{"id":51018556,"url":"https://github.com/alicankaya192/deep_learning_path","last_synced_at":"2026-06-21T14:01:06.734Z","repository":{"id":365670277,"uuid":"1273188055","full_name":"AlicanKaya192/Deep_Learning_Path","owner":"AlicanKaya192","description":"Sıfırdan ileri seviyeye Deep Learning yol haritası. 11 modül: PDF + Python + Jupyter Notebook. NumPy · TensorFlow · PyTorch karşılaştırmalı implementasyonlar.","archived":false,"fork":false,"pushed_at":"2026-06-18T09:54:30.000Z","size":320,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-06-18T11:29:55.114Z","etag":null,"topics":["artificial-intelligence","cnn","data-science","deep-learning","gan","jupyter-notebook","lstm","machine-learning","neural-networks","numpy","python","pytorch","rnn","tensorflow","transformer"],"latest_commit_sha":null,"homepage":"https://alican-kaya.com","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AlicanKaya192.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2026-06-18T09:34:20.000Z","updated_at":"2026-06-18T09:57:27.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/AlicanKaya192/Deep_Learning_Path","commit_stats":null,"previous_names":["alicankaya192/deep_learning_path"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/AlicanKaya192/Deep_Learning_Path","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlicanKaya192%2FDeep_Learning_Path","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlicanKaya192%2FDeep_Learning_Path/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlicanKaya192%2FDeep_Learning_Path/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlicanKaya192%2FDeep_Learning_Path/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AlicanKaya192","download_url":"https://codeload.github.com/AlicanKaya192/Deep_Learning_Path/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlicanKaya192%2FDeep_Learning_Path/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34610832,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-21T02:00:05.568Z","response_time":54,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","cnn","data-science","deep-learning","gan","jupyter-notebook","lstm","machine-learning","neural-networks","numpy","python","pytorch","rnn","tensorflow","transformer"],"created_at":"2026-06-21T14:01:05.437Z","updated_at":"2026-06-21T14:01:06.725Z","avatar_url":"https://github.com/AlicanKaya192.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🧠 Deep Learning Path\n\n\u003e Sıfırdan ileri seviyeye kapsamlı bir derin öğrenme yol haritası.  \n\u003e Her modül: **PDF konu anlatımı** + **Python implementasyonu** + **Jupyter Notebook**\n\n---\n\n## 📌 Hakkında\n\nBu repo, derin öğrenmeyi temelden öğrenmek isteyenler için hazırlanmış yapılandırılmış bir öğrenme yolu içerir. Her konu kendi klasöründe; teorik PDF, çalışabilir Python kodu ve kapsamlı Jupyter Notebook ile birlikte sunulmuştur.\n\nTüm implementasyonlar **üç framework** ile karşılaştırmalı olarak yapılmıştır:\n- 🔢 **NumPy** — sıfırdan, matematiği anlamak için\n- 🟠 **TensorFlow / Keras** — hızlı prototip ve production\n- 🔴 **PyTorch** — araştırma ve özel mimari\n\n---\n\n## 📂 Repo Yapısı\n\n```\nDeep-Learning-Path/\n│\n├── 01-Sinir_Aglari_Temelleri/\n│   ├── 01_Sinir_Aglari_Temelleri.pdf\n│   ├── 01_sinir_aglari_numpy_from_scratch.py\n│   └── 01_sinir_aglari_kapsamli.ipynb\n│\n├── 02-Aktivasyon_Fonksiyonlari/\n│   ├── 02_Aktivasyon_Fonksiyonlari.pdf\n│   ├── 02_aktivasyon_fonksiyonlari.py\n│   └── 02_aktivasyon_fonksiyonlari_kapsamli.ipynb\n│\n├── 03-Kayip_Fonksiyonlari_ve_Optimizasyon/\n│   ├── 03_Kayip_Optimizasyon.pdf\n│   ├── 03_kayip_optimizasyon.py\n│   └── 03_kayip_optimizasyon_kapsamli.ipynb\n│\n├── 04-Geri_Yayilim_Backpropagation/\n│   ├── 04_Backpropagation.pdf\n│   ├── 04_backpropagation_numpy.py\n│   └── 04_backpropagation_kapsamli.ipynb\n│\n├── 05-Derin_Aglar_Regularization/\n│   ├── 05_Regularization.pdf\n│   ├── 05_regularization.py\n│   └── 05_regularization_kapsamli.ipynb\n│\n├── 06-CNN_Evrisimsel_Sinir_Aglari/\n│   ├── 06_CNN.pdf\n│   ├── 06_cnn_tensorflow_pytorch.py\n│   └── 06_cnn_kapsamli.ipynb\n│\n├── 07-Transfer_Learning/\n│   ├── 07_Transfer_Learning.pdf\n│   ├── 07_transfer_learning.py\n│   └── 07_transfer_learning_kapsamli.ipynb\n│\n├── 08-RNN_LSTM_GRU/\n│   ├── 08_RNN_LSTM_GRU.pdf\n│   ├── 08_rnn_lstm_gru.py\n│   └── 08_rnn_lstm_gru_kapsamli.ipynb\n│\n├── 09-Transformer_ve_Attention/\n│   ├── 09_Transformer_Attention.pdf\n│   ├── 09_transformer_attention.py\n│   └── 09_transformer_kapsamli.ipynb\n│\n├── 10-Generative_Models_GAN_VAE/\n│   ├── 10_GAN_VAE.pdf\n│   ├── 10_gan_vae.py\n│   └── 10_gan_vae_kapsamli.ipynb\n│\n└── 11-FINAL_PROJE_Multimodal_Sentiment/\n    ├── multimodal_sentiment_analysis.py\n    ├── multimodal_sentiment_notebook.ipynb\n    └── FINAL_PROJE_RAPORU.pdf\n```\n\n---\n\n## 🗺️ Modül Listesi\n\n| # | Modül | Konular |\n|---|-------|---------|\n| 01 | Sinir Ağları Temelleri | Perceptron, MLP, Forward Pass, Backprop, XOR |\n| 02 | Aktivasyon Fonksiyonları | Sigmoid, ReLU, GELU, Vanishing Gradient, Dead Neuron |\n| 03 | Kayıp Fonk. \u0026 Optimizasyon | MSE, BCE, Adam, SGD, LR Scheduler |\n| 04 | Backpropagation | Zincir Kuralı, Hesaplama Grafı, Sıfırdan Impl. |\n| 05 | Regularization | Dropout, Batch Norm, L1/L2, Early Stopping |\n| 06 | CNN | Konvolüsyon, Pooling, ResNet, Grad-CAM |\n| 07 | Transfer Learning | Feature Extraction, Fine-tuning, VGG/ResNet |\n| 08 | RNN / LSTM / GRU | Zaman Serisi, BPTT, Seq2Seq |\n| 09 | Transformer \u0026 Attention | Self-Attention, BERT, GPT, Positional Encoding |\n| 10 | GAN \u0026 VAE | Üretici Modeller, Latent Space, DCGAN |\n| 11 | Final Proje | Multimodal Sentiment Analysis (CNN + BERT fusion) |\n\n---\n\n## 🚀 Kurulum\n\n```bash\ngit clone https://github.com/AlicanKaya192/Deep-Learning-Path.git\ncd Deep-Learning-Path\n\npip install numpy matplotlib scikit-learn tensorflow torch torchvision jupyter\n```\n\n---\n\n## 📋 Her Modülde Ne Var?\n\n### 📄 PDF\n- Konu anlatımı (teorik temel, formüller)\n- Görsel şemalar ve mimari diyagramlar\n- Adım adım sayısal örnekler\n- Framework karşılaştırma tabloları\n- Yaygın hatalar ve çözümleri\n- Özet tablo + kaynaklar\n\n### 🐍 Python (.py)\n- NumPy ile sıfırdan implementasyon\n- TensorFlow/Keras implementasyonu\n- PyTorch implementasyonu\n- Matplotlib görselleştirme\n- PEP8 uyumlu, tam yorumlanmış\n\n### 📓 Jupyter Notebook (.ipynb)\n- LaTeX matematiksel formüller\n- İnteraktif görselleştirmeler\n- Hiperparametre deneyleri\n- Gerçek veri seti uygulaması\n- Alıştırmalar (ipuçlu, çözümsüz)\n\n---\n\n## 🎯 Kimler İçin?\n\n- Derin öğrenmeye sıfırdan başlayanlar\n- Matematiği anlayarak ilerlemek isteyenler\n- Framework'leri karşılaştırmalı öğrenmek isteyenler\n- Bilgisayar Mühendisliği / Veri Bilimi öğrencileri\n\n---\n\n## 📊 İlerleme\n\n![%20](https://img.shields.io/badge/İlerleme-11%2F11%20Modül-success)\n\n- [x] Modül 01 — Sinir Ağları Temelleri\n- [x] Modül 02 — Aktivasyon Fonksiyonları\n- [x] Modül 03 — Kayıp Fonksiyonları \u0026 Optimizasyon\n- [x] Modül 04 — Backpropagation\n- [x] Modül 05 — Regularization\n- [x] Modül 06 — CNN\n- [x] Modül 07 — Transfer Learning\n- [x] Modül 08 — RNN / LSTM / GRU\n- [x] Modül 09 — Transformer \u0026 Attention\n- [x] Modül 10 — GAN \u0026 VAE\n- [x] Modül 11 — Final Proje\n\n---\n\n## 🔗 İlgili Repo\n\n[📦 Data-Science-RoadMap](https://github.com/AlicanKaya192/Data-Science-RoadMap) — Tam veri bilimi yol haritası\n\n---\n\n## 📜 Lisans\n\nMIT License — özgürce kullanabilir, katkıda bulunabilirsiniz.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falicankaya192%2Fdeep_learning_path","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falicankaya192%2Fdeep_learning_path","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falicankaya192%2Fdeep_learning_path/lists"}