Tensorflow with Python
What you'll learn
- Learn installation of TensorFlow, Introduction of TensorFlow, different data types in TensorFlow, PyCharm IDE environment setup, etc
- The set of skills that can be acquired upon completion of this TensorFlow training are Data Analysis, TensorFlow, Deep Learning Application
- There are few skills also which could be obtained in completing this course are such as TensorFlow Model, Neural Networks, PyCharm IDE, TensorFlow Eager API, Linear regression, Logistic regression, and TensorFlow, etc
Course content
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Requirements
- The TensorFlow training does not contain any prerequisites and can be preferred by any learner to master the basic concepts or knowledge on Machine Learning, Deep learning, data analytics tools, data processing techniques using PyCharm IDE, etc. All the learners who are interested to learn the TensorFlow concepts such as installation and setup of deep learning model using TensorFlow library and python programming etc. Data Analysis and Data Visualization techniques can also be carried out using different tools using the TensorFlow library which is explained in this course.
- Any previous knowledge or hands-on in the areas of Data Analytics or Big Data Development or Hadoop Development or data analytics tools is an added advantage in further learning the contents of the TensorFlow training.
Description
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
Who this course is for:
- The learners who are holding any Bachelor’s engineering in Computer Science or any technical areas can choose this TensorFlow training as a better option in getting expertise in Deep Learning technologies. All the learners who are keen in learning and obtaining knowledge on Deep Learning techniques or Data processing or Big data analytics or Hadoop frameworks can opt for this TensorFlow course.
- Software Developer, Research Scientist, Data Analyst, Business Analyst, Hadoop Developer, Researcher, SAS Programmer, R Programmer, Machine Learning Engineer, Machine Learning Developer, AI Expert, Chatbots Developer, AI ML Engineer, Python Developer, Python ML Engineer, Solution Architect, Machine Learning Scientist, etc. This course can opt also to pursue better career opportunities in the area of Machine Learning or Deep learning processes.
Instructor
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- 136,686 Students
- 36 Courses
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