Multivariable Calculus and Gradient Descent
Multivariable calculus for AI. Learn to implement Gradient Descent, understand optimization landscapes, and use Python to train ML models.
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Optimization
Derivatives, Partial Derivatives, and the Chain Rule
Calculus foundations of AI, covering derivatives, partial derivatives, the chain rule, and gradient descent with Python examples in TensorFlow.
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Profiling and Benchmarking ESP32 Applications
Implement profiling and benchmarking strategies for ESP32 applications to measure execution time, resource usage, and compare different implementations.
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Runtime Performance Analysis
Analyze the runtime performance of your ESP32 applications using tools like GDB, tracing, and ESP-IDF’s profiling features to identify bottlenecks.
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ESP-NOW Range Optimization
Techniques to optimize the communication range of ESP-NOW on ESP32, including antenna choice, power settings, and protocol timing.
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BLE Performance Optimization
Techniques to optimize BLE performance on ESP32, including connection parameters, MTU size negotiation, and efficient data transfer strategies.
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WiFi Performance Optimization Techniques
Learn techniques to optimize WiFi performance on ESP32, including antenna selection, buffer tuning, protocol choices, and minimizing interference.
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Using Heap Memory Efficiently
Learn best practices for efficient heap memory usage on ESP32. Avoid fragmentation and memory leaks in FreeRTOS applications.
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