{"id":327620,"date":"2025-12-05T19:35:53","date_gmt":"2025-12-05T20:35:53","guid":{"rendered":"https:\/\/express24.ir\/d\/product\/supercourse-0000013450\/"},"modified":"2025-12-21T06:47:22","modified_gmt":"2025-12-21T07:47:22","slug":"supercourse-0000013450","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/supercourse-0000013450\/","title":{"rendered":"\u06a9\u062a\u0627\u0628 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0627 PyTorch"},"content":{"rendered":"
\n

\ud83c\udf93 \u062f\u0648\u0631\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u062c\u0627\u0645\u0639<\/h2>\n<\/p><\/div>\n
\n

\ud83d\udcda \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u062f\u0648\u0631\u0647<\/h3>\n

\u0639\u0646\u0648\u0627\u0646 \u062f\u0648\u0631\u0647:<\/strong> \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0627 PyTorch<\/p>\n

\u0645\u0648\u0636\u0648\u0639 \u06a9\u0644\u06cc:<\/strong> \u0628\u0631\u0646\u0627\u0645\u0647 \u0646\u0648\u06cc\u0633\u06cc<\/p>\n

\u0645\u0648\u0636\u0648\u0639 \u0645\u06cc\u0627\u0646\u06cc:<\/strong> \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 (AI\/ML)<\/p>\n<\/div>\n

\n

\ud83d\udccb \u0633\u0631\u0641\u0635\u0644\u200c\u0647\u0627\u06cc \u062f\u0648\u0631\u0647 (100 \u0645\u0648\u0636\u0648\u0639)<\/h3>\n
    \n
  • \n 1.<\/span> \u0645\u0642\u062f\u0645\u0647\u200c\u0627\u06cc \u0628\u0631 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc\u060c \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642\n <\/li>\n
  • \n 2.<\/span> \u0686\u0631\u0627 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0648 PyTorch\u061f\n <\/li>\n
  • \n 3.<\/span> \u0646\u0635\u0628 \u0648 \u0631\u0627\u0647\u200c\u0627\u0646\u062f\u0627\u0632\u06cc \u0645\u062d\u06cc\u0637 \u062a\u0648\u0633\u0639\u0647 (CUDA, PyTorch)\n <\/li>\n
  • \n 4.<\/span> \u0645\u0631\u0648\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642\n <\/li>\n
  • \n 5.<\/span> \u0645\u0642\u062f\u0645\u0647\u200c\u0627\u06cc \u0628\u0631 NumPy \u0648 \u0639\u0645\u0644\u06cc\u0627\u062a \u0622\u0631\u0627\u06cc\u0647\u200c\u0627\u06cc\n <\/li>\n
  • \n 6.<\/span> \u0645\u0641\u0647\u0648\u0645 Tensor \u062f\u0631 PyTorch\n <\/li>\n
  • \n 7.<\/span> \u0627\u06cc\u062c\u0627\u062f \u0648 \u062f\u0633\u062a\u06a9\u0627\u0631\u06cc Tensor\u0647\u0627\n <\/li>\n
  • \n 8.<\/span> \u0639\u0645\u0644\u06cc\u0627\u062a \u0631\u06cc\u0627\u0636\u06cc \u0631\u0648\u06cc Tensor\u0647\u0627\n <\/li>\n
  • \n 9.<\/span> Indexing, Slicing \u0648 Reshaping Tensor\u0647\u0627\n <\/li>\n
  • \n 10.<\/span> \u062c\u0627\u0628\u062c\u0627\u06cc\u06cc Tensor \u0628\u06cc\u0646 CPU \u0648 GPU (CUDA)\n <\/li>\n
  • \n 11.<\/span> \u0645\u0641\u0647\u0648\u0645 \u0645\u062d\u0627\u0633\u0628\u0627\u062a \u06af\u0631\u0627\u062f\u06cc\u0627\u0646 \u0648 Backpropagation\n <\/li>\n
  • \n 12.<\/span> Autograd \u062f\u0631 PyTorch: \u0631\u062f\u06cc\u0627\u0628\u06cc \u0639\u0645\u0644\u06cc\u0627\u062a\n <\/li>\n
  • \n 13.<\/span> `requires_grad` \u0648 `grad_fn`\n <\/li>\n
  • \n 14.<\/span> \u0645\u062d\u0627\u0633\u0628\u0647 \u06af\u0631\u0627\u062f\u06cc\u0627\u0646 \u0628\u0627 `backward()`\n <\/li>\n
  • \n 15.<\/span> \u0645\u062f\u06cc\u0631\u06cc\u062a \u06af\u0631\u0627\u062f\u06cc\u0627\u0646\u200c\u0647\u0627 \u0648 \u0635\u0641\u0631 \u06a9\u0631\u062f\u0646 \u0622\u0646\u200c\u0647\u0627\n <\/li>\n
  • \n 16.<\/span> \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 `torch.no_grad()` \u0648 `model.eval()`\n <\/li>\n
  • \n 17.<\/span> \u0645\u062f\u0644 \u067e\u0631\u0633\u067e\u062a\u0631\u0648\u0646 \u0648 \u0645\u062d\u062f\u0648\u062f\u06cc\u062a\u200c\u0647\u0627\u06cc \u0622\u0646\n <\/li>\n
  • \n 18.<\/span> \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0686\u0646\u062f\u0644\u0627\u06cc\u0647 (MLP)\n <\/li>\n
  • \n 19.<\/span> \u062a\u0648\u0627\u0628\u0639 \u0641\u0639\u0627\u0644\u200c\u0633\u0627\u0632\u06cc: ReLU, Sigmoid, Tanh, Leaky ReLU\n <\/li>\n
  • \n 20.<\/span> \u0644\u0627\u06cc\u0647\u200c\u0647\u0627\u06cc \u062e\u0637\u06cc (Linear Layers) \u062f\u0631 `torch.nn`\n <\/li>\n
  • \n 21.<\/span> \u062a\u0627\u0628\u0639\u200c\u0647\u0627\u06cc \u0647\u0632\u06cc\u0646\u0647 (Loss Functions): MSE, Cross-Entropy\n <\/li>\n
  • \n 22.<\/span> \u0628\u0647\u06cc\u0646\u0647\u200c\u0633\u0627\u0632\u0647\u0627 (Optimizers): SGD, Adam, RMSprop\n <\/li>\n
  • \n 23.<\/span> \u06a9\u0644\u0627\u0633 `torch.nn.Module` \u0628\u0631\u0627\u06cc \u0633\u0627\u062e\u062a \u0645\u062f\u0644\u200c\u0647\u0627\n <\/li>\n
  • \n 24.<\/span> \u0633\u0627\u062e\u062a \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0633\u0627\u062f\u0647 \u0628\u0627 `nn.Sequential`\n <\/li>\n
  • \n 25.<\/span> \u0633\u0641\u0627\u0631\u0634\u06cc\u200c\u0633\u0627\u0632\u06cc \u0645\u062f\u0644 \u0628\u0627 \u0627\u0631\u062b\u200c\u0628\u0631\u06cc \u0627\u0632 `nn.Module`\n <\/li>\n
  • \n 26.<\/span> \u0627\u0633\u062a\u0631\u0627\u062a\u0698\u06cc\u200c\u0647\u0627\u06cc \u0645\u0642\u062f\u0627\u0631\u062f\u0647\u06cc \u0627\u0648\u0644\u06cc\u0647 \u0648\u0632\u0646\u200c\u0647\u0627\n <\/li>\n
  • \n 27.<\/span> \u0622\u0645\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u062f\u0627\u062f\u0647\u200c\u0647\u0627: `torch.utils.data.Dataset`\n <\/li>\n
  • \n 28.<\/span> \u0627\u06cc\u062c\u0627\u062f Batch\u0647\u0627: `torch.utils.data.DataLoader`\n <\/li>\n
  • \n 29.<\/span> \u0686\u0631\u062e\u0647 \u0622\u0645\u0648\u0632\u0634 (Training Loop) \u062f\u0631 PyTorch\n <\/li>\n
  • \n 30.<\/span> \u0627\u0646\u062c\u0627\u0645 Forward Pass \u0648 \u0645\u062d\u0627\u0633\u0628\u0647 Loss\n <\/li>\n
  • \n 31.<\/span> \u0627\u0646\u062c\u0627\u0645 Backward Pass \u0648 \u0628\u0647\u200c\u0631\u0648\u0632\u0631\u0633\u0627\u0646\u06cc \u0648\u0632\u0646\u200c\u0647\u0627\n <\/li>\n
  • \n 32.<\/span> \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0645\u062f\u0644: \u0686\u0631\u062e\u0647 \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc (Validation Loop)\n <\/li>\n
  • \n 33.<\/span> \u0630\u062e\u06cc\u0631\u0647 \u0648 \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0645\u062f\u0644\u200c\u0647\u0627\u06cc PyTorch\n <\/li>\n
  • \n 34.<\/span> \u062a\u0646\u0638\u06cc\u0645 \u0646\u0631\u062e \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0628\u0627 Learning Rate Schedulers\n <\/li>\n
  • \n 35.<\/span> \u067e\u0627\u06cc\u0634 \u0622\u0645\u0648\u0632\u0634 \u0628\u0627 TensorBoard \u06cc\u0627 Weights & Biases (\u0645\u0641\u0647\u0648\u0645\u06cc)\n <\/li>\n
  • \n 36.<\/span> \u0645\u0641\u0647\u0648\u0645 Early Stopping\n <\/li>\n
  • \n 37.<\/span> Overfitting \u0648 Underfitting: \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u0648 \u0645\u0642\u0627\u0628\u0644\u0647\n <\/li>\n
  • \n 38.<\/span> Regularization: L1 \u0648 L2 (Weight Decay)\n <\/li>\n
  • \n 39.<\/span> \u0644\u0627\u06cc\u0647 Dropout \u0628\u0631\u0627\u06cc \u062c\u0644\u0648\u06af\u06cc\u0631\u06cc \u0627\u0632 Overfitting\n <\/li>\n
  • \n 40.<\/span> Batch Normalization: \u06a9\u0627\u0647\u0634 Internal Covariate Shift\n <\/li>\n
  • \n 41.<\/span> \u062a\u0646\u0638\u06cc\u0645 Hyperparameter\u0647\u0627: \u0631\u0648\u06cc\u06a9\u0631\u062f\u0647\u0627\n <\/li>\n
