VijayBlogs

 What are the differences between Android and Linux ..? Bionic C: Uses less memory and is not CPU-hungry Low Memory Killer (OOM killer): Out of Memory killer decides which process is less important and can be terminated to free up the memory. Binder IPC HAL Logcat dmesg logs - we can see kernel logs logcat logs - Android app logs Wakelocks Linux Architecture Drivers play a crucial role as intermediaries between the hardware and the software. Here’s a simplified explanation: Think of the Linux kernel as a busy office manager who needs to ensure that all the different office equipment (hardware) works smoothly with the staff (software). The drivers are like specialized assistants who know how to operate each piece of equipment. They translate the staff’s requests into actions that the equipment can understand and carry out. Android Architecture How Linux Boots ..? When you power-on , the bootloader will be loaded to RAM and it finds the kernel and loads to RAM. Once the kernel boots ...

 Android Automotive Android Automotive is a base Android platform specifically designed for use in vehicle dashboards. It provides a full-stack solution, including both the underlying operating system and the user interface layer. Android Automotive is highly customizable, allowing implementers (such as car manufacturers) to differentiate their products according to their vision and requirements. Difference between Android Auto and Android Automotive Android Auto : This platform runs on the user's phone and projects the Android Auto user experience to a compatible in-vehicle infotainment system via USB. It supports apps designed for in-vehicle use. Android Automotive : It operates directly on the in-vehicle hardware, providing a complete infotainment platform. Android Automotive supports both Android apps and those built specifically for Android Auto. Android Open Source Project AOSP is the open-source operating system project maintained by Google and AOSP is the foundation upon w...

  Transfer learning is a machine learning technique where a model that is trained with a large dataset is applied to an unknown dataset. Benefits :  Significantly reduce the time and resources needed to develop and train a new model from scratch. Transfer learning can lead to better performance when the model is applied to the new task, especially when the available data for the new task is limited. It also helps in preventing overfitting and can speed up the training process. In Transfer learning we need to change the last output layer to match to output classes of new problem dataset. Pre-Trained model will be freeze and only the last output layer will be trained with new dataset and ensure the already existing model will not be modified with new data. Code:  Cat and dog Dataset example :  https://colab.research.google.com/github/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l06c01_tensorflow_hub_and_transfer_learning.ipynb E...