"The implementation part is very good and up-too the mark. The explanation step by step process is very good." (February 2018). "course done very well; everything is explained in detail; really satisfied!!!" (February 2018). "Difficult topics are simply illustrated and therefore easy to understand." (January 2018).
Creating machines to think for themselves may feel like the plot of a hardcore sci-fi novel. But in reality, it's what engineers and software developers are doing today to push the boundaries of technology. This limited-time offer bundles up high-level instruction on how to actual construct self-learning machines for 91 percent off from TNW Deals. TNW Conference is back for its 12th year. In over 14 hours of instruction, these four courses take you deep inside the artificial intelligence tech that underlies advanced Google web searches and even Tesla's self-driving cars.
In this course, intended to expand upon your knowledge of neural networks and deep learning, you'll harness these concepts for computer vision using convolutional neural networks. Going in-depth on the concept of convolution, you'll discover its wide range of applications, from generating image effects to modeling artificial organs. Explore the StreetView House Number (SVHN) dataset using convolutional neural networks (CNNs) Build convolutional filters that can be applied to audio or imaging Extend deep neural networks w/ just a few functions Note: we strongly recommend taking The Deep Learning & Artificial Intelligence Introductory Bundle before this course. The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning.
What you will learn in this course? In this course, you'll work with more complex environments, specifically provided by the OpenAI Gym: CartPole Mountain Car Atari games to train effective learning agents so you'll need new techniques. We've seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning.Supervised and unsupervised machine learning algorithms are for making predictions about data and analyzing, while reinforcement learning is about training an agent to interact with an environment and maximize its reward. Deep reinforcement learning and AI has a lot of potentials also carries huge risk. One main principle of training reinforcement learning agents is that there are unintended consequences when training an AI.