  • \n 42.<\/span> Data Augmentation \u0628\u0631\u0627\u06cc \u0627\u0641\u0632\u0627\u06cc\u0634 \u062a\u0646\u0648\u0639 \u062f\u0627\u062f\u0647\n <\/li>\n
  • \n 43.<\/span> \u0645\u0641\u0647\u0648\u0645 Bias-Variance Tradeoff\n <\/li>\n
  • \n 44.<\/span> \u0639\u06cc\u0628\u200c\u06cc\u0627\u0628\u06cc \u0648 \u062f\u06cc\u0628\u0627\u06af \u06a9\u0631\u062f\u0646 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642\n <\/li>\n
  • \n 45.<\/span> \u0645\u0642\u062f\u0645\u0647\u200c\u0627\u06cc \u0628\u0631 \u0628\u06cc\u0646\u0627\u06cc\u06cc \u06a9\u0627\u0645\u067e\u06cc\u0648\u062a\u0631\n <\/li>\n
  • \n 46.<\/span> \u0639\u0645\u0644\u06cc\u0627\u062a Convolution: \u0641\u06cc\u0644\u062a\u0631\u0647\u0627 \u0648 \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0648\u06cc\u0698\u06af\u06cc\n <\/li>\n
  • \n 47.<\/span> Padding \u0648 Stride \u062f\u0631 Convolution\n <\/li>\n
  • \n 48.<\/span> \u0644\u0627\u06cc\u0647\u200c\u0647\u0627\u06cc Pooling: Max Pooling \u0648 Average Pooling\n <\/li>\n
  • \n 49.<\/span> `torch.nn.Conv2d` \u0648 `torch.nn.MaxPool2d`\n <\/li>\n
  • \n 50.<\/span> \u0633\u0627\u062e\u062a \u06cc\u06a9 CNN \u0633\u0627\u062f\u0647 \u0628\u0631\u0627\u06cc \u062f\u0633\u062a\u0647\u200c\u0628\u0646\u062f\u06cc \u062a\u0635\u0627\u0648\u06cc\u0631\n <\/li>\n
  • \n 51.<\/span> \u062f\u0631\u06a9 \u0644\u0627\u06cc\u0647\u200c\u0647\u0627\u06cc Flatten \u0648 Fully Connected \u062f\u0631 CNN\n <\/li>\n
  • \n 52.<\/span> \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0628\u06cc\u0646\u0627\u06cc\u06cc \u06a9\u0627\u0645\u067e\u06cc\u0648\u062a\u0631 (MNIST, CIFAR-10)\n <\/li>\n
  • \n 53.<\/span> \u0627\u0646\u062a\u0642\u0627\u0644 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc (Transfer Learning)\n <\/li>\n
  • \n 54.<\/span> Fine-tuning \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0627\u0632 \u067e\u06cc\u0634 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647\n <\/li>\n
  • \n 55.<\/span> \u0645\u0639\u0645\u0627\u0631\u06cc\u200c\u0647\u0627\u06cc \u0645\u0639\u0631\u0648\u0641 CNN: LeNet, AlexNet, VGG\n <\/li>\n
  • \n 56.<\/span> ResNet: Residual Connections \u0628\u0631\u0627\u06cc \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0645\u06cc\u0642\n <\/li>\n
  • \n 57.<\/span> Inception Networks (GoogLeNet)\n <\/li>\n
  • \n 58.<\/span> MobileNets \u0648 \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0633\u0628\u06a9 \u0648\u0632\u0646\n <\/li>\n
  • \n 59.<\/span> \u0645\u062f\u0644\u200c\u0647\u0627\u06cc Pre-trained \u062f\u0631 `torchvision.models`\n <\/li>\n
  • \n 60.<\/span> \u0645\u0639\u0645\u0627\u0631\u06cc U-Net \u0628\u0631\u0627\u06cc Semantic Segmentation (\u0645\u0641\u0647\u0648\u0645\u06cc)\n <\/li>\n
  • \n 61.<\/span> \u0645\u0639\u0631\u0641\u06cc Object Detection (R-CNN, YOLO, SSD – \u0645\u0641\u0647\u0648\u0645\u06cc)\n <\/li>\n
  • \n 62.<\/span> \u062f\u0627\u062f\u0647\u200c\u0627\u0641\u0632\u0627\u06cc\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u062f\u0631 \u0628\u06cc\u0646\u0627\u06cc\u06cc \u06a9\u0627\u0645\u067e\u06cc\u0648\u062a\u0631\n <\/li>\n
  • \n 63.<\/span> PyTorch Hub \u0648 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0622\u0645\u0627\u062f\u0647\n <\/li>\n
  • \n 64.<\/span> \u0645\u0639\u0631\u0641\u06cc Vision Transformers (ViT)\n <\/li>\n
  • \n 65.<\/span> \u0645\u0642\u062f\u0645\u0647\u200c\u0627\u06cc \u0628\u0631 \u067e\u0631\u062f\u0627\u0632\u0634 \u0632\u0628\u0627\u0646 \u0637\u0628\u06cc\u0639\u06cc (NLP)\n <\/li>\n
  • \n 66.<\/span> \u0645\u062f\u0644\u200c\u0633\u0627\u0632\u06cc \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u062a\u0648\u0627\u0644\u06cc: \u0686\u0627\u0644\u0634\u200c\u0647\u0627\n <\/li>\n
  • \n 67.<\/span> Recurrent Neural Networks (RNN) \u067e\u0627\u06cc\u0647\n <\/li>\n
  • \n 68.<\/span> \u0645\u0634\u06a9\u0644 Vanishing\/Exploding Gradients \u062f\u0631 RNN\u0647\u0627\n <\/li>\n
  • \n 69.<\/span> \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u062d\u0627\u0641\u0638\u0647 \u0628\u0644\u0646\u062f-\u06a9\u0648\u062a\u0627\u0647 \u0645\u062f\u062a (LSTM)\n <\/li>\n
  • \n 70.<\/span> \u0648\u0627\u062d\u062f\u0647\u0627\u06cc \u0628\u0627\u0632\u06af\u0634\u062a\u06cc \u062f\u0631\u0648\u0627\u0632\u0647\u200c\u0628\u0646\u062f\u06cc \u0634\u062f\u0647 (GRU)\n <\/li>\n
  • \n 71.<\/span> \u067e\u06cc\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc RNN, LSTM, GRU \u062f\u0631 PyTorch\n <\/li>\n
  • \n 72.<\/span> \u0628\u0633\u062a\u0647\u200c\u0628\u0646\u062f\u06cc \u062a\u0648\u0627\u0644\u06cc\u200c\u0647\u0627 (Padding and Packing)\n <\/li>\n
  • \n 73.<\/span> RNN\u0647\u0627\u06cc \u062f\u0648 \u062c\u0647\u062a\u0647 (Bidirectional RNNs)\n <\/li>\n
  • \n 74.<\/span> \u06a9\u0627\u0631\u0628\u0631\u062f\u0647\u0627\u06cc RNN \u062f\u0631 \u062a\u0648\u0644\u06cc\u062f \u0645\u062a\u0646 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0627\u062d\u0633\u0627\u0633\u0627\u062a\n <\/li>\n
  • \n 75.<\/span> Word Embeddings: Word2Vec, GloVe (\u0645\u0641\u0647\u0648\u0645\u06cc)\n <\/li>\n
  • \n 76.<\/span> \u0644\u0627\u06cc\u0647 `torch.nn.Embedding`\n <\/li>\n
  • \n 77.<\/span> Subword Tokenization (BPE, WordPiece – \u0645\u0641\u0647\u0648\u0645\u06cc)\n <\/li>\n
  • \n 78.<\/span> \u0645\u0641\u0647\u0648\u0645 Attention Mechanism\n <\/li>\n
  • \n 79.<\/span> Self-Attention \u062f\u0631 \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc\n <\/li>\n
  • \n 80.<\/span> \u0645\u0639\u0645\u0627\u0631\u06cc Transformer: Encoder-Decoder\n <\/li>\n
  • \n 81.<\/span> Multi-Head Attention \u062f\u0631 Transformer\n <\/li>\n
  • \n 82.<\/span> Positional Encoding \u0628\u0631\u0627\u06cc \u062a\u0648\u0627\u0644\u06cc\u200c\u0647\u0627\n <\/li>\n
  • \n 83.<\/span> \u067e\u06cc\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u06cc\u06a9 \u0644\u0627\u06cc\u0647 Transformer \u062f\u0631 PyTorch\n <\/li>\n
  • \n 84.<\/span> \u0645\u0639\u0631\u0641\u06cc \u0645\u062f\u0644\u200c\u0647\u0627\u06cc Pre-trained Transformer (BERT, GPT – \u0645\u0641\u0647\u0648\u0645\u06cc)\n <\/li>\n
  • \n 85.<\/span> \u0645\u0642\u062f\u0645\u0647\u200c\u0627\u06cc \u0628\u0631 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0645\u0648\u0644\u062f (Generative Models)\n <\/li>\n
  • \n 86.<\/span> Autoencoder\u0647\u0627 \u0628\u0631\u0627\u06cc \u06a9\u0627\u0647\u0634 \u0627\u0628\u0639\u0627\u062f \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0648\u06cc\u0698\u06af\u06cc\n <\/li>\n
  • \n 87.<\/span> Variational Autoencoders (VAEs): \u062a\u0648\u0644\u06cc\u062f \u062f\u0627\u062f\u0647 \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0641\u0636\u0627\u06cc \u0646\u0647\u0627\u0646\n <\/li>\n
  • \n 88.<\/span> \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0645\u0648\u0644\u062f \u062a\u062e\u0627\u0635\u0645\u06cc (GANs): Generator \u0648 Discriminator\n <\/li>\n
  • \n 89.<\/span> \u0686\u0631\u062e\u0647 \u0622\u0645\u0648\u0632\u0634 GAN\u0647\u0627 \u0648 \u0686\u0627\u0644\u0634\u200c\u0647\u0627\u06cc \u0622\u0646\n <\/li>\n
  • \n 90.<\/span> Conditional GANs\n <\/li>\n
