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Implement ML using TensorFlow 2.3 (Oct 2020)

 

Implement ML using TensorFlow 2.3 (Oct 2020)

GETTING STARTED


The TensorFlow project announced the release of version 2.3.0, ... The tf.data package was introduced in TensorFlow version 1.4 as a way to ... This content is in the AI, ML & Data Engineering topic ... MLOps and Reproducible ML on AWS with Kubeflow and SageMaker (Live Webinar, October 7th, 2020) ...
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What you'll learn

  • Introduction to TensorFlow
  • Introduction to Google Colaboratory (Colab)
  • Classification and Regression Mechanism
  • Neural networks and implementation of neural network
  • Recommender System
  • Transfer Learning and Fine Tuning
  • Implementation of Deep convolutional GAN
  • Implementation of Cycle GAN

Requirements

  • Basics of Machine Learning
  • Python (Scipy, Scikit, Matplotlib, Pandas)
  • Working knowledge on Jupyter Notebook

Description

This course takes you through hands-on approach with TensorFlow using Google Colab.

In this course you will have an overview of TensorFlow and its key features and also you will look upon the TensorFlow architecture, Advantages and benefits of using TensorFlow. You will also explore on Neural networks and implementation on types of neural Network in depth using Classification and regression mechanism. You will also learn and understand about the advantages and benefits of using neural networks in brief.

Further, you will learn what is recommender system with an example and different ways to approach recommender system. Besides, you will also get to know the importance of recommender system.

You will explore on how to perform transfer learning on building the model and how to fine tune it. Additionally, you will have a brief overview about GAN (Generative adversarial Network)


Who this course is for:

  • Data Scientists
  • Machine Learning Developers
  • Big Data Developers
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