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Getting Started With TensorFlow 2.0 For Deep Learning

Getting Started With TensorFlow 2.0 For Deep Learning

Learn to develop deep learning models and kickstart your career in deep learning with TensorFlow 2.0.


Learn to develop deep learning models and kickstart your career in deep learning with TensorFlow 2.0.

Deep learning is a trending technology if you want to break into cutting-edge AI and solve real-world, data-driven problems. Google’s TensorFlow is a popular library for implementing deep learning algorithms because of its rapid developments and commercial deployments.

This course provides you with the core of deep learning using TensorFlow 2.0. You’ll learn to train your deep learning networks from scratch, pre-process and split your datasets, train deep learning models for real-world applications, and validate the accuracy of your models.

By the end of the course, you’ll have a profound knowledge of how you can leverage TensorFlow 2.0 to build real-world applications without much effort.

All the notebooks and supporting files for this course are available on GitHub at

About the Author

Muhammad Hamza Javed is a self-taught machine learning engineer, an entrepreneur, and an author with over five years of industrial experience. Along with his team, he has been working on several computer vision, machine learning, and deep learning international projects. He learned skills on his own without a direct mentor, so he knows how troublesome it is for everyone to find to-the-point content that improves one’s skillset. He’s designed this course considering the challenges he faced when he learned and in projects, so you don’t have to spend too much time finding what’s best for you.

Course Information

Basic knowledge of Python is required

Develop real-world deep learning applications
Classify IMDb Movie Reviews using Binary Classification Model
Build a model to classify news with multi-label
Train your deep learning model to predict house prices
Understand the whole package: prepare a dataset, build the deep learning model, and validate results
Understand the working of Recurrent Neural Networks and LSTM with hands-on examples
Implement autoencoders and denoise autoencoders in a project to regenerate images

This course is for developers, programmers, and data scientists who are familiar with machine learning concepts and want to get into deep learning using TensorFlow 2.0 in a fast and compelling way.

• Lifetime Access to Each Course
• Certificate on Completion of Course
• No Extra Charges Or Admin Fees
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• High Priority Support After Sales.
• Big Discounts on Individual Courses

Course Specifications

IT and Computing courses are available to study on our learning platform. 

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Adult education is the non-credential activity of gaining skills and improved education. 

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Online education is electronically supported learning that relies on the Internet for teacher/student interaction. 

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A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

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Course duration is 24 hours.

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