  • \n 91.<\/span> \u06a9\u0627\u0631\u0628\u0631\u062f\u0647\u0627\u06cc GANs: \u062a\u0648\u0644\u06cc\u062f \u062a\u0635\u0648\u06cc\u0631\u060c Style Transfer\n <\/li>\n
  • \n 92.<\/span> \u0622\u0645\u0648\u0632\u0634 \u062a\u0648\u0632\u06cc\u0639 \u0634\u062f\u0647 (Distributed Training) \u062f\u0631 PyTorch (\u0645\u0641\u0647\u0648\u0645\u06cc)\n <\/li>\n
  • \n 93.<\/span> \u0628\u0647\u06cc\u0646\u0647\u200c\u0633\u0627\u0632\u06cc \u0645\u062f\u0644 \u0628\u0631\u0627\u06cc \u062f\u06cc\u067e\u0644\u0648\u06cc: TorchScript \u0648 ONNX\n <\/li>\n
  • \n 94.<\/span> \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 GPU\u0647\u0627\u06cc \u0686\u0646\u062f\u06af\u0627\u0646\u0647 \u0628\u0627 `nn.DataParallel` (\u0645\u0641\u0647\u0648\u0645\u06cc)\n <\/li>\n
  • \n 95.<\/span> \u0645\u0639\u0631\u0641\u06cc Explainable AI (XAI) \u0648 Interpretability\n <\/li>\n
  • \n 96.<\/span> \u0627\u062e\u0644\u0627\u0642 \u062f\u0631 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642\n <\/li>\n
  • \n 97.<\/span> \u062a\u0639\u0635\u0628 \u0648 \u0639\u062f\u0627\u0644\u062a \u062f\u0631 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc ML\n <\/li>\n
  • \n 98.<\/span> \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0627\u06a9\u0648\u0633\u06cc\u0633\u062a\u0645 PyTorch (TorchVision, TorchText, TorchAudio)\n <\/li>\n
  • \n 99.<\/span> \u0645\u0631\u0648\u0631\u06cc \u0628\u0631 \u062c\u062f\u06cc\u062f\u062a\u0631\u06cc\u0646 \u067e\u06cc\u0634\u0631\u0641\u062a\u200c\u0647\u0627 \u0648 \u0631\u0648\u0646\u062f\u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642\n <\/li>\n
  • \n 100.<\/span> \u067e\u0631\u0648\u0698\u0647\u200c\u0647\u0627\u06cc \u0639\u0645\u0644\u06cc \u0648 \u0645\u0646\u0627\u0628\u0639 \u0628\u0631\u0627\u06cc \u0627\u062f\u0627\u0645\u0647 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc\n <\/li>\n<\/ul>\n<\/div>\n

    \n
    \n
    \n
    \n
    \n \u062f\u0648\u0631\u0647 \u062c\u0627\u0645\u0639 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0627 PyTorch: \u0622\u06cc\u0646\u062f\u0647 \u0631\u0627 \u06a9\u062f \u0628\u0632\u0646\u06cc\u062f<\/title><br \/>\n<\/head><br \/>\n<body><\/p>\n<h2>\u0622\u06cc\u0646\u062f\u0647 \u0631\u0627 \u06a9\u062f \u0628\u0632\u0646\u06cc\u062f: \u062f\u0648\u0631\u0647 \u062c\u0627\u0645\u0639 \u0648 \u067e\u0631\u0648\u0698\u0647\u200c\u0645\u062d\u0648\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0627 PyTorch<\/h2>\n<p>\n \u0628\u0647 \u0627\u0646\u0642\u0644\u0627\u0628 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u062e\u0648\u0634 \u0622\u0645\u062f\u06cc\u062f! \u062f\u0646\u06cc\u0627\u06cc \u062a\u06a9\u0646\u0648\u0644\u0648\u0698\u06cc \u0628\u0627 \u0633\u0631\u0639\u062a\u06cc \u0628\u0627\u0648\u0631\u0646\u06a9\u0631\u062f\u0646\u06cc \u062f\u0631 \u062d\u0627\u0644 \u062f\u06af\u0631\u06af\u0648\u0646\u06cc \u0627\u0633\u062a \u0648 \u062f\u0631 \u0642\u0644\u0628 \u0627\u06cc\u0646 \u062a\u062d\u0648\u0644\u060c \u00ab\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642\u00bb (Deep Learning) \u0642\u0631\u0627\u0631 \u062f\u0627\u0631\u062f. \u0627\u0632 \u062e\u0648\u062f\u0631\u0648\u0647\u0627\u06cc \u062e\u0648\u062f\u0631\u0627\u0646 \u0648 \u062f\u0633\u062a\u06cc\u0627\u0631\u0647\u0627\u06cc \u0635\u0648\u062a\u06cc \u0647\u0648\u0634\u0645\u0646\u062f \u06af\u0631\u0641\u062a\u0647 \u062a\u0627 \u062a\u0634\u062e\u06cc\u0635 \u0628\u06cc\u0645\u0627\u0631\u06cc\u200c\u0647\u0627 \u0648 \u062e\u0644\u0642 \u0622\u062b\u0627\u0631 \u0647\u0646\u0631\u06cc\u060c \u0647\u0645\u06af\u06cc \u0642\u062f\u0631\u062a \u062e\u0648\u062f \u0631\u0627 \u0627\u0632 \u0627\u06cc\u0646 \u0641\u0646\u0627\u0648\u0631\u06cc \u0634\u06af\u0641\u062a\u200c\u0627\u0646\u06af\u06cc\u0632 \u0645\u06cc\u200c\u06af\u06cc\u0631\u0646\u062f. \u0627\u06a9\u0646\u0648\u0646 \u0627\u06cc\u0646 \u0641\u0631\u0635\u062a \u0628\u06cc\u200c\u0646\u0638\u06cc\u0631 \u0628\u0631\u0627\u06cc \u0634\u0645\u0627 \u0641\u0631\u0627\u0647\u0645 \u0634\u062f\u0647 \u062a\u0627 \u0646\u0647 \u062a\u0646\u0647\u0627 \u06cc\u06a9 \u062a\u0645\u0627\u0634\u0627\u06af\u0631\u060c \u0628\u0644\u06a9\u0647 \u06cc\u06a9\u06cc \u0627\u0632 \u0645\u0639\u0645\u0627\u0631\u0627\u0646 \u0627\u06cc\u0646 \u0622\u06cc\u0646\u062f\u0647 \u0647\u06cc\u062c\u0627\u0646\u200c\u0627\u0646\u06af\u06cc\u0632 \u0628\u0627\u0634\u06cc\u062f.\n <\/p>\n<p>\n \u062f\u0648\u0631\u0647 \u00ab\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0627 PyTorch\u00bb \u06cc\u06a9 \u0645\u0633\u06cc\u0631 \u0622\u0645\u0648\u0632\u0634\u06cc \u062c\u0627\u0645\u0639 \u0648 \u06a9\u0627\u0645\u0644\u0627\u064b \u0639\u0645\u0644\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0634\u0645\u0627 \u0631\u0627 \u0627\u0632 \u0633\u0637\u062d \u0645\u0628\u062a\u062f\u06cc \u0628\u0647 \u06cc\u06a9 \u0645\u062a\u062e\u0635\u0635 \u062d\u0631\u0641\u0647\u200c\u0627\u06cc \u062f\u0631 \u062d\u0648\u0632\u0647 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0645\u06cc\u200c\u06a9\u0646\u062f. \u0645\u0627 \u0645\u0639\u062a\u0642\u062f\u06cc\u0645 \u06a9\u0647 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0631\u0627\u0647 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc\u060c \u0627\u0646\u062c\u0627\u0645 \u062f\u0627\u062f\u0646 \u0627\u0633\u062a. \u0628\u0647 \u0647\u0645\u06cc\u0646 \u062f\u0644\u06cc\u0644\u060c \u0627\u06cc\u0646 \u062f\u0648\u0631\u0647 \u0628\u0631 \u067e\u0627\u06cc\u0647 \u067e\u0631\u0648\u0698\u0647\u200c\u0647\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc \u0648 \u0686\u0627\u0644\u0634\u200c\u0647\u0627\u06cc \u062c\u0630\u0627\u0628 \u0637\u0631\u0627\u062d\u06cc \u0634\u062f\u0647 \u062a\u0627 \u0634\u0645\u0627 \u062f\u0627\u0646\u0634 \u062a\u0626\u0648\u0631\u06cc \u0631\u0627 \u0628\u0647 \u0645\u0647\u0627\u0631\u062a \u0639\u0645\u0644\u06cc \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u06cc\u062f. \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 PyTorch\u060c \u06cc\u06a9\u06cc \u0627\u0632 \u0642\u062f\u0631\u062a\u0645\u0646\u062f\u062a\u0631\u06cc\u0646 \u0648 \u0645\u062d\u0628\u0648\u0628\u200c\u062a\u0631\u06cc\u0646 \u0641\u0631\u06cc\u0645\u0648\u0631\u06a9\u200c\u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u06a9\u0647 \u062a\u0648\u0633\u0637 \u063a\u0648\u0644\u200c\u0647\u0627\u06cc \u062a\u06a9\u0646\u0648\u0644\u0648\u0698\u06cc \u0645\u0627\u0646\u0646\u062f \u0645\u062a\u0627 (\u0641\u06cc\u0633\u0628\u0648\u06a9) \u0648 \u062a\u0633\u0644\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f\u060c \u0634\u0645\u0627 \u0627\u0628\u0632\u0627\u0631\u06cc \u062f\u0631 \u062f\u0633\u062a \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u0627\u0634\u062a \u06a9\u0647 \u062f\u0631 \u0644\u0628\u0647 \u0639\u0644\u0645 \u0648 \u0635\u0646\u0639\u062a \u0642\u0631\u0627\u0631 \u062f\u0627\u0631\u062f.