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--- neural_network [2024/02/17 03:21] – [Convolutional Neural Network CNN] 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 57: / Line 60: @@
 K is the kernel or filter
+result of the convolution layer is the convolved feature map
+result of the pooling layer is the pooled feature map
 Unlike an artificial neuron in a fully-connected layer, a neuron in a convolutional layer is not connected to the entire input but just some section of the input data. These input neurons provide abstractions of small sections of the input data that, when combined over the entire input, we refer to as a feature map.
@@ Line 114: / Line 122: @@
 ==== You Only Look Once YOLO ====
+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 \\
+YOLOv4 2020 Alexey Bochkovskiy et al: YOLOv4: Optimal Speed and Accuracy of Object Detection \\
+YOLOv5 Ultralytics, switch from DarkNet to PyTorch \\
+YOLOv6 Alexey Bochkovskiy et al \\
+YOLOv7 Alexey Bochkovskiy et al \\
+YOLOv8 Ultralytics \\
+YOLOv9 ? \\
+https://deci.ai/blog/history-yolo-object-detection-models-from-yolov1-yolov8/
 Invented by Joseph Chet Redmon
@@ Line 485: / 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.