminative model (typically an RNN) to generate predictions and perform all sort of interesting things. Image and audio processing systems work with rich, high-dimensional datasets encoded as vectors of the individual raw pixel-intensities for image data, thus all the information is encoded in the data hence the relation between various entities in the system like (cats and dogs) can be established. But, when it comes to natural language processing systems traditionally it treats words as discrete atomic symbols, and therefore'cat' may be represented as Id537 and'dog' as Id143.These encodings are arbitrary, and provide no useful information to the system regarding the relationships that may exist between the individual symbols. This means that the model can leverage very little of what it has learned about'cats' when it is processing data about'dogs' (such that they are both animals, four-legged, pets, etc.).

They always say that don't compare apples to oranges. But how about if we're comparing one mix of apples and oranges with another mix of apples and oranges, but the distribution is different.

Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems. In this course you will investigate the challenges of working with large datasets: how to implement algorithms that work; how to use databases to manage your data; and how to learn from your data with machine learning tools. The focus is on practical skills - all the activities will be done in Python 3, a modern programming language used throughout astronomy.

Extraction of good features is integral for the efficient performance of a machine learning model. Input data may be large or it may be redundant or both, so their should efficient feature extraction technique which will transform input into a reduced set of features. In this post let's talk about feature extraction for textual data. Let's look at the two methods implemented in sk-lean for doing this: One tricky thing is that machines are not good with text as compared to numerical values. So it is important to represent these text in a vector form so that efficiency will increased.

This is also an excellent book.