TensorFlow 2.0 Practical from Udemy

Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster. TensorFlow 2.0 is Google’s most powerful, recently released open source platform to build and deploy AI models in practice.

AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab.

The course provides students with practical hands-on experience in training Artificial Neural Networks and Convolutional Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to:

(1) Train Feed Forward Artificial Neural Networks to perform regression tasks such as sales/revenue predictions and house price predictions

(2) Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection.

(3) Train Deep Learning models to perform image classification tasks such as face detection, Fashion classification and traffic sign classification.

(4) Develop AI models to perform sentiment analysis and analyze customer reviews.

(5) Perform AI models visualization and assess their performance using Tensorboard

(6) Deploy AI models in practice using Tensorflow 2.0 Serving

The course is targeted towards students wanting to gain a fundamental understanding of how to build and deploy models in Tensorflow 2.0. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master AI and Deep Learning techniques and can directly apply these skills to solve real world challenging problems using Google’s New TensorFlow 2.0.

What's inside

Learning objectives

Master google’s newly released tensorflow 2.0 to build, train, test and deploy artificial neural networks (anns) models.
Learn how to develop anns models and train them in google’s colab while leveraging the power of gpus and tpus.
Deploy anns models in practice using tensorflow 2.0 serving.
Learn how to visualize models graph and assess their performance during training using tensorboard.
Understand the underlying theory and mathematics behind artificial neural networks and convolutional neural networks (cnns).
Learn how to train network weights and biases and select the proper transfer functions.
Train artificial neural networks (anns) using back propagation and gradient descent methods.
Optimize anns hyper parameters such as number of hidden layers and neurons to enhance network performance.
Apply anns to perform regression tasks such as house prices predictions and sales/revenue predictions.
Assess the performance of trained ann models for regression tasks using kpi (key performance indicators) such as mean absolute error, mean squared error, and root mean squared error, r-squared, and adjusted r-squared.
Assess the performance of trained ann models for classification tasks using kpi such as accuracy, precision and recall.
Apply convolutional neural networks to classify images.
Sample real-world, practical projects:

Project #1: train simple ann to convert celsius temperature reading to fahrenheit
Project #2 (exercise): train feedforward ann to predict revenue/sales
Project #3: as a real-estate consultant, predict house prices using anns (regression task)
Project #4 (exercise): as a business owner, predict bike rental usage (regression task)
Project #5: develop artificial neural networks in the medical field to perform classification tasks such as diabetes detection (classification task)
Project #6: develop ai models to perform sentiment analysis and analyze online customer reviews.
Project #7: train lenet deep learning models to perform traffic signs classification.
Project #8: train cnn to perform fashion classification
Project #9: train cnn to perform image classification using cifar-10 dataset
Project #10: deploy deep learning image classification model using tf serving
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Master google’s newly released tensorflow 2.0 to build, train, test and deploy artificial neural networks (anns) models.
Learn how to develop anns models and train them in google’s colab while leveraging the power of gpus and tpus.
Deploy anns models in practice using tensorflow 2.0 serving.
Learn how to visualize models graph and assess their performance during training using tensorboard.
Understand the underlying theory and mathematics behind artificial neural networks and convolutional neural networks (cnns).
Learn how to train network weights and biases and select the proper transfer functions.
Train artificial neural networks (anns) using back propagation and gradient descent methods.
Optimize anns hyper parameters such as number of hidden layers and neurons to enhance network performance.
Apply anns to perform regression tasks such as house prices predictions and sales/revenue predictions.
Assess the performance of trained ann models for regression tasks using kpi (key performance indicators) such as mean absolute error, mean squared error, and root mean squared error, r-squared, and adjusted r-squared.
Assess the performance of trained ann models for classification tasks using kpi such as accuracy, precision and recall.
Apply convolutional neural networks to classify images.
Sample real-world, practical projects:
Project #1: train simple ann to convert celsius temperature reading to fahrenheit
Project #2 (exercise): train feedforward ann to predict revenue/sales
Project #3: as a real-estate consultant, predict house prices using anns (regression task)
Project #4 (exercise): as a business owner, predict bike rental usage (regression task)
Project #5: develop artificial neural networks in the medical field to perform classification tasks such as diabetes detection (classification task)
Project #6: develop ai models to perform sentiment analysis and analyze online customer reviews.
Project #7: train lenet deep learning models to perform traffic signs classification.
Project #8: train cnn to perform fashion classification
Project #9: train cnn to perform image classification using cifar-10 dataset
Project #10: deploy deep learning image classification model using tf serving
Show more
Show less

Syllabus

INTRODUCTION AND COURSE OUTLINE

Introduction and Welcome Message

Course Overview

EXTRA: Learning Path

What's AI, ML and DL

Machine Learning - Big Picture

Whats new in TF2 and Google Colab

Whats New in TensorFlow 2.0

What is Google Colab

Google Colab Demo

Eager Execution

Keras API

Get the materials

BUILD YOUR FIRST SIMPLE PERCEPTRON (SINGLE NEURON) MODEL IN TF 2.0

PROJECT #1 OVERVIEW: CONVERT CELSUIS TO FAHRENHEIT

PROJECT #1 What are ANNs and How they learn?