\n <\/p>\n<h2>\u062f\u0631\u0628\u0627\u0631\u0647 \u062f\u0648\u0631\u0647: \u0627\u0632 \u062a\u0626\u0648\u0631\u06cc \u062a\u0627 \u0633\u0627\u062e\u062a \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0647\u0648\u0634\u0645\u0646\u062f \u0648\u0627\u0642\u0639\u06cc<\/h2>\n<p>\n \u0627\u06cc\u0646 \u062f\u0648\u0631\u0647 \u0641\u0642\u0637 \u0645\u062c\u0645\u0648\u0639\u0647\u200c\u0627\u06cc \u0627\u0632 \u0648\u06cc\u062f\u0626\u0648\u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0646\u06cc\u0633\u062a\u061b \u0628\u0644\u06a9\u0647 \u06cc\u06a9 \u0646\u0642\u0634\u0647 \u0631\u0627\u0647 \u06a9\u0627\u0645\u0644 \u0628\u0631\u0627\u06cc \u0633\u0641\u0631 \u0634\u0645\u0627 \u062f\u0631 \u062f\u0646\u06cc\u0627\u06cc \u067e\u06cc\u0686\u06cc\u062f\u0647 \u0648 \u062c\u0630\u0627\u0628 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0627\u0633\u062a. \u0645\u0627 \u0627\u0632 \u0645\u0641\u0627\u0647\u06cc\u0645 \u067e\u0627\u06cc\u0647\u200c\u0627\u06cc \u0631\u06cc\u0627\u0636\u06cc\u0627\u062a \u0648 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0634\u0631\u0648\u0639 \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645 \u0648 \u0642\u062f\u0645 \u0628\u0647 \u0642\u062f\u0645 \u0628\u0647 \u0633\u0631\u0627\u063a \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0645\u0635\u0646\u0648\u0639\u06cc\u060c \u0628\u06cc\u0646\u0627\u06cc\u06cc \u06a9\u0627\u0645\u067e\u06cc\u0648\u062a\u0631\u060c \u067e\u0631\u062f\u0627\u0632\u0634 \u0632\u0628\u0627\u0646 \u0637\u0628\u06cc\u0639\u06cc \u0648 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0645\u0648\u0644\u062f \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0645\u06cc\u200c\u0631\u0648\u06cc\u0645. \u0647\u0631 \u0641\u0635\u0644 \u0628\u0627 \u0647\u062f\u0641 \u062d\u0644 \u06cc\u06a9 \u0645\u0633\u0626\u0644\u0647 \u0648\u0627\u0642\u0639\u06cc \u0637\u0631\u0627\u062d\u06cc \u0634\u062f\u0647 \u0648 \u0634\u0645\u0627 \u062f\u0631 \u0637\u0648\u0644 \u062f\u0648\u0631\u0647\u060c \u0686\u0646\u062f\u06cc\u0646 \u067e\u0631\u0648\u0698\u0647 \u06a9\u0627\u0631\u0628\u0631\u062f\u06cc \u0648 \u0686\u0634\u0645\u06af\u06cc\u0631 \u0631\u0627 \u0627\u0632 \u0635\u0641\u0631 \u062a\u0627 \u0635\u062f \u067e\u06cc\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u06a9\u0631\u062f. \u0627\u06cc\u0646 \u0631\u0648\u06cc\u06a9\u0631\u062f \u062a\u0636\u0645\u06cc\u0646 \u0645\u06cc\u200c\u06a9\u0646\u062f \u06a9\u0647 \u067e\u0633 \u0627\u0632 \u067e\u0627\u06cc\u0627\u0646 \u062f\u0648\u0631\u0647\u060c \u0634\u0645\u0627 \u0646\u0647\u200c\u062a\u0646\u0647\u0627 \u0645\u0641\u0627\u0647\u06cc\u0645 \u0631\u0627 \u062f\u0631\u06a9 \u06a9\u0631\u062f\u0647\u200c\u0627\u06cc\u062f\u060c \u0628\u0644\u06a9\u0647 \u0627\u0639\u062a\u0645\u0627\u062f \u0628\u0647 \u0646\u0641\u0633 \u0644\u0627\u0632\u0645 \u0628\u0631\u0627\u06cc \u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062f\u0646\u06cc\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc \u0631\u0627 \u0646\u06cc\u0632 \u0628\u0647 \u062f\u0633\u062a \u0622\u0648\u0631\u062f\u0647\u200c\u0627\u06cc\u062f.\n <\/p>\n<h2>\u0645\u0648\u0636\u0648\u0639\u0627\u062a \u06a9\u0644\u06cc\u062f\u06cc \u06a9\u0647 \u062f\u0631 \u0627\u06cc\u0646 \u062f\u0648\u0631\u0647 \u0641\u0631\u0627 \u062e\u0648\u0627\u0647\u06cc\u062f \u06af\u0631\u0641\u062a:<\/h2>\n<ul>\n<li>\u0645\u0628\u0627\u0646\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc \u0639\u0644\u0645 \u062f\u0627\u062f\u0647 \u0648 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647\u200c\u0647\u0627\u06cc \u06a9\u0644\u06cc\u062f\u06cc (NumPy, Pandas, Matplotlib)<\/li>\n<li>\u0631\u06cc\u0627\u0636\u06cc\u0627\u062a \u0636\u0631\u0648\u0631\u06cc \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 (\u062c\u0628\u0631 \u062e\u0637\u06cc\u060c \u062d\u0633\u0627\u0628\u0627\u0646 \u0648 \u0622\u0645\u0627\u0631)<\/li>\n<li>\u0645\u0641\u0627\u0647\u06cc\u0645 \u067e\u0627\u06cc\u0647\u200c\u0627\u06cc \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0645\u0635\u0646\u0648\u0639\u06cc (\u0646\u0648\u0631\u0648\u0646\u200c\u0647\u0627\u060c \u0644\u0627\u06cc\u0647\u200c\u0647\u0627\u060c \u062a\u0648\u0627\u0628\u0639 \u0641\u0639\u0627\u0644\u200c\u0633\u0627\u0632\u06cc)<\/li>\n<li>\u0628\u0647\u06cc\u0646\u0647\u200c\u0633\u0627\u0632\u06cc \u0648 \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644\u200c\u0647\u0627 (\u06af\u0631\u0627\u062f\u06cc\u0627\u0646 \u06a9\u0627\u0647\u0634\u06cc\u060c Backpropagation\u060c Regularization)<\/li>\n<li>\u06a9\u0627\u0631 \u0628\u0627 \u0641\u0631\u06cc\u0645\u0648\u0631\u06a9 \u0642\u062f\u0631\u062a\u0645\u0646\u062f PyTorch \u0627\u0632 \u0645\u0642\u062f\u0645\u0627\u062a\u06cc \u062a\u0627 \u067e\u06cc\u0634\u0631\u0641\u062a\u0647<\/li>\n<li>\u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u06cc (CNNs) \u0628\u0631\u0627\u06cc \u067e\u0631\u0648\u0698\u0647\u200c\u0647\u0627\u06cc \u0628\u06cc\u0646\u0627\u06cc\u06cc \u06a9\u0627\u0645\u067e\u06cc\u0648\u062a\u0631<\/li>\n<li>\u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0628\u0627\u0632\u06af\u0634\u062a\u06cc (RNNs)\u060c LSTM \u0648 GRU \u0628\u0631\u0627\u06cc \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u062a\u0631\u062a\u06cc\u0628\u06cc \u0648 \u0645\u062a\u0646<\/li>\n<li>\u0645\u0639\u0645\u0627\u0631\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u062a\u0631\u0646\u0633\u0641\u0648\u0631\u0645\u0631\u0647\u0627 (Transformers) \u0648 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 Attention<\/li>\n<li>\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0627\u0646\u062a\u0642\u0627\u0644\u06cc (Transfer Learning) \u0628\u0631\u0627\u06cc \u062f\u0633\u062a\u06cc\u0627\u0628\u06cc \u0628\u0647 \u0646\u062a\u0627\u06cc\u062c \u0634\u06af\u0641\u062a\u200c\u0627\u0646\u06af\u06cc\u0632 \u0628\u0627 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u06a9\u0645<\/li>\n<li>\u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0632\u0628\u0627\u0646\u06cc \u0628\u0632\u0631\u06af (LLMs) \u0648 \u0645\u0628\u0627\u0646\u06cc \u067e\u0631\u062f\u0627\u0632\u0634 \u0632\u0628\u0627\u0646 \u0637\u0628\u06cc\u0639\u06cc (NLP)<\/li>\n<li>\u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0645\u0648\u0644\u062f \u062a\u062e\u0627\u0635\u0645\u06cc (GANs) \u0628\u0631\u0627\u06cc \u062a\u0648\u0644\u06cc\u062f \u062a\u0635\u0648\u06cc\u0631 \u0648 \u062f\u0627\u062f\u0647<\/li>\n<li>\u0627\u0633\u062a\u0642\u0631\u0627\u0631 (Deployment) \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u062f\u0631 \u0645\u062d\u06cc\u0637 \u0648\u0627\u0642\u0639\u06cc<\/li>\n<\/ul>\n<h2>\u0627\u06cc\u0646 \u062f\u0648\u0631\u0647 \u0628\u0631\u0627\u06cc \u0686\u0647 \u06a9\u0633\u0627\u0646\u06cc \u0645\u0646\u0627\u0633\u0628 \u0627\u0633\u062a\u061f<\/h2>\n<ul>\n<li><b>\u062f\u0627\u0646\u0634\u062c\u0648\u06cc\u0627\u0646 \u0631\u0634\u062a\u0647\u200c\u0647\u0627\u06cc \u0645\u0647\u0646\u062f\u0633\u06cc \u0648 \u0639\u0644\u0648\u0645 \u06a9\u0627\u0645\u067e\u06cc\u0648\u062a\u0631:<\/b> \u06a9\u0647 \u0645\u06cc\u200c\u062e\u0648\u0627\u0647\u0646\u062f \u062f\u0627\u0646\u0634 \u0622\u06a9\u0627\u062f\u0645\u06cc\u06a9 \u062e\u0648\u062f \u0631\u0627 \u0628\u0627 \u0645\u0647\u0627\u0631\u062a\u200c\u0647\u0627\u06cc \u0639\u0645\u0644\u06cc \u0648 \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632 \u0628\u0627\u0632\u0627\u0631 \u06a9\u0627\u0631 \u062a\u06a9\u0645\u06cc\u0644 \u06a9\u0646\u0646\u062f.