Build and train models for real-world machine learning projects using Tensorflow 2.0
Key Features
- Make use of the amazing new feature of TensorFlow 2 called 'Eager Execution' which makes it easier to learn and use
- Upgrade your skills by building real-world Machine Learning projects
- Build, test and deploy different ML models and learn more modern techniques such as Reinforcement Learning and Transfer Learning
Book Description
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.
Codefiles are uploaded here:
https://github.com/PacktPublishing/Machine-Learning-Projects-with-TensorFlow-2.0
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 this book is 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.
This course will appeal to someone who has a basic understanding of ML concepts, Python and TensorFlow.
