Getting Started with TensorFlow 2.0 for Deep Learning
Learn the core of Deep Learning using TensorFlow 2.0. Train your Deep Learning networks from scratch, pre-process and split your datasets. Use Deep Learning models for real-world applications.
- Self-paced with Life Time Access
- Certificate on Completion
- Access on Android and iOS App
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.
- 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
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