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      Download the Game Design and Indie Game Marketing Freebook   07/19/17

      GameDev.net and CRC Press have teamed up to bring a free ebook of content curated from top titles published by CRC Press. The freebook, Practices of Game Design & Indie Game Marketing, includes chapters from The Art of Game Design: A Book of Lenses, A Practical Guide to Indie Game Marketing, and An Architectural Approach to Level Design. The GameDev.net FreeBook is relevant to game designers, developers, and those interested in learning more about the challenges in game development. We know game development can be a tough discipline and business, so we picked several chapters from CRC Press titles that we thought would be of interest to you, the GameDev.net audience, in your journey to design, develop, and market your next game. The free ebook is available through CRC Press by clicking here. The Curated Books The Art of Game Design: A Book of Lenses, Second Edition, by Jesse Schell Presents 100+ sets of questions, or different lenses, for viewing a game’s design, encompassing diverse fields such as psychology, architecture, music, film, software engineering, theme park design, mathematics, anthropology, and more. Written by one of the world's top game designers, this book describes the deepest and most fundamental principles of game design, demonstrating how tactics used in board, card, and athletic games also work in video games. It provides practical instruction on creating world-class games that will be played again and again. View it here. A Practical Guide to Indie Game Marketing, by Joel Dreskin Marketing is an essential but too frequently overlooked or minimized component of the release plan for indie games. A Practical Guide to Indie Game Marketing provides you with the tools needed to build visibility and sell your indie games. With special focus on those developers with small budgets and limited staff and resources, this book is packed with tangible recommendations and techniques that you can put to use immediately. As a seasoned professional of the indie game arena, author Joel Dreskin gives you insight into practical, real-world experiences of marketing numerous successful games and also provides stories of the failures. View it here. An Architectural Approach to Level Design This is one of the first books to integrate architectural and spatial design theory with the field of level design. The book presents architectural techniques and theories for level designers to use in their own work. It connects architecture and level design in different ways that address the practical elements of how designers construct space and the experiential elements of how and why humans interact with this space. Throughout the text, readers learn skills for spatial layout, evoking emotion through gamespaces, and creating better levels through architectural theory. View it here. Learn more and download the ebook by clicking here. Did you know? GameDev.net and CRC Press also recently teamed up to bring GDNet+ Members up to a 20% discount on all CRC Press books. Learn more about this and other benefits here.

Prozak

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  1. There is no specific reasons for doing it from scratch for company reasons, except that if I have the knowledge to do it from scratch, then I might adapt it into lower tier hardware we have lying around. None the less we already got a couple of 1080s on order.   Thank you for your site, do you know of any books that might help here?   Again, thank you for your time.
  2. Hi guys,   could someone point me to a book resource, or similar, regarding the implementation of CNNs for identification of features in images, such as letters, and then more complex features, like animals?   I would like to understand the underlying theory, and then do a CUDA implementation. Yes, I know there are libraries out there that already do this quite efficiently, but I would like to do this from scratch for academic, and then internal company reasons.   Thanks everyone and anyone for any help on this.
  3. Thx ApochPiQ,   I've done exactly that, capture a 400x400 area and reduce to 100x100, but the results are simply not acceptable.   What alternative are there outside of the outdated win32 gdi api?   Thx!
  4. Hi all,   just coded a small demo that captures the desktop window, and then tries to scale it down and draw it to my dialog' hDC.   Problem is, I'm using StretchBlt, and the final results are quite ugly. I've read several stuff that instructs us to use SetStretchBltMode and SetBrushOrgEx. The end result just isn't up to scratch.   Is there some solution, even if it involves GDI+/ OpenGL/ DirectX/ DirecDraw that can draw into my dialog and resize my original bitmap perfectly? Also, I'm looking for a somewhat fast solution. I wouldn't want this to take more than 1sec to draw...   Thanks for any tips on this. Have a great week.
  5. Hmm, even tough I've validated my BP algorithm with JavaNNS, a neural net doesn't always achieve a result when training XOR. Is this a documented symptom? If I place the same value on all weights, it doesn't seem to reach a solution. Seemingly, I would venture, the net enjoys more success when filled with random weights that favor a certain result. I'm off for holidays, but I will post more results here when I have them. Thanks all, and to all happy holidays with your families.
  6. I think I found something wrong with my code, I'm using JavaNNS to validate it right now, if I still have issues, I'll post them here soon.   For now thank you for your patience and assistance. 
  7. Sure, ok, I think I can do that. Gimme a day and I'll post the results... brb....
  8. So, you believe I should run the back-propagation always, period. Ok, I will change the code to do that.   Do you think sharing the weights after a run, and after, say, 100 runs, here, would be useful?   Thank you!
  9. Sorry for that lapse.   I initialize the weights with a random number between -1 and +1.   All transfer functions are simple sigmoid. I have two training methods: * Linear - go through each example, if error above max_allowed_error, then backpropagate * Stochastic - select a random training example, if error above max_allowed_error, then backpropagate   My current max_allowed_error is 0.1   Btw, is there any online resource where I can copy an initial topology and weight setup and cycle through 1.000 iterations (for example), and then check the resulting weights, a sort of Roseta Stone to make sure the math behind my implementation of the learning algorithm is valid and correctly implemented?   Thank you.
  10. This specific class is a 2 hidden layer class.   So the topology I use is 2-2-1-1 (2 inputs, 1 output, 2 layer 1 hidden neurons, 1 layer 2 hidden neuron).   It might reach solution in as little as 5000 iterations, using a constant learning rate of 1.0, or not reach it at all after 50 million iterations. I'm using doubles. Also, after inspection, some values in the weights seem to grow a lot, or be extremely small (near zero).   Thank you for any inputs you might have.
  11. I placed this question here, because, making sure that I start with the same network topology, the only thing changing being a randomization of the starting weights and bias, sometimes the network will achieve solution, and other times not (by that I mean multiple million iterations not reaching solution).   So, in an example where the learning rate remained untouched throughout, one network achieves solution, and another does not, due to starting weights conditions.   Could that be symptomatic of a bug, or is this something that sometimes just happens due to initial starting conditions? (thank you for your replies!)
  12. Hi all,   would just like to know if, for a given problem, in this case the classic XOR binary problem,  the network will eventually fluctuate to the solution, if given infinite time to do so, or, if due to learning rate issues, it can get stuck in over-shooting behaviors.   If the learning rate should pose no problem, can it be mathematically said that the network should travel towards solution-space?   Thank you.
  13. Hi all,   has anyone here worked on something that would render astral bodies, like stars, constellations, galaxies, planets, taking into account the latitude and longitude of the user?   I'm tasked with doing something like this, in a browser. If there was some REST API I could consume and then render the horizon and the astral bodies, that would be awesome, apart from that I would require the actual equations for calculating their position above or bellow the horizon...   Thanks for any tips on this, Hugo Ferreira
  14. Hi, I'm coding a simple webGL engine, and would like to import stereographic panoramas (taken from an iPad) into the engine as cubemaps.   Is there any free utility i can download that would accomplish this?   Thank you in advance!