I like where this line of thoughts goes: functional programming means functional composability. Decoupled deep learning modules is an exciting research area: Decoupled Neural Interfaces using Synthetic Gradients has shown, for example, very promising results [22] I am not sure if the term Differentiable Programming will stick around. The risk of confusion with Differential Dynamic Programming is high. The idea, on the other hand, is intriguing. Very intriguing and I am very happy to see projects such as Tensorlang [17] gaining traction. Wired argued that soon we won't program computers. We'll train them like dogs.

Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deep learning Tensorflow, CNTK, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular, and extensible. This course provides a comprehensive expert level details in deep learning(Keras). We start by a brief recap of the most common concepts found in machine learning.

You are a data scientist. Every day, you stare at reams of data trying to apply the latest and brightest of models to uncover new insights, but there seems to be an endless supply of obstacles. Your colleagues depend on you to monetize your firm's data - and the clock is ticking. Troubleshooting Python Machine Learning is the answer. We have systematically researched common ML problems documented online around data wrangling, debugging models such as Random Forests and SVMs, and visualizing tricky results.

With the increase of machine learning usage by industries and scientific communities in a variety of tasks such as text mining, image recognition and self-driving cars, automatic setting of hyper-parameter in learning algorithms is a key factor for achieving satisfactory performance regardless of user expertise in the inner workings of the techniques and methodologies. In particular, for a reinforcement learning algorithm, the efficiency of an agent learning a control policy in an uncertain environment is heavily dependent on the hyper-parameters used to balance exploration with exploitation. In this work, an autonomous learning framework that integrates Bayesian optimization with Gaussian process regression to optimize the hyper-parameters of a reinforcement learning algorithm, is proposed. Also, a bandits-based approach to achieve a balance between computational costs and decreasing uncertainty about the Q-values, is presented. A gridworld example is used to highlight how hyper-parameter configurations of a learning algorithm (SARSA) are iteratively improved based on two performance functions.

Linear regression is one of the essential tools in statistical analysis. In this course, we'll walk through step-by-step how to conduct many important analyses using SPSS. Although you will learn the basics of what these statistics are, we'll avoid complicated mathematical discussions and go right to what you need to know to conduct these analyses. Linear regression is basically a tool that allows you to test relationships between many variables at the same time, control for variables' effects, and create simple statistical models that allow you to make predictions. In this course, we'll cover the following key topics: You'll not only learn how to conduct these analyses, we'll also go over how to interpret the statistical results and how to graph the results using SPSS and a special Excel template I've created for you.

Whatever be your motivation to learn OpenCV, i can assure you that you've come to the right course. Hands on Computer Vision with OpenCV & Python is THE most comprehensive and cost-effective video course you will find on the web right now. This course is tailor made for an individual who wishes to transition quickly from an absolute beginner to an OpenCV expert in just three weeks. I ensure this by breaking down and articulating the most difficult concepts in plain and simple manner, replacing tough equations by examples and concepts by using small code snippets. This course covers topics using a methodical step-by-step approach with increasing difficulty, starting outright with the very basics and fundamentals.

About this course: We invite you to a fascinating journey into Graph Theory -- an area which connects the elegance of painting and the rigor of mathematics; is simple, but not unsophisticated. Graph Theory gives us, both an easy way to pictorially represent many major mathematical results, and insights into the deep theories behind them. In this course, among other intriguing applications, we will see how GPS systems find shortest routes, how engineers design integrated circuits, how biologists assemble genomes, why a political map can always be colored using a few colors. We will study Ramsey Theory which proves that in a large system, complete disorder is impossible! By the end of the course, we will implement an algorithm which finds an optimal assignment of students to schools.

Welcome to this course: TensorFlow Slim(TF-Slim) In Depth. TF-Slim is a lightweight library for defining, training and evaluating complex models in TensorFlow. It is a library that makes building, training and evaluation neural networks simple. The course helps you obtain in-depth knowledge of TensorFlow-Slim, making you the go-to person for solving artificial intelligence problems. At the end of this course, you will have mastered the offerings of TensorFlow, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems.

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist's toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated.

Carnegie Mellon University's School of Computer Science will offer a new undergraduate degree in artificial intelligence beginning this fall, providing students with in-depth knowledge of how to transform large amounts of data into actionable decisions. SCS has created the new AI degree, the first offered by a U.S. university, in response to extraordinary technical breakthroughs in AI and the growing demand by students and employers for training that prepares people for careers in AI. "Specialists in artificial intelligence have never been more important, in shorter supply or in greater demand by employers," said Andrew Moore, dean of the School of Computer Science. "Carnegie Mellon has an unmatched depth of expertise in AI, making us uniquely qualified to address this need for graduates who understand how the power of AI can be leveraged to help people." This spring, U.S. News and World Report ranked SCS as the No. 1 graduate school for artificial intelligence. The bachelor's degree program in computer science teaches students to think broadly about methods that can accomplish a wide variety of tasks across many disciplines, said Reid Simmons, research professor of robotics and computer science and director of the new AI degree program.