Objective-C 2.0速成指南:实战专家详解与深度学习

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"Learning Object-C 2.0" 是一本备受推崇的编程教材,作者罗伯特·克莱尔以其丰富的实战经验和深入浅出的讲解,引领读者快速掌握Objective-C语言的核心知识。该书不仅提供了全面的Objective-C语言覆盖,还包含了实用且节省时间的技巧,帮助开发者避免常见问题,通过具体的实例和详尽的实现细节,让学习者能够迅速、完整地理解这门语言及其核心特性和概念。 本书的独特之处在于其焦点集中在基础,适合那些希望从零开始或提升Objective-C技能的程序员。作者理解到,尽管市面上有许多试图涵盖广泛主题的Objective-C书籍,如面向对象编程、Objective-C编程语言以及针对苹果平台的应用开发,但这样的范围往往使得单一书籍难以做到全面深入。《Learning Objective-C 2.0》的编写策略是引导读者扎实掌握语言的基本原理,使具备一定能力的程序员能够顺利地开始编写Objective-C代码。 书中得到了专业开发者的好评,比如斯科特·D·耶利奇(Mobile Application Developer)赞赏克莱尔在书中简洁的概述、生动的示例以及对特定实施细节的关注,这些都促进了学习者的高效理解和实践。另一位评论者约瑟夫·E·萨科博士(Ph.D., J.E.Sacco & Associates, Inc.)也肯定了这本书对基础Objective-C掌握的重要性,强调它对于程序员进入Apple平台应用开发领域的实用性。 《Learning Objective-C 2.0》是一本理想的教程,不仅为读者提供了系统的学习路径,而且通过作者的亲身经验确保了内容的实用性和深度,是任何想要深入学习或者提升Objective-C技能的开发者的宝贵资源。无论是初次接触Objective-C还是寻求进阶指导,这本书都值得一读。

C:\Users\80977\.conda\envs\pytorchenv\python.exe D:\work\DL-codes\deep-learning-for-image-processing-master\deep-learning-for-image-processing-master\pytorch_object_detection\faster_rcnn\train_mobilenetv2.py Using cuda device training. Using [0, 0.5, 0.6299605249474366, 0.7937005259840997, 1.0, 1.2599210498948732, 1.5874010519681994, 2.0, inf] as bins for aspect ratio quantization Count of instances per bin: [ 5 25 929 117 260 4198 135 48] Using 8 dataloader workers Traceback (most recent call last): File "<string>", line 1, in <module> File "C:\Users\80977\.conda\envs\pytorchenv\lib\multiprocessing\spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "C:\Users\80977\.conda\envs\pytorchenv\lib\multiprocessing\spawn.py", line 125, in _main prepare(preparation_data) File "C:\Users\80977\.conda\envs\pytorchenv\lib\multiprocessing\spawn.py", line 236, in prepare _fixup_main_from_path(data['init_main_from_path']) File "C:\Users\80977\.conda\envs\pytorchenv\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "C:\Users\80977\.conda\envs\pytorchenv\lib\runpy.py", line 288, in run_path return _run_module_code(code, init_globals, run_name, File "C:\Users\80977\.conda\envs\pytorchenv\lib\runpy.py", line 97, in _run_module_code _run_code(code, mod_globals, init_globals, File "C:\Users\80977\.conda\envs\pytorchenv\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "D:\work\DL-codes\deep-learning-for-image-processing-master\deep-learning-for-image-processing-master\pytorch_object_detection\faster_rcnn\train_mobilenetv2.py", line 4, in <module> import torch File "C:\Users\80977\AppData\Roaming\Python\Python39\site-packages\torch\__init__.py", line 123, in <module> raise err OSError: [WinError 1455] 页面文件太小,无法完成操作。 Error loading "C:\Users\80977\AppData\Roaming\Python\Python39\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies.

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