Deep Learning Fundamentals content move to Deep_Learning_Fundamentals.md

Author: xlisp

Deep_Learning_Fundamentals.md

@@ -0,0 +1,53 @@
+## Deep Learning Fundamentals
+
+Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers (deep neural networks) to progressively extract higher-level features from raw input. Here are the key components and concepts:
+
+### Neural Network Architecture
+* Input Layer: Receives raw data and normalizes it for processing
+* Hidden Layers: Multiple layers that transform data through weighted connections
+* Output Layer: Produces the final prediction or output
+* Activation Functions: Non-linear functions (ReLU, sigmoid, tanh) that help networks learn complex patterns
+
+### Key Deep Learning Concepts
+1. Backpropagation
+   * Algorithm for calculating gradients in neural networks
+   * Efficiently updates weights by propagating error backwards through the network
+   * Uses chain rule to compute partial derivatives
+
+2. Gradient Descent Optimization
+   * Stochastic Gradient Descent (SGD)
+   * Mini-batch Gradient Descent
+   * Adaptive optimizers (Adam, RMSprop)
+
+3. Loss Functions
+   * Mean Squared Error (MSE) for regression
+   * Cross-Entropy Loss for classification
+   * Custom loss functions for specific tasks
+
+4. Regularization Techniques
+   * Dropout: Randomly deactivates neurons during training
+   * L1/L2 Regularization: Adds penalty terms to prevent overfitting
+   * Batch Normalization: Normalizes layer inputs for stable training
+
+### Deep Learning Architectures
+
+1. Convolutional Neural Networks (CNNs)
+   * Specialized for processing grid-like data (images)
+   * Key components: Convolutional layers, pooling layers, fully connected layers
+   * Applications: Image classification, object detection, segmentation
+
+2. Recurrent Neural Networks (RNNs)
+   * Process sequential data with memory of previous inputs
+   * Variants: LSTM, GRU for handling long-term dependencies
+   * Applications: Time series prediction, natural language processing
+
+3. Transformers
+   * State-of-the-art architecture for sequence processing
+   * Self-attention mechanism for capturing relationships
+   * Applications: Language models, machine translation, text generation
+
+4. Autoencoders
+   * Unsupervised learning for dimensionality reduction
+   * Encoder-decoder architecture
+   * Applications: Feature learning, denoising, anomaly detection
+

README.md

@@ -7,59 +7,6 @@
 * The idea of high-dimensional space: the code is cut into high-dimensional space, and then a very detailed high-dimensional classification is done to separate it. Then the search is also high-dimensional, just like the code, it is entered into the treesitter to do training to obtain logical learning relationships. Most of NLP is a multi-classification problem in high-dimensional space.
 * Collect the input x and output y around you as training data, and mine their mapping relationship f(x) at any time. You can use GPT to generate certain data for your model training needs or write crawler to get you need data.
 
-## Deep Learning Fundamentals
-
-Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers (deep neural networks) to progressively extract higher-level features from raw input. Here are the key components and concepts:
-
-### Neural Network Architecture
-* Input Layer: Receives raw data and normalizes it for processing
-* Hidden Layers: Multiple layers that transform data through weighted connections
-* Output Layer: Produces the final prediction or output
-* Activation Functions: Non-linear functions (ReLU, sigmoid, tanh) that help networks learn complex patterns
-
-### Key Deep Learning Concepts
-1. Backpropagation
-   * Algorithm for calculating gradients in neural networks
-   * Efficiently updates weights by propagating error backwards through the network
-   * Uses chain rule to compute partial derivatives
-
-2. Gradient Descent Optimization
-   * Stochastic Gradient Descent (SGD)
-   * Mini-batch Gradient Descent
-   * Adaptive optimizers (Adam, RMSprop)
-
-3. Loss Functions
-   * Mean Squared Error (MSE) for regression
-   * Cross-Entropy Loss for classification
-   * Custom loss functions for specific tasks
-
-4. Regularization Techniques
-   * Dropout: Randomly deactivates neurons during training
-   * L1/L2 Regularization: Adds penalty terms to prevent overfitting
-   * Batch Normalization: Normalizes layer inputs for stable training
-
-### Deep Learning Architectures
-
-1. Convolutional Neural Networks (CNNs)
-   * Specialized for processing grid-like data (images)
-   * Key components: Convolutional layers, pooling layers, fully connected layers
-   * Applications: Image classification, object detection, segmentation
-
-2. Recurrent Neural Networks (RNNs)
-   * Process sequential data with memory of previous inputs
-   * Variants: LSTM, GRU for handling long-term dependencies
-   * Applications: Time series prediction, natural language processing
-
-3. Transformers
-   * State-of-the-art architecture for sequence processing
-   * Self-attention mechanism for capturing relationships
-   * Applications: Language models, machine translation, text generation
-
-4. Autoencoders
-   * Unsupervised learning for dimensionality reduction
-   * Encoder-decoder architecture
-   * Applications: Feature learning, denoising, anomaly detection
-
 - [Python & R Machine Learning](#python--r-machine-learning)
   - [R Machine Learning](https://github.com/chanshunli/jim-emacs-machine-learning/tree/master/R-Lang-machine-learning)
   - [least squares method](#least-squares-method)