Ma imos teɣare toɣadat?
Context:Deep learning eqqal achrut n egadach maqqoran n tikarsiwen n teɣare n maršin tijjat fal rezo tan n artificial neural s teɣare tan representation Teɣare tadobat ad atwəsəkən as akayad,as akayad azaran meɣ as akayad waren ikna.  Dumu tan n teɣare chajret chund deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks d transformer tan ijraw nat alxidmat daɣ abara tan eha computer vision, musnat n awal, natural language processing, machine translation, bioinformatics, edey n asafar, medical image analysis, climate science, material inspection d program tan n board game, s inta a id eraw nen ittusan olahan id tolas daɣ ihandagan iyyad ojjaran alxidmat tan imusanan n addinat  Rezo tan win artificial neural (ANN) idayan s data processing d distributed communication nodes daɣ sistem tan win abarkot ANNs ilan anmizlayan ajjotnen id akəlkəl wan abarkot Dǎɣ atamosan, rezo ən əanəeretan atiwəjanen amosan ayknan aśuhu əd əmik, agud was eɣaf ən biolojiq ən ajət ən imudaran adutətnen eqal aśuhu (təmusne) əd analogique.  "Deep" daɣ deep learning eqqal fal axadam n layer tan ajjotnen daɣ rezo Alxidmat ta tazzarat t asukna as perceptron n iyaka war addobat ad eqqil a eqqalan amaskat n alɣalim mucham rezo s nonpolynomial activation function s layer iyyan eɣlalan n iddikud war n ila alxadud addobat Deep learning eqqal amutti eynayan eqqalan fal iddikud war n izlay n layer tan n iddikud eqqalan a orakan, eha ijjuch n axadam d axadam olaɣan, as ijja awen eha amutti n mirda daɣ ihandagan wi madroynen Daɣ deep learning, layer tan addobat nat ad aqqilan anmizlay tolas ad iknan anmizlay hullan daɣ dumu tan n biologically informed connectionist i alxidmat, trainability d afaham
Deep learning eqqal achrut n egadach maqqoran n tikarsiwen n teɣare n maršin tijjat fal rezo tan n artificial neural s teɣare tan representation