Instructional Material

Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more! This comprehensive course by Jose Portilla will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy! You will learn how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python!

This KDnuggets post will get your feet wet in the world of autonomous vehicle algorithms, providing an introductory insight into what to expect if you travel down this road, pun intended. Recent years have witnessed amazing progress in AI related fields such as computer vision, machine learning and autonomous vehicles. The purpose of this project is to use Python to play Grand Theft Auto 5. Check out the accompanying code here: Explorations of Using Python to play Grand Theft Auto 5.

In the case of a sequence prediction, this model would produce one time step forecast for each observed time step received as input. A many-to-one model produces one output value after receiving multiple input values. A many-to-many model produces multiple outputs after receiving multiple input values. For example, a model that takes as input one time step of temperature and pressure and predicts one time step of temperature and pressure is a one-to-one model, not a many-to-many model.

About this course: This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google is currently blocked in China).

Prior to our paper coming out, the main source of AI bias people talked about was seen as a consequence of the lack of diversity of AI developers. That is, the second source of AI bias is poorly-selected training data for machine learning, or poorly reasoned rules. Every intelligent artefact has system design behind it. Quite a lot of the algorithms that affect people's lives are just macros someone programmed in a spread sheet -- macros they may claim are proprietary.

Massive Open Online Courses (MOOCs) are a good starting point, with a lot to offer. The article entitled "Top Machine Learning MOOCs and Online Lectures: A Comprehensive Survey" lists a number of good resources. For example, the MXNet website lists a number of data set sources for CNNs and RNNs. Intel's Python-based Neon framework from Nervana, now an Intel company, supports platforms like Apache Spark, TensorFlow, Caffe, and Theano.

The data for your sequence prediction problem probably needs to be scaled when training a neural network, such as a Long Short-Term Memory recurrent neural network. Running the example prints the sequence, prints the mean and standard deviation estimated from the sequence, prints the standardized values, then prints the values back in their original scale. You must ensure that the scale of your output variable matches the scale of the activation function (transfer function) on the output layer of your network. In this tutorial, you discovered how to scale your sequence prediction data when working with Long Short-Term Memory recurrent neural networks.

It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems. We start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then we introduce Artificial Neural Networks and explain how they are trained to solve Regression and Classification problems. At the end of the course you'll be able to recognize which problems can be solved with Deep Learning, you'll be able to design and train a variety of Neural Network models and you'll be able to use cloud computing to speed up training and improve your model's performance.

This training is ideal for engineers creating algorithms and software for visual machine perception in all types of applications (e.g. It's also appropriate for managers who want to get a flavor for creating deep neural networks and using TensorFlow. After the training, attendees will be ready to begin using TensorFlow productively in their work. In addition, for attendees who require an introduction to deep neural network algorithms, the Embedded Vision Alliance will offer a two-hour video tutorial presentation online prior to the TensorFlow class.

This book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008. The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 46 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as statistical learning; probability and stochastic processes; boosting and experts; active and query learning; and inductive inference.