<\/li>\n<li><b>\u0628\u0631\u0646\u0627\u0645\u0647\u200c\u0646\u0648\u06cc\u0633\u0627\u0646 \u0648 \u062a\u0648\u0633\u0639\u0647\u200c\u062f\u0647\u0646\u062f\u06af\u0627\u0646 \u0646\u0631\u0645\u200c\u0627\u0641\u0632\u0627\u0631:<\/b> \u06a9\u0647 \u0642\u0635\u062f \u062f\u0627\u0631\u0646\u062f \u0628\u0627 \u0645\u0647\u0627\u062c\u0631\u062a \u0628\u0647 \u062d\u0648\u0632\u0647 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc\u060c \u0645\u0633\u06cc\u0631 \u0634\u063a\u0644\u06cc \u062e\u0648\u062f \u0631\u0627 \u0645\u062a\u062d\u0648\u0644 \u06a9\u0646\u0646\u062f.<\/li>\n<li><b>\u062a\u062d\u0644\u06cc\u0644\u06af\u0631\u0627\u0646 \u0648 \u062f\u0627\u0646\u0634\u0645\u0646\u062f\u0627\u0646 \u062f\u0627\u062f\u0647:<\/b> \u06a9\u0647 \u0645\u06cc\u200c\u062e\u0648\u0627\u0647\u0646\u062f \u0628\u0627 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642\u060c \u062c\u0639\u0628\u0647 \u0627\u0628\u0632\u0627\u0631 \u062e\u0648\u062f \u0631\u0627 \u0628\u0631\u0627\u06cc \u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u067e\u06cc\u0686\u06cc\u062f\u0647\u200c\u062a\u0631 \u06af\u0633\u062a\u0631\u0634 \u062f\u0647\u0646\u062f.<\/li>\n<li><b>\u0645\u062d\u0642\u0642\u0627\u0646 \u0648 \u0639\u0644\u0627\u0642\u0647\u200c\u0645\u0646\u062f\u0627\u0646 \u0628\u0647 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc:<\/b> \u06a9\u0647 \u0628\u0647 \u062f\u0646\u0628\u0627\u0644 \u06cc\u06a9 \u0645\u0646\u0628\u0639 \u062c\u0627\u0645\u0639 \u0648 \u0633\u0627\u062e\u062a\u0627\u0631\u06cc\u0627\u0641\u062a\u0647 \u0628\u0631\u0627\u06cc \u062f\u0631\u06a9 \u0639\u0645\u06cc\u0642 \u062c\u062f\u06cc\u062f\u062a\u0631\u06cc\u0646 \u062f\u0633\u062a\u0627\u0648\u0631\u062f\u0647\u0627\u06cc \u0627\u06cc\u0646 \u062d\u0648\u0632\u0647 \u0647\u0633\u062a\u0646\u062f.<\/li>\n<li><b>\u0645\u062f\u06cc\u0631\u0627\u0646 \u0645\u062d\u0635\u0648\u0644 \u0648 \u06a9\u0627\u0631\u0622\u0641\u0631\u06cc\u0646\u0627\u0646:<\/b> \u06a9\u0647 \u0645\u06cc\u200c\u062e\u0648\u0627\u0647\u0646\u062f \u0628\u0627 \u062f\u0631\u06a9 \u0642\u0627\u0628\u0644\u06cc\u062a\u200c\u0647\u0627\u06cc AI\u060c \u0645\u062d\u0635\u0648\u0644\u0627\u062a \u0648 \u062e\u062f\u0645\u0627\u062a \u0646\u0648\u0622\u0648\u0631\u0627\u0646\u0647\u200c\u0627\u06cc \u0631\u0627 \u062e\u0644\u0642 \u06a9\u0646\u0646\u062f.<\/li>\n<\/ul>\n<h2>\u0686\u0631\u0627 \u0628\u0627\u06cc\u062f \u062f\u0631 \u0627\u06cc\u0646 \u062f\u0648\u0631\u0647 \u0634\u0631\u06a9\u062a \u06a9\u0646\u06cc\u062f\u061f<\/h2>\n<h3>\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u067e\u0631\u0648\u0698\u0647\u200c\u0645\u062d\u0648\u0631 \u0648 \u06a9\u0627\u0645\u0644\u0627\u064b \u0639\u0645\u0644\u06cc<\/h3>\n<p>\n \u0627\u0632 \u062a\u0626\u0648\u0631\u06cc\u200c\u0647\u0627\u06cc \u0627\u0646\u062a\u0632\u0627\u0639\u06cc \u0648 \u062e\u0633\u062a\u0647\u200c\u06a9\u0646\u0646\u062f\u0647 \u062e\u0628\u0631\u06cc \u0646\u06cc\u0633\u062a! \u0634\u0645\u0627 \u0627\u0632 \u0647\u0645\u0627\u0646 \u0627\u0628\u062a\u062f\u0627 \u062f\u0633\u062a \u0628\u0647 \u06a9\u062f \u0645\u06cc\u200c\u0634\u0648\u06cc\u062f \u0648 \u0628\u0627 \u0633\u0627\u062e\u062a \u067e\u0631\u0648\u0698\u0647\u200c\u0647\u0627\u06cc\u06cc \u0645\u0627\u0646\u0646\u062f \u062a\u0634\u062e\u06cc\u0635 \u0627\u0634\u06cc\u0627\u0621 \u062f\u0631 \u062a\u0635\u0627\u0648\u06cc\u0631\u060c \u062a\u062d\u0644\u06cc\u0644 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0645\u062a\u0646\u060c \u062a\u0648\u0644\u06cc\u062f \u062a\u0635\u0627\u0648\u06cc\u0631 \u062c\u062f\u06cc\u062f \u0648 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u0633\u0631\u06cc\u200c\u0647\u0627\u06cc \u0632\u0645\u0627\u0646\u06cc\u060c \u0645\u0641\u0627\u0647\u06cc\u0645 \u0631\u0627 \u0628\u0647 \u0635\u0648\u0631\u062a \u0639\u0645\u06cc\u0642 \u0648 \u06a9\u0627\u0631\u0628\u0631\u062f\u06cc \u06cc\u0627\u062f \u0645\u06cc\u200c\u06af\u06cc\u0631\u06cc\u062f.\n <\/p>\n<h3>\u062c\u0627\u0645\u0639\u200c\u062a\u0631\u06cc\u0646 \u0633\u0631\u0641\u0635\u0644 \u0622\u0645\u0648\u0632\u0634\u06cc \u0628\u0647 \u0632\u0628\u0627\u0646 \u0641\u0627\u0631\u0633\u06cc<\/h3>\n<p>\n \u0628\u0627 \u0628\u06cc\u0634 \u0627\u0632 \u06f1\u06f0\u06f0 \u0633\u0631\u0641\u0635\u0644 \u062f\u0642\u06cc\u0642 \u0648 \u062c\u0632\u0626\u06cc\u060c \u0627\u06cc\u0646 \u062f\u0648\u0631\u0647 \u062a\u0645\u0627\u0645 \u0645\u0628\u0627\u062d\u062b \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632 \u0628\u0631\u0627\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0634\u062f\u0646 \u0628\u0647 \u06cc\u06a9 \u0645\u062a\u062e\u0635\u0635 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0631\u0627 \u067e\u0648\u0634\u0634 \u0645\u06cc\u200c\u062f\u0647\u062f. \u0645\u0627 \u0647\u06cc\u0686 \u0646\u06a9\u062a\u0647\u200c\u0627\u06cc \u0631\u0627 \u0646\u0627\u06af\u0641\u062a\u0647 \u0628\u0627\u0642\u06cc \u0646\u06af\u0630\u0627\u0634\u062a\u0647\u200c\u0627\u06cc\u0645 \u0648 \u0634\u0645\u0627 \u0631\u0627 \u0628\u0631\u0627\u06cc \u0647\u0631 \u0686\u0627\u0644\u0634\u06cc \u062f\u0631 \u062f\u0646\u06cc\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc \u0622\u0645\u0627\u062f\u0647 \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645.\n <\/p>\n<h3>\u062a\u0633\u0644\u0637 \u0628\u0631 PyTorch\u060c \u0627\u0628\u0632\u0627\u0631 \u0645\u062d\u0628\u0648\u0628 \u063a\u0648\u0644\u200c\u0647\u0627\u06cc \u062a\u06a9\u0646\u0648\u0644\u0648\u0698\u06cc<\/h3>\n<p>\n PyTorch \u0628\u0647 \u062f\u0644\u06cc\u0644 \u0627\u0646\u0639\u0637\u0627\u0641\u200c\u067e\u0630\u06cc\u0631\u06cc \u0648 \u0642\u062f\u0631\u062a \u0628\u0627\u0644\u0627\u060c \u0628\u0647 \u0627\u0646\u062a\u062e\u0627\u0628 \u0627\u0648\u0644 \u0645\u062d\u0642\u0642\u0627\u0646 \u0648 \u0634\u0631\u06a9\u062a\u200c\u0647\u0627\u06cc \u067e\u06cc\u0634\u0631\u0648 \u062f\u0631 \u062d\u0648\u0632\u0647 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0634\u062f\u0647 \u0627\u0633\u062a. \u0628\u0627 \u062a\u0633\u0644\u0637 \u0628\u0631 \u0627\u06cc\u0646 \u0641\u0631\u06cc\u0645\u0648\u0631\u06a9\u060c \u0634\u0645\u0627 \u0645\u0647\u0627\u0631\u062a\u06cc \u0631\u0627 \u06a9\u0633\u0628 \u0645\u06cc\u200c\u06a9\u0646\u06cc\u062f \u06a9\u0647 \u0645\u0633\u062a\u0642\u06cc\u0645\u0627\u064b \u062a\u0648\u0633\u0637 \u0634\u0631\u06a9\u062a\u200c\u0647\u0627\u06cc\u06cc \u0645\u0627\u0646\u0646\u062f \u0645\u062a\u0627\u060c \u062a\u0633\u0644\u0627\u060c \u0627\u0648\u0628\u0631 \u0648 OpenAI \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.\n <\/p>\n<h3>\u0622\u06cc\u0646\u062f\u0647 \u0634\u063a\u0644\u06cc \u062e\u0648\u062f \u0631\u0627 \u062a\u0636\u0645\u06cc\u0646 \u06a9\u0646\u06cc\u062f<\/h3>\n<p>\n \u0645\u062a\u062e\u0635\u0635\u0627\u0646 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u062c\u0632\u0648 \u067e\u0631\u062a\u0642\u0627\u0636\u0627\u062a\u0631\u06cc\u0646 \u0648 \u067e\u0631\u062f\u0631\u0622\u0645\u062f\u062a\u0631\u06cc\u0646 \u0646\u06cc\u0631\u0648\u0647\u0627\u06cc \u0645\u062a\u062e\u0635\u0635 \u062f\u0631 \u0633\u0631\u0627\u0633\u0631 \u062c\u0647\u0627\u0646 \u0647\u0633\u062a\u0646\u062f. \u0628\u0627 \u06af\u0630\u0631\u0627\u0646\u062f\u0646 \u0627\u06cc\u0646 \u062f\u0648\u0631\u0647\u060c \u0634\u0645\u0627 \u06cc\u06a9 \u0633\u0631\u0645\u0627\u06cc\u0647\u200c\u06af\u0630\u0627\u0631\u06cc \u0645\u0637\u0645\u0626\u0646 \u0631\u0648\u06cc \u0622\u06cc\u0646\u062f\u0647 \u062e\u0648\u062f \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc\u200c\u062f\u0647\u06cc\u062f \u0648 \u062f\u0631\u0647\u0627\u06cc \u0641\u0631\u0635\u062a\u200c\u0647\u0627\u06cc \u0634\u063a\u0644\u06cc \u0634\u06af\u0641\u062a\u200c\u0627\u0646\u06af\u06cc\u0632\u06cc \u0631\u0627 \u0628\u0647 \u0631\u0648\u06cc \u062e\u0648\u062f \u0628\u0627\u0632 \u0645\u06cc\u200c\u06a9\u0646\u06cc\u062f.