Machine Learning Projects with TensorFlow 2.0

Machine Learning Projects with TensorFlow 2.0, available at $19.99, has an average rating of 4.15, with 36 lectures, 5 quizzes, based on 10 reviews, and has 103 subscribers.

You will learn about Strengthen your foundations to build TensorFlow 2.0 projects by exploring its new features Analyze the Titanic data set to obtain desired results with ease Implement and organize your Tensorflow projects in a professional manner Use Tensorboard to inspect various metrics and monitor your project’s performance Research and make the most of other people's Kaggle solutions Use OpenAI Gym Environments for implementing state of the art reinforcement learning techniques using TF-Agents Apply the latest Transfer Learning techniques from Tensorflow This course is ideal for individuals who are This course is for developers, data scientists and ML engineers who now want to enhance their skill set in Machine Learning using TensorFlow by building real-world projects. It is particularly useful for This course is for developers, data scientists and ML engineers who now want to enhance their skill set in Machine Learning using TensorFlow by building real-world projects.

Enroll now: Machine Learning Projects with TensorFlow 2.0

Summary

Title: Machine Learning Projects with TensorFlow 2.0

Price: $19.99

Average Rating: 4.15

Number of Lectures: 36

Number of Quizzes: 5

Number of Published Lectures: 36

Number of Published Quizzes: 5

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Strengthen your foundations to build TensorFlow 2.0 projects by exploring its new features
Analyze the Titanic data set to obtain desired results with ease
Implement and organize your Tensorflow projects in a professional manner
Use Tensorboard to inspect various metrics and monitor your project’s performance
Research and make the most of other people's Kaggle solutions
Use OpenAI Gym Environments for implementing state of the art reinforcement learning techniques using TF-Agents
Apply the latest Transfer Learning techniques from Tensorflow

Who Should Attend

This course is for developers, data scientists and ML engineers who now want to enhance their skill set in Machine Learning using TensorFlow by building real-world projects.

Target Audiences

This course is for developers, data scientists and ML engineers who now want to enhance their skill set in Machine Learning using TensorFlow by building real-world projects.

TensorFlow is the world’s most widely adopted framework for Machine Learning and Deep Learning. TensorFlow 2.0 is a major milestone due to its inclusion of some major changes making TensorFlow easier to learn and use such as “Eager Execution”. It will support more platforms and languages, improved compatibility and remove deprecated APIs.

This course will guide you to upgrade your skills in Machine Learning by practically applying them by building real-world Machine Learning projects.

Each section should cover a specific project on a Machine Learning task and you will learn how to implement it into your system using TensorFlow 2. You will implement various Machine Learning techniques and algorithms using the TensorFlow 2 library. Each project will put your skills to test, help you understand and overcome the challenges you can face in a real-world scenario and provide some tips and tricks to help you become more efficient. Throughout the course, you will cover the new features of TensorFlow 2 such as Eager Execution. You will cover at least 3-4 projects. You will also cover some tasks such as Reinforcement Learning and Transfer Learning.

By the end of the course, you will be confident to build your own Machine Learning Systems with TensorFlow 2 and will be able to add this valuable skill to your CV.

About the Author

Vlad Ionescu is a lecturer at Babes-Bolyai University. He has a PhD in machine learning, a field he is continuously researching and exploring every day with technologies such as Python, Keras, and TensorFlow.

His philosophy is “If I can’t explain something well enough for most people to understand it, I need to go back and understand it better myself before trying again”. This philosophy helps him to give of his best in his lectures and tutorials.

He started as a high school computer science teacher while he was doing his Masters over 5 years ago. Right now, he teaches various university-level courses and tutorials, covering languages, technologies, and concepts such as Python, Keras, machine learning, C#, Java, algorithms, and data structures.

During his high school and college years, he participated in many computer science contests and Olympiads and was active on some online judge sites. He also owns a StackOverflow gold badge in the Algorithm tag.

Course Curriculum

Chapter 1: Regression Task Airbnb Prices in New York

Lecture 1: Course Overview

Lecture 2: Setting Up TensorFlow 2.0

Lecture 3: Getting Started with TensorFlow 2.0

Lecture 4: Analyzing the Airbnb Dataset and Making a Plan

Lecture 5: Implementing a Simple Linear Regression Algorithm

Lecture 6: Implementing a Multi Layer Perceptron (Artificial Neural Network)

Lecture 7: Improving the Network with Better Activation Functions and Dropout

Lecture 8: Adding More Metrics to Gain a Better Understanding

Lecture 9: Putting It All Together in a Professional Way

Chapter 2: Classification Task Build Real World Apps: Who Will Win the Next UFC?

Lecture 1: Collecting Possible Kaggle Data

Lecture 2: Analysis and Planning of the Dataset

Lecture 3: Introduction to Google Colab and How It Benefits Us

Lecture 4: Setting Up Training on Google Colab

Lecture 5: Some Advanced Neural Network Approaches

Lecture 6: Introducing a Deeper Network

Lecture 7: Inspecting Metrics with TensorBoard

Lecture 8: Inspecting the Existing Kaggle Solutions

Chapter 3: Natural Language Processing Task: How to Generate Our Own Text

Lecture 1: Introduction to Natural Language Processing

Lecture 2: NLP and the Importance of Data Preprocessing

Lecture 3: A Simple Text Classifier

Lecture 4: Text Generation Methods

Lecture 5: Text Generation with a Recurrent Neural Network

Lecture 6: Refinements with Federated Learning

Chapter 4: Reinforcement Learning Task: How to Become Best at Pacman

Lecture 1: Introduction to Reinforcement Learning

Lecture 2: OpenAI Gym Environments

Lecture 3: The Pacman Gym Environment That We Are Going to Use

Lecture 4: Reinforcement Learning Principles with TF-Agents

Lecture 5: TF-Agents for Our Pacman Gym Environment

Lecture 6: The Agents That We Are Going to Use

Lecture 7: Selecting the Best Approaches and Real World Applications

Chapter 5: Transfer Learning Task: How to Build a Powerful Image Classifier

Lecture 1: Introduction to Transfer Learning in TensorFlow 2

Lecture 2: Picking a Kaggle Dataset to Work On

Lecture 3: Picking a Base Model Suitable for Transfer Learning with Our Dataset

Lecture 4: Implementing our Transfer Learning approach

Lecture 5: How Well Are We Doing and Can We Do Better

Lecture 6: Conclusions and Future Work

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

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2 stars: 0 votes
3 stars: 2 votes
4 stars: 3 votes
5 stars: 4 votes

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