{"product_id":"make-your-first-gan-with-pytorch-paperback","title":"Make Your First GAN With PyTorch - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eTariq Rashid\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003eA gentle introduction to Generative Adversarial Networks, and a practical step-by-step tutorial on making your own with PyTorch.\u003cbr\u003e \u003cbr\u003eThis beginner-friendly guide will give you hands-on experience: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003elearning PyTorch basics\u003c\/li\u003e\n\u003cli\u003edeveloping your first PyTorch neural network\u003c\/li\u003e\n\u003cli\u003eexploring neural network refinements to improve performance\u003c\/li\u003e\n\u003cli\u003eintroduce CUDA GPU acceleration\u003c\/li\u003e\n\u003c\/ul\u003eIt will introduce GANs, one of the most exciting areas of machine learning: \u003cul\u003e\n\u003cli\u003eintroducing the concept step-by-step, in plain English\u003c\/li\u003e\n\u003cli\u003ecoding the simplest GAN to develop a good workflow\u003c\/li\u003e\n\u003cli\u003egrowing our confidence with an MNIST GAN\u003c\/li\u003e\n\u003cli\u003eprogressing to develop a GAN to generate full-colour human faces\u003c\/li\u003e\n\u003cli\u003eexperiencing how GANs fail, exploring remedies and improving GAN performance and stability\u003c\/li\u003e\n\u003c\/ul\u003eBeyond the very basics, readers can explore more sophisticated GANs: \u003cul\u003e\n\u003cli\u003econvolutional GANs for generated higher quality images\u003c\/li\u003e\n\u003cli\u003econditional GANs for generated images of a desired class\u003c\/li\u003e\n\u003c\/ul\u003eThe appendices will be useful for students of machine learning as they explain themes often skipped over in many courses: \u003cul\u003e\n\u003cli\u003ecalculating ideal loss values for balanced GANs\u003c\/li\u003e\n\u003cli\u003eprobability distributions and sampling them to create images\u003c\/li\u003e\n\u003cli\u003ecarefully chosen examples illustrating how convolutions work\u003c\/li\u003e\n\u003cli\u003ea brief explanation of why gradient descent isn't suited to adversarial machine learning\u003c\/li\u003e\n\u003c\/ul\u003eAll code is available publicly as open source on github.\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 208\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.54 x 11 x 8.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e March 14, 2020\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47212873220345,"sku":"9798624728158","price":63.04,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/aHJScjg1Z3lXK2xYQ3BXN25GYnhYdz09.webp?v=1768098784","url":"https:\/\/bookscloud.io\/products\/make-your-first-gan-with-pytorch-paperback","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}