\n <\/p>\n<h2>\u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u0633\u0631\u0641\u0635\u0644\u200c\u0647\u0627\u06cc \u062c\u0627\u0645\u0639 \u062f\u0648\u0631\u0647 (\u0628\u06cc\u0634 \u0627\u0632 \u06f1\u06f0\u06f0 \u062f\u0631\u0633\u0646\u0627\u0645\u0647)<\/h2>\n<p>\u0627\u06cc\u0646 \u062f\u0648\u0631\u0647 \u062f\u0631 \u0686\u0646\u062f\u06cc\u0646 \u0628\u062e\u0634 \u0627\u0635\u0644\u06cc \u0633\u0627\u0632\u0645\u0627\u0646\u062f\u0647\u06cc \u0634\u062f\u0647 \u062a\u0627 \u06cc\u06a9 \u0645\u0633\u06cc\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0631\u0648\u0627\u0646 \u0648 \u0645\u0646\u0637\u0642\u06cc \u0631\u0627 \u0628\u0631\u0627\u06cc \u0634\u0645\u0627 \u0641\u0631\u0627\u0647\u0645 \u06a9\u0646\u062f. \u062f\u0631 \u0632\u06cc\u0631\u060c \u0646\u06af\u0627\u0647\u06cc \u06af\u0630\u0631\u0627 \u0628\u0647 \u0628\u0631\u062e\u06cc \u0627\u0632 \u0633\u0631\u0641\u0635\u0644\u200c\u0647\u0627\u06cc \u06a9\u0644\u06cc\u062f\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u0634\u062a:<\/p>\n<h3>\u0628\u062e\u0634 \u0627\u0648\u0644: \u0645\u0628\u0627\u0646\u06cc \u0648 \u0645\u0642\u062f\u0645\u0627\u062a<\/h3>\n<ul>\n<li>\u0646\u0635\u0628 \u0648 \u0631\u0627\u0647\u200c\u0627\u0646\u062f\u0627\u0632\u06cc \u0645\u062d\u06cc\u0637 \u062a\u0648\u0633\u0639\u0647 (Python, Jupyter, VS Code)<\/li>\n<li>\u0622\u0645\u0648\u0632\u0634 \u0633\u0631\u06cc\u0639 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc<\/li>\n<li>\u06a9\u0627\u0631 \u0628\u0627 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 NumPy \u0628\u0631\u0627\u06cc \u0645\u062d\u0627\u0633\u0628\u0627\u062a \u0639\u062f\u062f\u06cc<\/li>\n<li>\u0645\u0628\u0627\u0646\u06cc \u06a9\u0627\u0631 \u0628\u0627 Pandas \u0648 \u0645\u0635\u0648\u0631\u0633\u0627\u0632\u06cc \u062f\u0627\u062f\u0647 \u0628\u0627 Matplotlib<\/li>\n<li>\u0645\u0641\u0627\u0647\u06cc\u0645 \u06a9\u0644\u06cc\u062f\u06cc \u062c\u0628\u0631 \u062e\u0637\u06cc \u0648 \u062d\u0633\u0627\u0628\u0627\u0646 \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646<\/li>\n<\/ul>\n<h3>\u0628\u062e\u0634 \u062f\u0648\u0645: \u0648\u0631\u0648\u062f \u0628\u0647 \u062f\u0646\u06cc\u0627\u06cc \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0648 PyTorch<\/h3>\n<ul>\n<li>\u062a\u0646\u0633\u0648\u0631\u0647\u0627 \u062f\u0631 PyTorch: \u0633\u0627\u062e\u062a\u060c \u0639\u0645\u0644\u06cc\u0627\u062a \u0648 \u0645\u062d\u0627\u0633\u0628\u0627\u062a<\/li>\n<li>\u0645\u062d\u0627\u0633\u0628\u0647 \u0645\u0634\u062a\u0642 \u062e\u0648\u062f\u06a9\u0627\u0631 \u0628\u0627 Autograd<\/li>\n<li>\u0633\u0627\u062e\u062a \u0627\u0648\u0644\u06cc\u0646 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u067e\u0631\u0633\u067e\u062a\u0631\u0648\u0646 \u0686\u0646\u062f\u0644\u0627\u06cc\u0647 (MLP)<\/li>\n<li>\u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644: \u062a\u0627\u0628\u0639 \u0647\u0632\u06cc\u0646\u0647\u060c \u0628\u0647\u06cc\u0646\u0647\u200c\u0633\u0627\u0632\u0647\u0627 \u0648 \u0686\u0631\u062e\u0647 \u0622\u0645\u0648\u0632\u0634<\/li>\n<li>\u0645\u0641\u0627\u0647\u06cc\u0645 \u067e\u06cc\u0634\u0631\u0641\u062a\u0647: \u0628\u06cc\u0634\u200c\u0628\u0631\u0627\u0632\u0634 (Overfitting)\u060c \u06a9\u0645\u200c\u0628\u0631\u0627\u0632\u0634 (Underfitting) \u0648 \u0631\u0648\u0634\u200c\u0647\u0627\u06cc \u0645\u0642\u0627\u0628\u0644\u0647<\/li>\n<\/ul>\n<h3>\u0628\u062e\u0634 \u0633\u0648\u0645: \u0628\u06cc\u0646\u0627\u06cc\u06cc \u06a9\u0627\u0645\u067e\u06cc\u0648\u062a\u0631 (Computer Vision) \u0628\u0627 CNNs<\/h3>\n<ul>\n<li>\u0645\u0628\u0627\u0646\u06cc \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u06cc (\u0644\u0627\u06cc\u0647 \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u060c Pooling)<\/li>\n<li>\u067e\u06cc\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u06cc\u06a9 \u0645\u062f\u0644 CNN \u0628\u0631\u0627\u06cc \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u062a\u0635\u0627\u0648\u06cc\u0631 (\u067e\u0631\u0648\u0698\u0647 CIFAR-10)<\/li>\n<li>\u0645\u0639\u0645\u0627\u0631\u06cc\u200c\u0647\u0627\u06cc \u0645\u0639\u0631\u0648\u0641: LeNet, AlexNet, VGG, ResNet<\/li>\n<li>\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0627\u0646\u062a\u0642\u0627\u0644\u06cc (Transfer Learning) \u0648 Fine-tuning \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0627\u0632 \u067e\u06cc\u0634 \u0622\u0645\u0648\u0632\u0634\u200c\u062f\u06cc\u062f\u0647<\/li>\n<li>\u067e\u0631\u0648\u0698\u0647 \u0639\u0645\u0644\u06cc: \u062a\u0634\u062e\u06cc\u0635 \u0627\u0634\u06cc\u0627\u0621 \u062f\u0631 \u062a\u0635\u0627\u0648\u06cc\u0631 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 YOLO<\/li>\n<\/ul>\n<h3>\u0628\u062e\u0634 \u0686\u0647\u0627\u0631\u0645: \u067e\u0631\u062f\u0627\u0632\u0634 \u0632\u0628\u0627\u0646 \u0637\u0628\u06cc\u0639\u06cc (NLP) \u0648 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u062a\u0631\u062a\u06cc\u0628\u06cc<\/h3>\n<ul>\n<li>\u0645\u0628\u0627\u0646\u06cc \u06a9\u0627\u0631 \u0628\u0627 \u0645\u062a\u0646: \u062a\u0648\u06a9\u0646\u06cc\u0632\u0647 \u06a9\u0631\u062f\u0646 \u0648 \u0633\u0627\u062e\u062a \u0648\u0627\u0698\u06af\u0627\u0646<\/li>\n<li>\u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0628\u0627\u0632\u06af\u0634\u062a\u06cc (RNN) \u0648 \u0645\u0634\u06a9\u0644 \u0645\u062d\u0648 \u0634\u062f\u06af\u06cc \u06af\u0631\u0627\u062f\u06cc\u0627\u0646<\/li>\n<li>\u0645\u0639\u0645\u0627\u0631\u06cc\u200c\u0647\u0627\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 LSTM \u0648 GRU<\/li>\n<li>\u0645\u06a9\u0627\u0646\u06cc\u0633\u0645 \u062a\u0648\u062c\u0647 (Attention Mechanism)<\/li>\n<li>\u0645\u0639\u0645\u0627\u0631\u06cc \u0627\u0646\u0642\u0644\u0627\u0628\u06cc \u062a\u0631\u0646\u0633\u0641\u0648\u0631\u0645\u0631 (Transformer) \u0648 \u0645\u062f\u0644 BERT<\/li>\n<li>\u067e\u0631\u0648\u0698\u0647 \u0639\u0645\u0644\u06cc: \u0633\u0627\u062e\u062a \u0645\u062f\u0644 \u062a\u062d\u0644\u06cc\u0644 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0646\u0638\u0631\u0627\u062a \u06a9\u0627\u0631\u0628\u0631\u0627\u0646<\/li>\n<\/ul>\n<h3>\u0628\u062e\u0634 \u067e\u0646\u062c\u0645: \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0645\u0648\u0644\u062f \u0648 \u0645\u0628\u0627\u062d\u062b \u067e\u06cc\u0634\u0631\u0641\u062a\u0647<\/h3>\n<ul>\n<li>\u0645\u0628\u0627\u0646\u06cc \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0645\u0648\u0644\u062f \u062a\u062e\u0627\u0635\u0645\u06cc (GANs)<\/li>\n<li>\u067e\u0631\u0648\u0698\u0647 \u0639\u0645\u0644\u06cc: \u062a\u0648\u0644\u06cc\u062f \u062a\u0635\u0627\u0648\u06cc\u0631 \u0686\u0647\u0631\u0647\u200c\u0647\u0627\u06cc \u0648\u0627\u0642\u0639\u200c\u06af\u0631\u0627\u06cc\u0627\u0646\u0647 \u0628\u0627 DCGAN<\/li>\n<li>\u0627\u062a\u0648\u0627\u0646\u06a9\u062f\u0631\u0647\u0627 (Autoencoders) \u0648 \u06a9\u0627\u0631\u0628\u0631\u062f\u0647\u0627\u06cc \u0622\u0646\u200c\u0647\u0627<\/li>\n<li>\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062a\u0642\u0648\u06cc\u062a\u06cc (Reinforcement Learning) \u0686\u06cc\u0633\u062a\u061f<\/li>\n<li>\u0646\u06a9\u0627\u062a \u0648 \u062a\u0631\u0641\u0646\u062f\u0647\u0627\u06cc \u0639\u0645\u0644\u06cc \u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u0642\u0631\u0627\u0631 \u0645\u062f\u0644 \u0631\u0648\u06cc \u0633\u0631\u0648\u0631 (Deployment)<\/li>\n<\/ul>\n<p>\n <b>\u0648 \u062f\u0647\u200c\u0647\u0627 \u0633\u0631\u0641\u0635\u0644 \u062f\u06cc\u06af\u0631…<\/b> \u0627\u06cc\u0646 \u062a\u0646\u0647\u0627 \u0628\u062e\u0634 \u06a9\u0648\u0686\u06a9\u06cc \u0627\u0632 \u0633\u0641\u0631 \u0622\u0645\u0648\u0632\u0634\u06cc \u0647\u06cc\u062c\u0627\u0646\u200c\u0627\u0646\u06af\u06cc\u0632\u06cc \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 \u067e\u06cc\u0634 \u0631\u0648 \u062f\u0627\u0631\u06cc\u062f. \u0627\u06af\u0631 \u0622\u0645\u0627\u062f\u0647\u200c\u0627\u06cc\u062f \u062a\u0627 \u0645\u0647\u0627\u0631\u062a\u200c\u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u0628\u0647 \u0633\u0637\u062d \u0628\u0627\u0644\u0627\u062a\u0631\u06cc \u0628\u0631\u0633\u0627\u0646\u06cc\u062f \u0648 \u0628\u0647 \u062c\u0645\u0639 \u0645\u062a\u062e\u0635\u0635\u0627\u0646 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u0628\u067e\u06cc\u0648\u0646\u062f\u06cc\u062f\u060c \u0647\u0645\u06cc\u0646 \u0627\u0645\u0631\u0648\u0632 \u062f\u0631 \u0627\u06cc\u0646 \u062f\u0648\u0631\u0647 \u062b\u0628\u062a\u200c\u0646\u0627\u0645 \u06a9\u0646\u06cc\u062f \u0648 \u0627\u0648\u0644\u06cc\u0646 \u0642\u062f\u0645 \u0631\u0627 \u0628\u0631\u0627\u06cc \u0633\u0627\u062e\u062a\u0646 \u0622\u06cc\u0646\u062f\u0647\u200c\u0627\u06cc \u062f\u0631\u062e\u0634\u0627\u0646 \u0628\u0631\u062f\u0627\u0631\u06cc\u062f.\n <\/p>\n<p><\/body><br \/>\n<\/html><\/div>\n<div\r\n style=\"border: 2px dashed #4CAF50; border-radius: 16px; padding: 20px; background: #f9fff9; font-family: 'IRANSans', sans-serif;\">\r\n <h2 style=\"color: #2E7D32; margin-top: 0;\">\ud83d\udcda \u0645\u062d\u062a\u0648\u0627\u06cc \u0627\u06cc\u0646 \u0645\u062d\u0635\u0648\u0644 \u0622\u0645\u0648\u0632\u0634\u06cc (\u067e\u06a9\u06cc\u062c \u06a9\u0627\u0645\u0644)<\/h2>\r\n <div\r\n style=\"background: #E8F5E9; border-radius: 12px; padding: 15px 20px; margin-bottom: 20px; border: 1px solid #A5D6A7;\">\r\n <h3 style=\"color: #1B5E20; margin-top: 0;\">\ud83d\udca1 \u0627\u06cc\u0646 \u0645\u062d\u0635\u0648\u0644 \u06cc\u06a9 \u0646\u0633\u062e\u0647\u0654 \u06a9\u0627\u0645\u0644 \u0648 \u062c\u0627\u0645\u0639 \u0627\u0633\u062a<\/h3>\r\n <p style=\"font-size:16px; line-height:1.8; color:#2E7D32; margin:0;\"> \u062a\u0645\u0627\u0645\u06cc \u0645\u062d\u062a\u0648\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0627\u06cc\u0646 \u06a9\u062a\u0627\u0628 \u062f\u0631 \u0642\u0627\u0644\u0628 \u06cc\u06a9\r\n \u0628\u0633\u062a\u0647\u200c\u06cc \u06a9\u0627\u0645\u0644 \u0648 \u06cc\u06a9\u067e\u0627\u0631\u0686\u0647 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f \u0648 \u0634\u0627\u0645\u0644 \u062a\u0645\u0627\u0645 \u0646\u0633\u062e\u0647\u200c\u0647\u0627 \u0648 \u0641\u0627\u06cc\u0644\u200c\u0647\u0627\u06cc \u0645\u0648\u0631\u062f\u0646\u06cc\u0627\u0632 \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0627\u0633\u062a. <\/p>\r\n <\/div>\r\n <h3 style=\"color: #2E7D32;\">\ud83c\udf81 \u0645\u062d\u062a\u0648\u06cc\u0627\u062a \u06a9\u0627\u0645\u0644 \u0628\u0633\u062a\u0647 \u062f\u0627\u0646\u0644\u0648\u062f\u06cc<\/h3>\r\n\r\n\t\r\n<ul style=\"list-style-type: '\u2705 '; padding-left: 20px; font-size: 16px; line-height: 1.8;\">\r\n <li><strong>\u0648\u06cc\u062f\u06cc\u0648\u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0641\u0627\u0631\u0633\u06cc<\/strong> \u2014 \u0622\u0645\u0648\u0632\u0634 \u0642\u062f\u0645\u200c\u0628\u0647\u200c\u0642\u062f\u0645\u060c \u06a9\u0627\u0631\u0628\u0631\u062f\u06cc \u0648 \u0642\u0627\u0628\u0644 \u0641\u0647\u0645<\/li>\r\n <li><strong>\u067e\u0627\u062f\u06a9\u0633\u062a\u200c\u0647\u0627\u06cc \u0635\u0648\u062a\u06cc \u0641\u0627\u0631\u0633\u06cc<\/strong> \u2014 \u062a\u0648\u0636\u06cc\u062d \u0645\u0641\u0627\u0647\u06cc\u0645 \u06a9\u0644\u06cc\u062f\u06cc \u0648 \u0646\u06a9\u0627\u062a \u062a\u06a9\u0645\u06cc\u0644\u06cc<\/li>\r\n <li><strong>\u06a9\u062a\u0627\u0628 PDF \u0641\u0627\u0631\u0633\u06cc<\/strong> \u2014 \u0634\u0627\u0645\u0644 \u06a9\u0644\u06cc\u0647\u0654 \u0633\u0631\u0641\u0635\u0644\u200c\u0647\u0627 \u0648 \u0645\u062d\u062a\u0648\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc<\/li>\r\n <li><strong>\u06a9\u062a\u0627\u0628 \u062e\u0644\u0627\u0635\u0647 \u0646\u06a9\u0627\u062a \u0648\u06cc\u062f\u06cc\u0648\u0647\u0627 \u0648 \u067e\u0627\u062f\u06a9\u0633\u062a\u200c\u0647\u0627 \u2013 \u0646\u0633\u062e\u0647 PDF<\/strong> \u2014 \u0645\u0646\u0627\u0633\u0628 \u0645\u0631\u0648\u0631 \u0633\u0631\u06cc\u0639 \u0648 \u062c\u0645\u0639\u200c\u0628\u0646\u062f\u06cc \u0645\u0628\u0627\u062d\u062b<\/li>\r\n <li><strong>\u06a9\u062a\u0627\u0628 \u0635\u062f\u0647\u0627 \u0646\u06a9\u062a\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062e\u0648\u062f\u0645\u0648\u0646\u06cc) \u2013 \u0646\u0633\u062e\u0647 PDF<\/strong> \u2014 \u0632\u0628\u0627\u0646 \u0633\u0627\u062f\u0647 \u0648 \u06a9\u0627\u0631\u0628\u0631\u062f\u06cc<\/li>\r\n <li><strong>\u06a9\u062a\u0627\u0628 \u0635\u062f\u0647\u0627 \u0646\u06a9\u062a\u0647 \u0631\u0633\u0645\u06cc \u0641\u0627\u0631\u0633\u06cc \u2013 \u0646\u0633\u062e\u0647 PDF<\/strong> \u2014 \u0646\u06af\u0627\u0631\u0634 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f\u060c \u0639\u0644\u0645\u06cc \u0648 \u0645\u0646\u0627\u0633\u0628 \u0686\u0627\u067e<\/li>\r\n\r\n <li>\r\n <strong>\u06a9\u062a\u0627\u0628 \u0635\u062f\u0647\u0627 \u067e\u0631\u0633\u0634 \u0648 \u067e\u0627\u0633\u062e \u062a\u0634\u0631\u06cc\u062d\u06cc \u2013 \u0646\u0633\u062e\u0647 PDF<\/strong><br>\r\n \u2014 \u0647\u0631 \u0633\u0624\u0627\u0644 \u0628\u0644\u0627\u0641\u0627\u0635\u0644\u0647 \u0647\u0645\u0631\u0627\u0647 \u0628\u0627 \u067e\u0627\u0633\u062e \u06a9\u0627\u0645\u0644 \u0648 \u0634\u0641\u0627\u0641 \u0627\u0631\u0627\u0626\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a\u061b \u0645\u0646\u0627\u0633\u0628 \u062f\u0631\u06a9 \u0639\u0645\u06cc\u0642 \u0645\u0641\u0627\u0647\u06cc\u0645 \u0648 \u0631\u0641\u0639 \u0627\u0628\u0647\u0627\u0645.\r\n <\/li>\r\n\r\n <li>\r\n <strong>\u06a9\u062a\u0627\u0628 \u0635\u062f\u0647\u0627 \u067e\u0631\u0633\u0634 \u0648 \u067e\u0627\u0633\u062e \u0686\u0647\u0627\u0631\u06af\u0632\u06cc\u0646\u0647\u200c\u0627\u06cc \u2013 \u0646\u0633\u062e\u0647 PDF (\u0646\u0633\u062e\u0647 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0633\u0631\u06cc\u0639)<\/strong><br>\r\n \u2014 \u067e\u0627\u0633\u062e\u200c\u0647\u0627 \u0628\u0644\u0627\u0641\u0627\u0635\u0644\u0647 \u067e\u0633 \u0627\u0632 \u0633\u0624\u0627\u0644 \u0642\u0631\u0627\u0631 \u062f\u0627\u0631\u0646\u062f\u061b \u0645\u0646\u0627\u0633\u0628 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0633\u0631\u06cc\u0639 \u0648 \u062a\u062b\u0628\u06cc\u062a \u0645\u0637\u0627\u0644\u0628.