Machine Learning Projects with TensorFlow 2.0
What you'll 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
Course content
- Preview06:08
- 03:16Setting Up TensorFlow 2.0
- 08:55Getting Started with TensorFlow 2.0
- 03:50Analyzing the Airbnb Dataset and Making a Plan
- 13:04Implementing a Simple Linear Regression Algorithm
- 11:38Implementing a Multi Layer Perceptron (Artificial Neural Network)
- 05:41Improving the Network with Better Activation Functions and Dropout
- 05:10Adding More Metrics to Gain a Better Understanding
- 07:08Putting It All Together in a Professional Way
- 4 questionsTest your knowledge
Requirements
- This course will appeal to someone who has a basic understanding of ML concepts, Python and TensorFlow.
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.
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.
Who this course 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.
Instructor
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- 62,348 Reviews
- 371,502 Students
- 1,266 Courses
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