Implement ML using TensorFlow 2.3 (Apr 2023)

Implement ML using TensorFlow 2.3 (Apr 2023), available at $19.99, has an average rating of 4.15, with 22 lectures, 7 quizzes, based on 14 reviews, and has 296 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Scientists or Machine Learning Developers or Big Data Developers It is particularly useful for Data Scientists or Machine Learning Developers or Big Data Developers.

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Summary

Title: Implement ML using TensorFlow 2.3 (Apr 2023)

Price: $19.99

Average Rating: 4.15

Number of Lectures: 22

Number of Quizzes: 7

Number of Published Lectures: 22

Number of Published Quizzes: 7

Number of Curriculum Items: 29

Number of Published Curriculum Objects: 29

Original Price: ?3,299

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Data Scientists

Machine Learning Developers

Big Data Developers

Target Audiences

Data Scientists

Machine Learning Developers

Big Data Developers

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

In this course you will have an overview of TensorFlow. TensorFlow is an open source software library released by Google. It is a Python library/framework which allows developers to express arbitrary computation as data flow graph and for easy calculation of complex mathematical expressions.

Here you will look upon TensorFlow architecture, Advantages and benefits of TensorFlow. You will also explore on Neural networks and implementation, types of neural Network in depth using Classification and regression mechanism. 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)

Our focus is to teach topics that flow smoothly. The course teaches you everything you need to know about Implementation of ML using TensorFlow 2.3 with hands-on examples.

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Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Introduction to TensorFlow and Google Colaboratory

Lecture 1: Lesson 1: Introduction to TensorFlow and Google Colaboratory

Lecture 2: Practice 1-1: Set up Google Colaboratory for TensorFLow

Lecture 3: Practice 1-2: Creating and Running Simple Applications using TensorFlow

Chapter 3: Implementing Classification and Regression using TensorFlow

Lecture 1: Lesson 2: Implementing Classification and Regression using TensorFlow

Lecture 2: Practice 2-1: Implement ML Application using Linear Classification Mechanism

Lecture 3: Practice 2-2: ML Application using Linear Regression Mechanism

Chapter 4: Neural Networks and Artificial Neural Network (ANN)

Lecture 1: Lesson 3: Neural Networks and Artificial Neural Network (ANN)

Lecture 2: Practice 3-1: Implementing Image Classification Application using ANN

Lecture 3: Practice 3-2: Implementing Logistic Regression Application using ANN

Chapter 5: Recurrent Neural Network (RNN) and Time Series Prediction

Lecture 1: Lesson 4: Recurrent Neural Network (RNN) and Time Series Prediction

Lecture 2: Practice 4-1: Implementing Time Series Prediction using RNN

Lecture 3: Practice 4-2: Implementing Image Classification using RNN

Chapter 6: Working with Convolution Neural Network (CNN)

Lecture 1: Lesson 5: Working with Convolution Neural Network (CNN)

Lecture 2: Practice 5-1: Implementing CIFAR-10 Application using CNN

Lecture 3: Practice 5-2: Implementing Fashion MNIST Application using CNN

Chapter 7: Recommender System, Transfer Learning and Fine Tuning

Lecture 1: Lesson 6: Recommender System, Transfer Learning and Fine Tuning

Lecture 2: Practice 6-1: Implement Recommender Systems using Deep Learning Concept

Lecture 3: Practice 6-2: Working with Transfer Leaning and Fine Tuning

Chapter 8: Generative Adversarial Network (GAN)

Lecture 1: Lesson 7: Generative Adversarial Network (GAN)

Lecture 2: Practice 7-1: Implement DCGAN for Digit MNIST Dataset

Lecture 3: Practice 7-2: Implement CycleGAN for Translating the Images

Instructors

Proton Expert Systems & Solutions
Proton Expert Systems & Solutions

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2 stars: 1 votes

3 stars: 4 votes

4 stars: 2 votes

5 stars: 7 votes

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