Optimization Level 16: Quantization

Results:
- Perceptron: ✅ (1.86s) 100.0%
- XOR: ✅ (1.90s) 54.5%
- MNIST: ✅ (2.05s) 10.0%
- CIFAR: ❌ (60.00s)
- TinyGPT: ✅ (1.84s)

optimization_log_20250928_214329.txt

@@ -54,3 +54,23 @@ Testing Optimization Level 15: Acceleration
 [2025-09-28 21:45:23]     ✅ Complete in 1.87s
 [2025-09-28 21:45:23] 
 Committing results for Acceleration...
+[2025-09-28 21:45:24]   Committed results
+[2025-09-28 21:45:24] 
+Verifying previous optimizations still work...
+[2025-09-28 21:45:24]   Previous optimizations verified
+[2025-09-28 21:45:24] 
+Testing Optimization Level 16: Quantization
+[2025-09-28 21:45:24] Description: Module 16: Quantization and compression
+[2025-09-28 21:45:24] ------------------------------------------------------------
+[2025-09-28 21:45:24]   Testing Perceptron with Quantization...
+[2025-09-28 21:45:26]     ✅ Complete in 1.86s
+[2025-09-28 21:45:26]   Testing XOR with Quantization...
+[2025-09-28 21:45:27]     ✅ Complete in 1.90s
+[2025-09-28 21:45:27]   Testing MNIST with Quantization...
+[2025-09-28 21:45:29]     ✅ Complete in 2.05s
+[2025-09-28 21:45:29]   Testing CIFAR with Quantization...
+[2025-09-28 21:46:00]     ⏱️ Timeout after 60s
+[2025-09-28 21:46:00]   Testing TinyGPT with Quantization...
+[2025-09-28 21:46:01]     ✅ Complete in 1.84s
+[2025-09-28 21:46:01] 
+Committing results for Quantization...

results_Quantization.json

@@ -0,0 +1,34 @@
+{
+  "Perceptron": {
+    "success": true,
+    "time": 1.8575267791748047,
+    "output_preview": "ion\n\n\ud83d\ude80 Next Steps:\n   \u2022 Continue to XOR 1969 milestone after Module 06 (Autograd)\n   \u2022 YOUR foundation enables solving non-linear problems!\n   \u2022 With 100.0% accuracy, YOUR perceptron works perfectly!\n",
+    "loss": 0.2038,
+    "accuracy": 100.0
+  },
+  "XOR": {
+    "success": true,
+    "time": 1.8962900638580322,
+    "output_preview": "ayer networks\n\n\ud83d\ude80 Next Steps:\n   \u2022 Continue to MNIST MLP after Module 08 (Training)\n   \u2022 YOUR XOR solution scales to real vision problems!\n   \u2022 Hidden layers principle powers all modern deep learning!\n",
+    "loss": 0.2497,
+    "accuracy": 54.5
+  },
+  "MNIST": {
+    "success": true,
+    "time": 2.04866886138916,
+    "output_preview": " a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n  one_hot[i, int(labels_np[i])] = 1.0\n",
+    "loss": 0.0,
+    "accuracy": 10.0
+  },
+  "CIFAR": {
+    "success": false,
+    "time": 60,
+    "timeout": true
+  },
+  "TinyGPT": {
+    "success": true,
+    "time": 1.8439507484436035,
+    "output_preview": "ining\n   \u2022 Complete transformer architecture from first principles\n\n\ud83c\udfed Production Note:\n   Real PyTorch uses optimized CUDA kernels for attention,\n   but you built and understand the core mathematics!\n",
+    "loss": 0.3174
+  }
+}