Description
Course Workflow: This course is focused on Embedded Deep learning in Python. Raspberry PI 4 is utilized as a main hardware and we will be building practical projects with custom data. We will start with trigonometric functions approximation. In which we will generate random data and produce a model for Sin function approximationNext is a calculator that takes images as input and builds up an equation and produces a result. This Computer vision based project is going to be using convolution network architecture for Categorical classificationAnother amazing project is focused on convolution network but the data is custom voice recordings. We will involve a little bit of electronics to show the output by controlling our multiple LEDs using own voice. Unique learning point in this course is Post Quantization applied on Tensor flow models trained on Google Colab. Reducing size of models to 3 times and increasing inferencing speed up to 0.03 sec per input. Sections : Non-Linear Function ApproximationVisual CalculatorCustom Voice Controlled LedOutcomes After this Course: You can create Deep Learning Projects on Embedded HardwareConvert your models into Tensorflow Lite modelsSpeed up Inferencing on embedded devicesPost QuantizationCustom Data for Ai ProjectsHardware Optimized Neural NetworksComputer Vision projects with OPENCVDeep Neural Networks with fast inferencing SpeedHardware RequirementsRaspberry PI 412V Power Bank2 LEDs ( Red and Green )Jumper Wires Bread BoardRaspberry PI Camera V2RPI 4 Fan3D printed Parts Software Requirements Python3Motivated mind for a huge programming Project----------------------------------------- Before buying take a look into this course GitHub repository
