This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.
This blog is a part of "A Guide To TensorFlow", where we will explore the TensorFlow API and use it to build multiple machine learning models for real- life examples. In this blog we shall uncover TensorFlow *Graph*, understand the concept of *Tensors* and also explore TensorFlow data types.
ReSanskrit explores every aspect of Aditya Hrudayam Stotram - right from the story, rules of recitation, benefits and scientific significant. Click to read now
One of the hardest concepts to grasp when learning about Convolutional Neural Networks for object detection is the idea of anchor boxes. It is also one of the most important parameters you can tune…
The developer homepage - join the programming community from gitconnected. Discover and share coding news, with the best stories rising to the top. Get the latest updates on JavaScript, web development, frontend, backend, and programming. Build your skills, reputation, and network with your personal developer homepage and portfolio. Collaborate with other software engineers.
This book explains the algorithms behind those collisions using basic shapes like circles, rectangles, and lines so you can implement them into your own projects.
This book is an interactive introduction to the theory and applications of complex functions from a visual point of view. However, it does not cover all the topics of a standard course. In fact, it is a collection of selected topics and interactive applets that can be used as a supplementary learning resource by anyone interested in learning this fascinating branch of mathematics.
This tutorial aims to encourage creative coders to consider Blender as a platform for creating 3D artworks. Blender can be daunting to learn, so this primer is written for those who’ve tried their…
This is a PyTorch implementation/tutorial of Deep Q Networks (DQN) from paper Playing Atari with Deep Reinforcement Learning. This includes dueling network architecture, a prioritized replay buffer and double-Q-network training.
- Sep. 28 – Oct. 2, 2020
- Lihong Li (Google Brain; chair), Marc G. Bellemare (Google Brain)
- The success of deep neural networks in modeling complicated functions has recently been applied by the reinforcement learning community, resulting in algorithms that are able to learn in environments previously thought to be much too large. Successful applications span domains from robotics to health care. However, the success is not well understood from a theoretical perspective. What are the modeling choices necessary for good performance, and how does the flexibility of deep neural nets help learning? This workshop will connect practitioners to theoreticians with the goal of understanding the most impactful modeling decisions and the properties of deep neural networks that make them so successful. Specifically, we will study the ability of deep neural nets to approximate in the context of reinforcement learning.
Path tracing is a method for generating digital images by simulating how light would interact with objects in a virtual world. The path of light is traced by...
The program focused on the following four themes:
- Optimization: How and why can deep models be fit to observed (training) data?
- Generalization: Why do these trained models work well on similar but unobserved (test) data?
- Robustness: How can we analyze and improve the performance of these models when applied outside their intended conditions?
- Generative methods: How can deep learning be used to model probability distributions?
- Understanding the GitHub Flow
- Hello World
- Getting Started with GitHub Pages
- Git Handbook
- Forking Projects
- Be Social
- Making Your Code Citable
- Mastering Issues
- Mastering Markdown
- Documenting your projects on GitHub
This is the Graph Neural Networks: Hands-on Session from the Stanford 2019 Fall CS224W course.
In this tutorial, we will explore the implementation of graph neural networks and investigate what representations these networks learn. Along the way, we'll see how PyTorch Geometric and TensorBoardX can help us with constructing and training graph models.
Pytorch Geometric tutorial part starts at -- 0:33:30
Details on:
* Graph Convolutional Neural Networks (GCN)
* Custom Convolutional Model
* Message passing
* Aggregation functions
* Update
* Graph Pooling
In this tutorial I’ll explain how to build a simple working Recurrent Neural Network in TensorFlow. This is the first in a series of seven parts where various aspects and techniques of building…
Fullstack GraphQL Tutorial to go from zero to production covering all basics and advanced concepts. Includes tutorials for Apollo, Relay, React and NodeJS.
Going to a conference is always an excitement and fun: one can connect with like-minded individuals and exchange stimulating ideas. However, in order to make the most out of a conference, a lot of hard work is needed before, during and after the meeting. This blog post provides a checklist of things to do before,…
An attempt to create a convenient workspace that makes it possible to work with multiple custom python libraries, while keeping all benefits of Google Colaboratory.
An introduction to what a Mesh, Shader and Material is in Unity, how to set Shader Properties from C#, a brief look at Forward vs Deferred rendering and some information about Material instances and Batching. HLSL | Unity Shader Tutorials, @Cyanilux
Learn the Linux/ Unix command line (Bash) with our 13 part beginners tutorial. Clear descriptions, command outlines, examples, shortcuts and best practice.
Hi, I’m Greg, and for the last two years, I’ve been developing a 3d fractal exploration game, which started as just a “what if” experiment. I would describe myself as technical artist, meaning, I am…
Proteins play countless roles throughout the biological world, from catalyzing chemical reactions to building the structures of all living things. Despite this wide range of functions all proteins are made out of the same twenty amino acids, but combined in different ways. The way these twenty amino acids are arranged dictates the folding of the protein into its primary, secondary, tertiary, and quaternary structure. Since protein function is based on the ability to recognize and bind to specific molecules, having the correct shape is critical for proteins to do their jobs correctly. Learn more about the relationship between protein structure and function in this video.
Hi Guys, I have Always been asked to share my code which I use in my video. Answering people’s questions is great, and the feeling you get when you solve a p...
A collection of .BLEND and .FBX files to accompany the Robotic Design with Blender tutorial series on YouTube:(Part 1) https://youtu.be/aRBHMRa6pIA(Part 2) https://youtu.be/TKc-g84j2x8(Part 3) https://youtu.be/Cuo_ytkvCpo(Part
These tutorials walk you through writing medium-size software projects from scratch, step by step. The projects are based on real open-source software projects, and most of the tutorials stay true to the original source code. Every line of code is explained in detail, allowing you to thoroughly understand the project’s entire codebase.
List of 51 TensorFlow deep learning tutorial videos. TensorFlow™ is an open source software library for numerical computation using data flow graphs....
You remember prime numbers, right? Those numbers you can’t divide into other numbers, except when you divide them by themselves or 1? Right. Here is a 3000 year old question: Present an argument or…
- Aug. 31 – Sep. 4, 2020
- Csaba Szepesvari (University of Alberta, Google DeepMind; chair), Emma Brunskill (Stanford University), Sébastien Bubeck (MSR), Alan Malek (DeepMind), Sean Meyn (University of Florida), Ambuj Tewari (University of Michigan), Mengdi Wang (Princeton)
R. Sharipov. (2004)cite arxiv:math/0412421Comment: The textbook, AmSTeX, 132 pages, amsppt style, prepared for double side printing on letter size paper.
R. Sharipov. (2004)cite arxiv:math/0405323Comment: The textbook, AmSTeX, 143 pages, amsppt style, prepared for double side printing on letter size paper.
A. Slivkins. (2019)cite arxiv:1904.07272Comment: The manuscript is complete, but comments are very welcome! To be published with Foundations and Trends in Machine Learning.