Deep Learning Experiment Collection: Systematic Practice from Neural Network Basics to CNN and Optimization Techniques

This article introduces the "Deep-Learning-Experiments" project, which helps learners gain an in-depth understanding of deep learning's underlying mechanisms and engineering skills through two paths: implementation from scratch and framework practice. The project covers neural network basics (MLP), convolutional neural networks (CNN), optimization techniques, and modern deep learning extensions (such as RNN and attention mechanisms), aiming to enable learners to systematically master core deep learning content from theory to practice.

Current deep learning frameworks (PyTorch, TensorFlow) provide highly encapsulated APIs, which simplify model building but easily lead to learners' lack of understanding of underlying mechanisms (such as backpropagation and convolution operations). This project is based on the philosophy of "knowing not only what but also why", and designs two learning paths:

• Implementation from scratch path: Use pure Python/NumPy to implement core components (forward/backward propagation, activation functions, etc.) to understand every mathematical operation step;
• Framework practice path: Implement the same experiments using mainstream frameworks to master engineering skills such as automatic differentiation and GPU acceleration. These two paths complement each other, building both theoretical foundations and engineering capabilities.

Multilayer Perceptron (MLP) is the core of deep learning for beginners. This experiment guides learners to implement a complete MLP from scratch:

1. Forward propagation: Manually implement matrix multiplication, bias addition, and activation functions such as ReLU/Sigmoid/Tanh;
2. Loss functions: Implement MSE (regression) and cross-entropy (classification) to understand how loss quantifies the gap between predictions and true values;
3. Backpropagation: Use the chain rule to calculate parameter gradients for each layer and update weights and biases layer by layer;
4. Parameter initialization: Explore the impact of Xavier/He initialization on training;
5. Training loop: Build a complete process of "forward prediction → loss calculation → backward gradient → parameter update".

CNN is a core technology in computer vision. This experiment includes:

1. Convolution operation: Implement 2D convolution from scratch (including step size and padding control) to understand the principle of convolution kernel sliding for feature extraction;
2. Pooling layer: Implement max/average pooling to understand its role in dimensionality reduction and translation invariance;
3. Backpropagation: Master gradient propagation and parameter update for convolution layers;
4. Reproduction of classic architectures: Implement LeNet, AlexNet, VGG, ResNet, etc., using frameworks to understand their design ideas;
5. Visualization: Intuitively understand the CNN feature learning process through feature map visualization, filter visualization, and Grad-CAM.

This experiment explores the principles and effects of various optimization techniques:

1. Momentum method: Introduce the concept of velocity, accumulate gradient directions to accelerate convergence;
2. Adaptive learning rate: Implement AdaGrad, RMSProp, Adam, and compare the performance of different optimizers;
3. Learning rate scheduling: Implement strategies such as step decay and exponential decay to understand the necessity of reducing the learning rate in later stages;
4. Batch normalization: Implement batch normalization layers and analyze their role in stabilizing training and regularization;
5. Regularization: Apply L1/L2 regularization, Dropout, and early stopping to prevent overfitting.

The project also covers advanced topics:

1. Recurrent Neural Networks: Implement LSTM/GRU units to solve long-term dependency problems and apply them to sequence tasks;
2. Attention mechanism: Implement self-attention layers to understand the core of the Transformer architecture;
3. Generative models: Implement VAE and GAN to learn data distributions and generate new samples;
4. Transfer learning: Fine-tune pre-trained models (such as ResNet) for specific tasks to understand their good performance with small amounts of data.

Learning Value:

• Theoretical understanding: Manually implement algorithms to deeply grasp underlying principles;
• Debugging ability: Solve problems such as dimension mismatch and gradient errors to improve the ability to debug complex systems;
• Engineering intuition: Compare the effects of hyperparameter/structure changes to cultivate intuition for architecture design;
• Research foundation: Lay the foundation for reading cutting-edge papers and engaging in research.

Practical Suggestions:

1. First try to implement from scratch, then refer to the code when encountering difficulties;
2. Visualize loss curves, feature maps, etc., to build an intuitive understanding;
3. Change hyperparameters/structures, observe the impact, and cultivate experimental design capabilities;
4. Read PyTorch/TensorFlow source code to understand the details of production-level code.

The "Deep-Learning-Experiments" project provides a complete learning path from theory to practice. In the era of rapid AI iteration, framework tools will be updated, but the underlying mathematical principles and algorithmic ideas have lasting value. Through systematic experiments, learners not only master current technologies but also cultivate the ability to understand and create new technologies, which is essential basic training for in-depth development in the field of deep learning.