PROJECT #1 Build our first ANN model

PROJECT #1 TF Playground

PROJECT #1 Coding Step 1 - Load TF and Data

PROJECT #1 Coding Step 2 - Model Training

PROJECT #1 Coding Step 3 - Model Evaluation

PROJECT #2 Overview

PROJECT#2: Google Colab Questions Overview

PROJECT # 2 Coding Part 1

PROJECT # 2 Coding Part 2

PROJECT # 2 Coding Part 3

BUILD A MULTI LAYER ARTIFICIAL NEURAL NETWORKS FOR REGRESSION TASKS

PROJECT #3: Overview

PROJECT #3 Regression basics

PROJECT #3 ANN in Action

PROJECT #3 Activation functions overview

PROJECT #3 MultiLayer Perceptron Network

PROJECT #3 ANN Training and Epochs Definition

PROJECT #3 Tensorflow Playground 3

PROJECT #3 Gradient Descent

PROJECT #3 Back Propagation

PROJECT #3 Bias Variance Tradeoff

PROJECT #3 Performance Metrics

PROJECT #3 Coding part 1

PROJECT #3 Coding part 2

PROJECT #3 Coding part 3

PROJECT #3 Coding part 4

PROJECT #3 Coding part 5 - Training

PROJECT #3 Coding part 6

PROJECT #4 Overview

PROJECT #4 Google Colab Overview

PROJECT #4 Coding Part 1

PROJECT #4 Coding Part 2

PROJECT #4 Coding Part 3

ARTIFICIAL NEURAL NETWORKS FOR CLASSIFICATION TASKS

PROJECT #5 Project Overview sentiment

PROJECT #5 Tokenization and Count Vectorizer

PROJECT #5 Confusion Matrix

PROJECT #5 Load Dataset

PROJECT #5 Data Visualization

PROJECT #5 Data Tokenization

PROJECT #5 Model Building and Training

PROJECT #5 Model Evaluation

PROJECT #6 Project Overview

PROJECT #6 Google Colab Project Questions Overview

PROJECT #6 Google Colab Project Questions Overview 2

PROJECT #6 Project Coding Solution Part 1

PROJECT #6 Project Coding Solution Part 2

DEEP LEARNING FOR IMAGE CLASSIFICATION

PROJECT #7 Overview

PROJECT #7 CNN Entire Network Overview

PROJECT #7 Feature Detectors

PROJECT #7 RELU

PROJECT #7 Pooling and Downsampling

PROJECT #7 Performance Improvement

PROJECT #7 Coding part 1 Import Data

PROJECT #7 Coding part 2 Visualization

PROJECT #7 Coding part 3 Train model

PROJECT #7 Coding part 4 - Evaluate model

PROJECT #8 Project Overview

PROJECT #8 LeNet Architecture

PROJECT #8 Coding part 1

PROJECT #8 Coding part 2

PROJECT #8 Coding part 3

PROJECT #9 Overview

PROJECT #9 Questions Overview

PROJECT #9 Solution Part 1

PROJECT #9 Solution Part 2

MODEL DEPLOYMENT USING TF SERVING

TF Serving Coding Part 1

TF Serving Coding Part 2

TF Serving Coding Part 3

Tensorboard Example 1

Tensorboard Example 2

Distributed Strategy

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Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a solid introduction to the latest version of TensorFlow, TF2.0, for students who are new to the area

Covers fundamental concepts of AI and deep learning, including topics like regression tasks, image processing, and natural language processing

Involves hands-on implementation using Python and Google Colab, providing students with practical experience

Introduces TensorBoard for visualizing models and assessing performance

Offers projects designed to address real-world challenges using AI techniques

Covers deployment techniques for trained models using TensorFlow Serving

Career center

Learners who complete TensorFlow 2.0 Practical will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer builds and deploys machine learning models to solve real-world problems. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Machine Learning Engineer, such as machine learning, artificial intelligence, and deep learning. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Engineer

An Artificial Intelligence Engineer designs, develops, and deploys artificial intelligence systems. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become an Artificial Intelligence Engineer, such as artificial intelligence, machine learning, and deep learning. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

See salaries and explore the career path for Artificial Intelligence Engineer

Data Analyst

A Data Analyst analyzes data to help businesses make better decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Data Analyst, such as data analysis, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

See salaries and explore the career path for Data Analyst

Data Scientist

A Data Scientist analyzes data to detect patterns and trends in order to help make business decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Data Scientist, such as data analysis, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

See salaries and explore the career path for Data Scientist

Marketing Analyst

A Marketing Analyst analyzes marketing data to help businesses make better decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Marketing Analyst, such as marketing analysis, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

See salaries and explore the career path for Marketing Analyst

Quantitative Analyst

A Quantitative Analyst analyzes data to help businesses make better decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Quantitative Analyst, such as quantitative analysis, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

See salaries and explore the career path for Quantitative Analyst

Game Developer

A Game Developer designs, develops, and deploys video games. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Game Developer, such as game development, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

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Robotics Engineer

A Robotics Engineer designs, develops, and deploys robots. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Robotics Engineer, such as robotics, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

See salaries and explore the career path for Robotics Engineer

Research Scientist

A Research Scientist conducts research in a variety of fields. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Research Scientist, such as research, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

See salaries and explore the career path for Research Scientist

Computer Vision Engineer

A Computer Vision Engineer designs, develops, and deploys computer vision systems. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Computer Vision Engineer, such as computer vision, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

See salaries and explore the career path for Computer Vision Engineer

Natural Language Processing Engineer

A Natural Language Processing Engineer designs, develops, and deploys natural language processing systems. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Natural Language Processing Engineer, such as natural language processing, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

See salaries and explore the career path for Natural Language Processing Engineer

Healthcare Analyst

A Healthcare Analyst analyzes healthcare data to help healthcare providers make better decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Healthcare Analyst, such as healthcare analysis, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

See salaries and explore the career path for Healthcare Analyst

Operations Research Analyst

An Operations Research Analyst analyzes data to help businesses make better decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become an Operations Research Analyst, such as operations research, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

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Software Engineer

A Software Engineer designs, develops, and deploys software applications. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Software Engineer, such as software development, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

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Financial Analyst

A Financial Analyst analyzes financial data to help businesses make better decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Financial Analyst, such as financial analysis, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.

See salaries and explore the career path for Financial Analyst