Implement ML using TensorFlow 2.3 (Oct 2020)
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 587 MB | Duration: 2h 4m

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