The first thing we'll do is to introduce you to TensorFlow, the most widely used machine learning library. By learning the basics and then moving on to some fundamental techniques, you will gain a reasonable understanding of how to use this powerful library in your applications. The next step is to build an actual model. As you go through the process, you will learn how to prepare your data, train the network, and make predictions.

The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don't exist in the real world! This course will teach you the fundamentals of convolution and why it's useful for deep learning and even NLP (natural language processing). You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself. The basics of machine learning and neurons (just a review to get you warmed up!) Neural networks for classification and regression (just a review to get you warmed up!) All of the materials required for this course can be downloaded and installed for FREE.

All machine learning models involve optimization. Most likely, we use computational algorithms to optimize. There are many ways to optimize numerically. SciPy has a number of functions handy for this. We can also try to implement the optimization algorithms on our own. In this crash course, you will discover how you can get started and confidently run algorithms to optimize a function with Python in seven days. This is a big and important post. You might want to bookmark it. Optimization for Machine Learning (7-Day Mini-Course) Photo by Brewster Malevich, some rights reserved.

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilised efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organisations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory.

Become a Python Pro with these valuable skills. Find the right instructor for you. Choose from many topics, skill levels, and languages. This course is designed to provide a complete introduction to Deep Learning. 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.

Principal component analysis (PCA) is an unsupervised machine learning technique. Perhaps the most popular use of principal component analysis is dimensionality reduction. Besides using PCA as a data preparation technique, we can also use it to help visualize data. A picture is worth a thousand words. With the data visualized, it is easier for us to get some insights and decide on the next step in our machine learning models.

After the article in Nature about the open-source of AlphaFold v2.0 on GitHub by DeepMind, many in the scientific and research community have wanted to try out DeepMind's AlphaFold implementation firsthand. With compute resources through Amazon Elastic Compute Cloud (Amazon EC2) with Nvidia GPU, you can quickly get AlphaFold running and try it out yourself. In this post, I provide you with step-by-step instructions on how to install AlphaFold on an EC2 instance with Nvidia GPU. The process starts with a Deep Learning Amazon Machine Image (DLAMI). After installation, we run predictions using the AlphaFold model with CASP14 samples on the instance.

In this course, we look at core Linear Algebra concepts and how it can be used in solving real world problems. We shall go through core Linear Algebra topics like Matrices, Vectors and Vector Spaces. If you are interested in learning the mathematical concepts in linear algebra, but also want to apply those concepts to datascience, statistics, finance, engineering, etc.then this course is for you! We shall explain detaily all Maths Concepts and also implement them programmaticaly in Python. We lay much emphasis on feedback.

This course is designed for beginners to machine learning. Some of the most exciting advances in artificial intelligence have occurred by challenging neural networks to play games. I will introduce the concept of reinforcement learning, by teaching you to code a neural network in Python capable of delayed gratification. We will use the NChain game provided by the Open AI institute. The computer gets a small reward if it goes backwards, but if it learns to make short term sacrifices by persistently pressing forwards it can earn a much larger reward.

TensorFlow 2.0 has just been released, and it introduced many features that simplify the model development and maintenance processes. From the educational side, it boosts people's understanding by simplifying many complex concepts. From the industry point of view, models are much easier to understand, maintain, and develop. Deep Learning is one of the fastest growing areas of Artificial Intelligence. In the past few years, we have proven that Deep Learning models, even the simplest ones, can solve very hard and complex tasks.