Welcome to Deep Reinforcement Learning 2.0! In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field). In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more.

Below are three alternatives to Kaggle which I've become interested in: A fourth competition does exist -- HALITE by Two Sigma-- however, HALITE appears to have been discontinued…you're welcome to check it out on your own; only Battlecode, Terminal, and Lux are summarized below. Battle Code has been around since 2003; the description below is taken directly from the site. Battlecode is a real-time strategy game, for which you will write an AI player. Your AI player will need to strategically manage a robot army and control how your robots work together to defeat the enemy team. As a contestant, you will learn to use artificial intelligence, pathfinding, distributed algorithms, and communications to make your player as competitive as possible.

A-Z Codes for Text Classification, Language Translation, Sentiment Analysis, Chatbots, Text Writing & Movie Recommender

Microsoft Azure DP-100: Designing and Implementing a Data Science Solution Exam Covered. Learn Azure Machine Learning.

This is a course called "Intelligently Extract Text and Data from a Document with OCR and NER." In this course, you will learn how to make your own Named Entity Recognizer. The main goal of this class is to learn how to find things like invoices, business cards, shipping bills, Bill of Lading documents, and more from scanned documents like this one. However, for the sake of privacy, we only looked at the Business Card. But you can use the same framework to write any kind of financial report.

The Region-based CNN (R-CNN) approach to bounding-box object detection is to attend to a manageable number of candidate object regions and evaluate convolutional networks independently on each RoI. R-CNN was extended to allow attending to RoIs on feature maps using RoIPool, leading to fast speed and better accuracy. Faster R-CNN advanced this stream by learning the attention mechanism with a Region Proposal Network (RPN). Faster R-CNN is flexible and robust to many follow-up improvements, and is the current leading framework in several benchmarks. The family of methods may be among the most effective for object detection, achieving then state-of-the-art results on computer vision benchmark datasets. Although accurate, the models can be slow when making a prediction as compared to alternate models such as YOLO that may be less accurate but are designed for real-time prediction. Mask R-CNN is a sophisticated model to implement, especially as compared to a simple or even state-of-the-art deep convolutional neural network model. Source code is available for each version of the R-CNN model, provided in separate GitHub repositories with prototype models based on the Caffe deep learning framework.

Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn

This course will allow you to become a professional who understands the math on which algorithms are built, rather than someone who applies them blindly without knowing what happens behind the scenes. But let's answer a pressing question you probably have at this point: "What can I expect from this course and how it will help my professional development?" In brief, we will provide you with the theoretical and practical foundations for two fundamental parts of data science and statistical analysis – linear algebra and dimensionality reduction. Linear algebra is often overlooked in data science courses, despite being of paramount importance. Most instructors tend to focus on the practical application of specific frameworks rather than starting with the fundamentals, which leaves you with knowledge gaps and a lack of full understanding.

This course is Very Practical, Easy to Understand and Every Concept is Explained with an Example! I have added real life examples to understand the applications of statistics in the field of Data Science... We'll cover everything that you need to know about statistics and probability for Data Science and Business Analytics! So What Are You Waiting For?

We will also use not only basic concepts but also teach you advance level concepts and use them to make all the 20 Applications in Python. In this course, we will assume that you know basics of python and is now ready to make real time applications in python. You are now ready to use python to make something real out it. We will also use not only basic concepts but also teach you advance level concepts and use them to make all the 20 Applications in Python. Not only that we have covered and taught many Machine Learning Models and then also using these Machine Learning Models, we have build Advance Level Applications as well.