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
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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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