Welcome to the Python for Time Series - Data Analysis & Forecasting course. This course is built for students who wants to learn python applications for time series data sets. This course covers the usage of Python libraries on time series data. There will be both short lectures of statistics and Python fundamentals at the starting of the course in order to remembering the basics. Then the libraries of Python which is used for time series data will be covered.

Are you looking for the Best Self-Driving Car Courses?… If yes, you should check these listed 5 Best Self-Driving Car Courses. A self-driving car is a vehicle that uses a combination of sensors, cameras, radar, and artificial intelligence (AI) to travel between destinations without a human operator. Now, without further ado, let's get started and find the Best Self-Driving Car Courses. This Udacity Self Driving Car Nanodegree Program is a good mixture of practical exercises & content to gain skills across a wide array of critical topics, including computer vision, sensor fusion, localization, motion control, and more.

Partial differential equations (PDEs) are central to describing complex physical system simulations. Their expensive solution techniques have led to an increased interest in deep neural network based surrogates. However, the practical utility of training such surrogates is contingent on their ability to model complex multi-scale spatio-temporal phenomena. Various neural network architectures have been proposed to target such phenomena, most notably Fourier Neural Operators (FNOs), which give a natural handle over local & global spatial information via parameterization of different Fourier modes, and U-Nets which treat local and global information via downsampling and upsampling paths. However, generalizing across different equation parameters or time-scales still remains a challenge. In this work, we make a comprehensive comparison between various FNO, ResNet, and U-Net like approaches to fluid mechanics problems in both vorticity-stream and velocity function form. For U-Nets, we transfer recent architectural improvements from computer vision, most notably from object segmentation and generative modeling. We further analyze the design considerations for using FNO layers to improve performance of U-Net architectures without major degradation of computational cost. Finally, we show promising results on generalization to different PDE parameters and time-scales with a single surrogate model. Source code for our PyTorch benchmark framework is available at https://github.com/microsoft/pdearena.

Unsupervised region representation learning aims to extract dense and effective features from unlabeled urban data. While some efforts have been made for solving this problem based on multiple views, existing methods are still insufficient in extracting representations in a view and/or incorporating representations from different views. Motivated by the success of contrastive learning for representation learning, we propose to leverage it for multi-view region representation learning and design a model called ReMVC (Region Embedding with Multi-View Contrastive Learning) by following two guidelines: i) comparing a region with others within each view for effective representation extraction and ii) comparing a region with itself across different views for cross-view information sharing. We design the intra-view contrastive learning module which helps to learn distinguished region embeddings and the inter-view contrastive learning module which serves as a soft co-regularizer to constrain the embedding parameters and transfer knowledge across multi-views. We exploit the learned region embeddings in two downstream tasks named land usage clustering and region popularity prediction. Extensive experiments demonstrate that our model achieves impressive improvements compared with seven state-of-the-art baseline methods, and the margins are over 30% in the land usage clustering task.

A place to collaborate and share lecture notes on all topics related to machine learning, NLP, and AI. If you encounter any problems with the notes? If you have any questions, open an issue or reach out to me on Twitter.

Coursera Deep Learning Specialization provides an introduction to DL methods for computer vision applications for practitioners who are familiar with the basics of DL. You will discover a breakdown and review of the convolutional neural networks course taught by Andrew Ng on deep learning specialization. It does not focus too much on math and does not include any code. After finishing the specialization you will know how to build models for photo classification, object detection, face recognition, and more. Instructors patiently explain the requisite math and programming concepts in a carefully planned order for learners who could be rusty in math/coding.

For deep learning and artificial intelligence, Tensorflow is the most popular library built by Google. Many AI and Machine Learning companies choose it over other libraries to achieve their goals. To put it simply, if you want to do Deep Learning, you'll need Tensorflow. Therefore, I have created this list of the best TensorFlow courses for developers who want to learn this machine learning library and deep learning framework. I have also created a detailed comparison between TensorFlow and Keras, if you want to check it out, you can check it out here.

In this post, you will learn to clarify business problems & constraints, understand problem statements, select evaluation metrics, overcome technical challenges, and design high-level systems.

This course is about the discussion of sampling and exploring data, as well as basic probability theory and Bayes' rule. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. The concepts and techniques you will find in this course will serve as building blocks for the inference and modeling courses in the Specialization.

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