Requirements: • Know reinforcement learning basics, MDPs, Dynamic Programming, Monte Carlo, TD Learning • Calculus and probability at the undergraduate level • Experience building machine learning models in Python and Numpy • Know how to build a feedforward, convolutional, and recurrent neural network using Theano and Tensorflow This course is all about the application of deep learning and neural networks to reinforcement learning. If you've taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Reinforcement learning has been around since the 70s but none of this has been possible until now. The world is changing at a very fast pace.

An algorithm is presented for momentum gradient descent optimization based on the first-order differential equation of the Newtonian dynamics. The fictitious mass is introduced to the dynamics of momentum for regularizing the adaptive stepsize of each individual parameter. The dynamic relaxation is adapted for stochastic optimization of nonlinear objective functions through an explicit time integration with varying damping ratio. The adaptive stepsize is optimized for each individual neural network layer based on the number of inputs. The adaptive stepsize for every parameter over the entire neural network is uniformly optimized with one upper bound, independent of sparsity, for better overall convergence rate. The numerical implementation of the algorithm is similar to the Adam Optimizer, possessing computational efficiency, similar memory requirements, etc. There are three hyper-parameters in the algorithm with clear physical interpretation. Preliminary trials show promise in performance and convergence.

Multiagent coordination in cooperative multiagent systems (MASs) has been widely studied in both fixed-agent repeated interaction setting and the static social learning framework. However, two aspects of dynamics in real-world multiagent scenarios are currently missing in existing works. First, the network topologies can be dynamic where agents may change their connections through rewiring during the course of interactions. Second, the game matrix between each pair of agents may not be static and usually not known as a prior. Both the network dynamic and game uncertainty increase the coordination difficulty among agents. In this paper, we consider a multiagent dynamic social learning environment in which each agent can choose to rewire potential partners and interact with randomly chosen neighbors in each round. We propose an optimal rewiring strategy for agents to select most beneficial peers to interact with for the purpose of maximizing the accumulated payoff in repeated interactions. We empirically demonstrate the effectiveness and robustness of our approach through comparing with benchmark strategies. The performance of three representative learning strategies under our social learning framework with our optimal rewiring is investigated as well.

This course will take you through the process of predictive analytics/predictive modeling. A statistical technique or machine learning algorithm is borrowed to help predict an outcome. The goal of this course is to start you on your journey to becoming a top data scientist. To do that, you need to understand the methodology or methods at your disposal in solving these problems. By using a famous example (the titanic disaster), we will show you how to understand the problem in-front of you, how to explore your data, pre-process your data, how to create your first model, how to improve model accuracy, and look at some evaluation metrics.

The Hidden Markov Model or HMM is all about learning sequences. A lot of the data that would be very useful for us to model is in sequences. Stock prices are sequences of prices. Language is a sequence of words. Credit scoring involves sequences of borrowing and repaying money, and we can use those sequences to predict whether or not you're going to default.

Deep Learning is part of a broader family of machine learning methods based on learning data representations. Deeplearning4j is a Deep Learning programming library written in Java and the Java Virtual Machine (JVM) and is a computing framework with wide support for Deep Learning algorithms. In this course, you start by installing Deep Learning software for Java. You learn how to use the DL4J library and apply Deep Learning to a range of real-world use cases. The course will take you into Neural Networks, working with Perceptron, XOR, and Gradient Descent on code examples.

If you are looking for a complete course on Python programming, then go for this Learning Path. Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great scripting language that can power your applications and provide speed, safety, and scalability. We will begin this learning journey by understanding the basic concepts of Python such as statements and syntax along with using numbers, strings, and tuples. We will then explore various function definition techniques along with learning the basics of classes and objects.

As an example, consider Elon Musk's statements backtracking on the practical nature and benefits of using automated workers in his factories to boost Model 3 production. A piece in Bloomberg covers the Silicon Valley darling's explanation by drawing attention to his Twitter post: "Excessive automation at Tesla was a mistake. To be precise, my mistake." That was swept under the rug pretty quickly, right? It feels like a repetition of the trend of these breakthrough advancements in cancer treatments, where every other week it seems like some doctor, researcher, or precocious high school student has designed some new method that could lead to the potential "cure to cancer."

Artificial intelligence is yet to become a standard in schools, but it has the potential to transform the educational field. It's is a technology whose time has certainly come because it can already outperform humans in many ways. However, it can be very helpful for tech students. Meeting the needs of each student becomes a must in today's classroom. For example, a teacher should create personalized tasks to fit the learning style of students and ensure that they enjoy the same access to learning.

About this course: This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn how to carry out no-ops data warehousing, analysis and pipeline processing. Prerequisites: • Google Cloud Platform Big Data and Machine Learning Fundamentals • Experience using a SQL-like query language to analyze data • Knowledge of either Python or Java 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 services are currently unavailable in China).