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Machine Learning For Developers

#artificialintelligence

Machine Learning has definitely gone mainstream with almost all the major vendors announcing support for Machine Learning platforms, frameworks, libraries and how they have started to use it in their applications. This blog post highlights the key Machine Learning process and the spectrum of Machine Learning platforms that are available today for developers to get started. The first thing that developers need to understand that Machine Learning techniques are very different from the traditional programming constructs that they use, while developing their applications. It is often remarked in a lighter vein that some vendors pass of multiple if, then, else statements are machine learning in their applications. The diagram shown below depicts a typical Machine Learning process.


8 Neural Network Compression Techniques For ML Developers

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In addition, recent years witnessed significant progress in virtual reality, augmented reality, and smart wearable devices, creating challenges in deploying deep learning systems to portable devices with limited resources (e.g. Now let's take a look at a few papers that introduced novel compression models: In this paper, the authors propose two novel network quantization approaches single-level network quantization (SLQ) for high-bit quantization and multi-level network quantization (MLQ). The network quantization is considered from both width and depth level. In this paper the authors proposed an efficient method for obtaining the rank configuration of the whole network. Unlike previous methods which consider each layer separately, this method considers the whole network to choose the right rank configuration.


Book: Python Data Science Handbook: Tools and Techniques for Developers 1st Edition

@machinelearnbot

The Python Data Science Handbook provides a reference to the breadth of computational and statistical methods that are central to data-intensive science, research, and discovery. People with a programming background who want to use Python effectively for data science tasks will learn how to face a variety of problems: e.g., how can I read this data format into my script? How can I manipulate, transform, and clean this data? How can I visualize this type of data? How can I use this data to gain insight, answer questions, or to build statistical or machine learning models?


Book: Python Data Science Handbook: Tools and Techniques for Developers 1st Edition

@machinelearnbot

The Python Data Science Handbook provides a reference to the breadth of computational and statistical methods that are central to data-intensive science, research, and discovery. People with a programming background who want to use Python effectively for data science tasks will learn how to face a variety of problems: e.g., how can I read this data format into my script? How can I manipulate, transform, and clean this data? How can I visualize this type of data? How can I use this data to gain insight, answer questions, or to build statistical or machine learning models?


AI for Game Developers

AITopics Original Links

Advances in 3D visualization and physics-based simulation technology make it possible for game developers to create compelling, visually immersive gaming environments that were only dreamed of years ago. But today's game players have grown in sophistication along with the games they play. It's no longer enough to wow your players with dazzling graphics; the next step in creating even more immersive games is improved artificial intelligence, or AI.Fortunately, advanced AI game techniques are within the grasp of every game developer--not just those who dedicate their careers to AI. If you're new to game programming or if you're an experienced game programmer who needs to get up to speed quickly on AI techniques, you'll find AI for Game Developers to be the perfect starting point for understanding and applying AI techniques to your games.Written for the novice AI programmer, AI for Game Developers introduces you to techniques such as finite state machines, fuzzy logic, neural networks, and many others, in straightforward, easy-to-understand language, supported with code samples throughout the entire book (written in C/C). From basic techniques such as chasing and evading, pattern movement, and flocking to genetic algorithms, the book presents a mix of deterministic (traditional) and non-deterministic (newer) AI techniques aimed squarely at beginners AI developers.