\r\n <\/li>\r\n\r\n <li>\r\n <strong>\u06a9\u062a\u0627\u0628 \u0635\u062f\u0647\u0627 \u067e\u0631\u0633\u0634 \u0648 \u067e\u0627\u0633\u062e \u0686\u0647\u0627\u0631\u06af\u0632\u06cc\u0646\u0647\u200c\u0627\u06cc \u2013 \u0646\u0633\u062e\u0647 PDF (\u0646\u0633\u062e\u0647 \u062e\u0648\u062f\u0622\u0632\u0645\u0627\u06cc\u06cc \u067e\u0627\u06cc\u0627\u0646\u200c\u0628\u062e\u0634)<\/strong><br>\r\n \u2014 \u067e\u0627\u0633\u062e\u200c\u0647\u0627 \u062f\u0631 \u0627\u0646\u062a\u0647\u0627\u06cc \u0647\u0631 \u0628\u062e\u0634 \u0622\u0645\u062f\u0647\u200c\u0627\u0646\u062f\u061b \u0645\u0646\u0627\u0633\u0628 \u0622\u0632\u0645\u0648\u0646 \u0648\u0627\u0642\u0639\u06cc \u0648 \u0633\u0646\u062c\u0634 \u0645\u06cc\u0632\u0627\u0646 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc.\r\n <\/li>\r\n\r\n <li>\r\n <strong>\u06a9\u062a\u0627\u0628 \u062a\u0645\u0631\u06cc\u0646\u200c\u0647\u0627\u06cc \u062f\u0631\u0633\u062a \/ \u0646\u0627\u062f\u0631\u0633\u062a (True \/ False) \u2013 \u0646\u0633\u062e\u0647 PDF<\/strong><br>\r\n \u2014 \u0645\u0646\u0627\u0633\u0628 \u0627\u0641\u0632\u0627\u06cc\u0634 \u062f\u0642\u062a \u0645\u0641\u0647\u0648\u0645\u06cc \u0648 \u062a\u0634\u062e\u06cc\u0635 \u0635\u062d\u06cc\u062d \u06cc\u0627 \u0646\u0627\u062f\u0631\u0633\u062a \u0628\u0648\u062f\u0646 \u06af\u0632\u0627\u0631\u0647\u200c\u0647\u0627.\r\n <\/li>\r\n\r\n <li>\r\n <strong>\u06a9\u062a\u0627\u0628 \u062a\u0645\u0631\u06cc\u0646\u200c\u0647\u0627\u06cc \u062c\u0627\u06cc \u062e\u0627\u0644\u06cc \u2013 \u0646\u0633\u062e\u0647 PDF<\/strong><br>\r\n \u2014 \u062a\u0642\u0648\u06cc\u062a \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0641\u0639\u0627\u0644 \u0648 \u062a\u0633\u0644\u0637 \u0628\u0631 \u0645\u0641\u0627\u0647\u06cc\u0645 \u0648 \u0627\u0635\u0637\u0644\u0627\u062d\u0627\u062a \u06a9\u0644\u06cc\u062f\u06cc.\r\n <\/li>\r\n<\/ul>\r\n\t\r\n\t\r\n\t\r\n\t\r\n <p style=\"color: #388E3C; font-weight: bold; font-size: 18px; margin-top: 20px;\"> \ud83c\udfaf \u0627\u06cc\u0646 \u0628\u0633\u062a\u0647 \u06cc\u06a9 \u062f\u0648\u0631\u0647\u0654 \u0622\u0645\u0648\u0632\u0634\u06cc \u06a9\u0627\u0645\u0644 \u0648\r\n \u0686\u0646\u062f\u0644\u0627\u06cc\u0647 \u0627\u0633\u062a\u061b \u0634\u0627\u0645\u0644 \u0622\u0645\u0648\u0632\u0634 \u062a\u0635\u0648\u06cc\u0631\u06cc\u060c \u0635\u0648\u062a\u06cc\u060c \u06a9\u062a\u0627\u0628\u200c\u0647\u0627\u060c \u062a\u0645\u0631\u06cc\u0646\u200c\u0647\u0627 \u0648 \u062e\u0648\u062f\u0622\u0632\u0645\u0627\u06cc\u06cc . <\/p>\r\n <hr style=\"border: none; border-top: 1px dashed #81C784; margin: 20px 0;\">\r\n <h3 style=\"color: #2E7D32;\">\u2139\ufe0f \u0646\u06a9\u0627\u062a \u0645\u0647\u0645 \u0647\u0646\u06af\u0627\u0645 \u062e\u0631\u06cc\u062f<\/h3>\r\n <ul style=\"list-style-type: '\ud83d\udd38 '; padding-left: 20px; font-size: 15px; line-height: 1.9;\">\r\n <li>\u0627\u06cc\u0646 \u0645\u062d\u0635\u0648\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a <strong>\u0641\u0627\u06cc\u0644 \u062f\u0627\u0646\u0644\u0648\u062f\u06cc \u06a9\u0627\u0645\u0644<\/strong> \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f \u0648 \u0646\u0633\u062e\u0647\u0654 \u0686\u0627\u067e\u06cc \u0646\u062f\u0627\u0631\u062f.<\/li>\r\n <li>\u062a\u0645\u0627\u0645\u06cc \u0641\u0627\u06cc\u0644\u200c\u0647\u0627 \u0648 \u06a9\u062a\u0627\u0628\u200c\u0647\u0627 <strong>\u06a9\u0627\u0645\u0644\u0627\u064b \u0641\u0627\u0631\u0633\u06cc<\/strong> \u0647\u0633\u062a\u0646\u062f.<\/li>\r\n <li><strong>\u062a\u0648\u062c\u0647:<\/strong> \u0644\u06cc\u0646\u06a9\u200c\u0647\u0627\u06cc \u0627\u062e\u062a\u0635\u0627\u0635\u06cc \u062f\u0648\u0631\u0647 \u0637\u06cc <strong>\u06f4\u06f8 \u0633\u0627\u0639\u062a<\/strong> \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634 \u0627\u0631\u0633\u0627\u0644 \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/li>\r\n <li>\u0646\u06cc\u0627\u0632\u06cc \u0628\u0647 \u062f\u0631\u062c \u0634\u0645\u0627\u0631\u0647 \u0645\u0648\u0628\u0627\u06cc\u0644 \u0646\u06cc\u0633\u062a\u061b \u0627\u0645\u0627 \u0628\u0631\u0627\u06cc \u067e\u0634\u062a\u06cc\u0628\u0627\u0646\u06cc \u0633\u0631\u06cc\u0639\u200c\u062a\u0631 \u062a\u0648\u0635\u06cc\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/li>\r\n <li>\u062f\u0631 \u0635\u0648\u0631\u062a \u0628\u0631\u0648\u0632 \u0645\u0634\u06a9\u0644 \u062f\u0631 \u062f\u0627\u0646\u0644\u0648\u062f \u0628\u0627 \u0634\u0645\u0627\u0631\u0647 <strong>09395106248<\/strong> \u062a\u0645\u0627\u0633 \u0628\u06af\u06cc\u0631\u06cc\u062f.<\/li>\r\n <li>\u0627\u06af\u0631 \u067e\u0631\u062f\u0627\u062e\u062a \u0627\u0646\u062c\u0627\u0645 \u0634\u062f\u0647 \u0648\u0644\u06cc \u0644\u06cc\u0646\u06a9\u200c\u0647\u0627 \u0631\u0627 \u062f\u0631\u06cc\u0627\u0641\u062a \u0646\u06a9\u0631\u062f\u0647\u200c\u0627\u06cc\u062f\u060c \u0646\u0627\u0645 \u0648 \u0646\u0627\u0645 \u062e\u0627\u0646\u0648\u0627\u062f\u06af\u06cc \u0648 \u0646\u0627\u0645 \u0645\u062d\u0635\u0648\u0644 \u0631\u0627 \u067e\u06cc\u0627\u0645\u06a9 \u06a9\u0646\u06cc\u062f \u062a\u0627\r\n \u0644\u06cc\u0646\u06a9\u200c\u0647\u0627 \u062f\u0648\u0628\u0627\u0631\u0647 \u0627\u0631\u0633\u0627\u0644 \u0634\u0648\u0646\u062f.<\/li>\r\n <\/ul>\r\n <p style=\"font-size: 16px; line-height: 1.8; margin-top: 15px;\"> \ud83d\udcac \u0631\u0627\u0647\u200c\u0647\u0627\u06cc \u0627\u0631\u062a\u0628\u0627\u0637\u06cc \u067e\u0634\u062a\u06cc\u0628\u0627\u0646\u06cc:<br> \u0648\u0627\u062a\u0633\u200c\u0627\u067e \u06cc\u0627 \u067e\u06cc\u0627\u0645\u06a9:\r\n <strong>09395106248<\/strong><br> \u062a\u0644\u06af\u0631\u0627\u0645: <strong>@ma_limbs<\/strong> <\/p>\r\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u062f\u0648\u0631\u0647 \u062c\u0627\u0645\u0639 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0627 PyTorch: \u0622\u06cc\u0646\u062f\u0647 \u0631\u0627 \u06a9\u062f \u0628\u0632\u0646\u06cc\u062f \u0622\u06cc\u0646\u062f\u0647 \u0631\u0627 \u06a9\u062f \u0628\u0632\u0646\u06cc\u062f: \u062f\u0648\u0631\u0647 \u062c\u0627\u0645\u0639 \u0648 \u067e\u0631\u0648\u0698\u0647\u200c\u0645\u062d\u0648\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0627 PyTorch \u0628\u0647 \u0627\u0646\u0642\u0644\u0627\u0628 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u062e\u0648\u0634 \u0622\u0645\u062f\u06cc\u062f! \u062f\u0646\u06cc\u0627\u06cc \u062a\u06a9\u0646\u0648\u0644\u0648\u0698\u06cc \u0628\u0627 \u0633\u0631\u0639\u062a\u06cc \u0628\u0627\u0648\u0631\u0646\u06a9\u0631\u062f\u0646\u06cc \u062f\u0631 \u062d\u0627\u0644 \u062f\u06af\u0631\u06af\u0648\u0646\u06cc…<\/p>\n","protected":false},"featured_media":67493,"comment_status":"open","ping_status":"closed","template":"","meta":{"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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