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--- neural_network [2024/03/01 04:50] – [You Only Look Once YOLO] jhagstrand
+++ neural_network [2024/08/27 10:43] (current) – [Orientation] jhagstrand
@@ Line 1: / Line 1: @@
 ====== Neural Network ======
+[[Gradient Descent]] \\
+[[Linear Algebra]]
 === Neural Network NN ===
@@ Line 122: / Line 125: @@
 YOLOv1 2015 Joseph Ched Redmon et al \\
 YOLOv2 2016 Redmon and Farhadi, aka YOLO9000 \\
-YOLOv3 2018 Redmon last version for Redmon, he bails out due to concerns about military applications
+YOLOv3 2018 Redmon last version for Redmon, he bails out due to concerns about military applications \\
-YOLOv4 2020 Alexey Bochkovskiy et al: YOLOv4: Optimal Speed and Accuracy of Object Detection
+YOLOv4 2020 Alexey Bochkovskiy et al: YOLOv4: Optimal Speed and Accuracy of Object Detection \\
-YOLOv5 Ultralytics, switch from DarkNet to PyTorch
+YOLOv5 Ultralytics, switch from DarkNet to PyTorch \\
-YOLOv6 Alexey Bochkovskiy et al
+YOLOv6 Alexey Bochkovskiy et al \\
-YOLOv7 Alexey Bochkovskiy et al
+YOLOv7 Alexey Bochkovskiy et al \\
-YOLOv8 Ultralytics
+YOLOv8 Ultralytics \\
-YOLOv9 ?
+YOLOv9 ? \\
 https://deci.ai/blog/history-yolo-object-detection-models-from-yolov1-yolov8/
@@ Line 504: / Line 507: @@
 aerial photo object orientation
+===== LLM =====
+==== NLP ====
+==== AI Code Writing ====
+As of August 2024
+AI models and tools for code writing
+from Grok
+OpenAI: ChatGPT
+VS Code, GitHub, Copilot: All from the Microsoft world.
+nlp
+llm
+transformer
+capabilities:
+  * code suggestions
+  * autocompletion
+  * generate a function from a prompt
+stand-alone operation vs integrated with IDE
+LLM coding assistant
+  * the model
+  * the corpora
+    * source code
+    * multiple languages
+    * organized into categories and contexts
+  * the trained model
+  * the interface:
+    * chatbot
+    * virtual assistant
+    * code completion plugin for an IDE
+^ Company  ^ Product        ^  Open Source  ^ Languages  ^ Comment  ^
+| OpenAI   | ChatGPT        |      No       |            |          |
+| OpenAI   | Codex          |      No       |            |          |
+| GitHub   | Copilot        |      No       |            | Based on Codex, integrated into IDE's like VSCode.         |
+| xAI      | Grok           |      ?        |            |          |
+| Amazon   | CodeWhisperer  |      No       |            | For use with AWS  |
+| Tabnine  | Tabnine        |      No       |            |                   |
+?:Tabnine
+AI-based code completion with support for over 30 programming languages.
+It's known for its ability to run locally or in the cloud, providing flexibility in deployment.
+Tabnine also emphasizes privacy by allowing developers to host their own models.
+Meta:Code Llama
+free for both research and commercial use.
+Code Llama has been highlighted for its performance in coding tasks,
+even outperforming some versions of models like GPT-3.5 in certain benchmarks.
+?:DeepSeek-v2-Coder
+Mentioned for its impressive performance in coding tasks,
+this model has been recognized for producing 100% compilable Java code in some evaluations,
+indicating high-quality code generation capabilities.
+Anthropic: Claude
+While primarily known for its conversational abilities,
+Claude's latest iterations, like Claude 3.5, have been praised for coding proficiency,
+especially in understanding and generating code for less common libraries or languages.
+- **Local and Open-Source Models**:
+There's a growing trend towards using open-source models like those based on LLaMA, Pythia,
+or even customized versions of these models for coding tasks.
+Tools like `ollama.nvim` for Neovim or platforms allowing you to run these models
+locally or on personal servers are becoming popular for those who prefer not to rely on
+cloud-based solutions.
+xAI: Grok
+Although primarily known for its conversational abilities and integration with X (formerly Twitter),
+Grok's capabilities in understanding and potentially generating code could be inferred from its
+general language processing skills, though specific coding features might be less documented.
+Each of these models or tools brings unique strengths to the table,
+from integration capabilities with existing workflows,
+support for a wide array of programming languages,
+to performance in generating high-quality, compilable code.
+The choice between them might depend on factors like integration with
+your current development environment, privacy concerns, cost, or specific coding task requirements.
+Remember, while these tools can significantly enhance productivity,
+they should be used as aids, with human oversight for critical or complex coding tasks.