Tensorflow with Python

Free Courses : Tensorflow with Python

The TensorFlow is an open-source library for machine learning and deep learning applications. It is a freeware and does not require a license. TensorFlow was developed by Google Brain Team. TensorFlow was initially released in the year 2015. It was purely written in Python, C++ and CUDA languages. It supports multiple cross platforms such as macOS, Windows, Linux, Android, etc. It is mainly used in the form of a Math library. It was licensed under Apache License 2.0. The usage of Machine Learning contains the classification of basic elements and text, overfitting and underfitting, saving and restoration models. The production scale levels of Machine Learning include linear model, wide and deep learning, boosted trees, estimators based on CNN. The different generative models under TensorFlow are the translation, image captioning, DCGAN and VAE techniques. The different data representation ways in TensorFlow are a vector representation of words, kernel methods, large scale linear models and Unicode.

This TensorFlow is a machine learning platform that is under open source licensing. TensorFlow library can be used for both production and research applications. The different applications that can be carried out under TensorFlow are Research and experimentation, production scale Machine Learning, generative models, Images, Sequences, Load data, data representation, Non-Machine Learning applications.

The training will include the following:

1. Tensorflow Installation using Pip and Anaconda Navigator
2. TensorFlow Introduction
3. Environment set up in PyCharm IDE and running Sample Hello World Program
4. Data Types used in TensorFlow and their handling in Python
5. Implementing Linear model example, calculating loss value and reducing loss value using Optimizer and Train
6. Updating existing data element value using Feed Dictionary
7. Placeholder example and Usage and declaration of Constructor
8. Addition of 2 numbers and progammatically calculation of Random numbers

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