TensorFlow Tutorials and Courses

Machine learning is a complex field, but implementing its model has become easy with available frameworks like Google TensorFlow. TensorFlow is an open-source platform for creating machine learning models. It has flexible tools, libraries that allow the developers to build and deploy ML applications. TensorFlow is a combined bundle of ML and deep learning algorithms to solve complex numerical calculations. It also uses Python for creating front-end API and uses C++ for executing the applications.

The best way is to choose the language that supports the TensorFlow and get a great grip in that language like Python. You should have knowledge of linear algebra and how to preprocess the data. Once you get the basic idea of how to code, you can go deep into the coding part and solve neural network problems. If you sort the way to solve NN problems, then you can start with CNNs and RNNs.

The time may vary depending on how much you have knowledge about Python, deep learning, and their framework to implement machine learning. If you are completely new and do not even have the basic knowledge about the above mentioned, it may take time to learn from scratch. If you know Python, you can learn using TensorFlow within a few days as it will be easy to implement Python logic.

You can easily implement the deep learning models using the TensorFlow. It has a variety of libraries and uses data flow graphs to build models. You can create NNs with multiple layers. We can use TensorFlow for implementing below features to the applications:

• Voice recognition
• Text-based applications
• Image recognition
• Video detection
• Classification
• Prediction
• Perception

Google has used C++ programming language for the underlying TensorFlow software. But if you are going to develop AI applications using TensorFlow, you can use either C++ or Python, which are considered the most commonly used languages among developers.

No, the TensorFlow core is not written in Python but is written in C++ and CUDA. The large library of C++ and CUDA allows you to solve complex numerical calculations, including the NNs models. If you implement the models using C++, the code will execute fast, but the code will not get copied back to Python. So, developers used some C++ and some Python for better results.

TensorFlow itself is a platform for implementing machine learning models with complex calculations. But TensorFlow supports several APIs in different languages that allow you to create and execute the TensorFlow graphs. The most commonly used API in Python is easy to understand and handle. Still, other languages also show better performance for graph execution.