This is the code repository for Getting Started with TensorFlow 2.0 for Deep Learning [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
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.
- 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 who have a basic knowledge of Python. If you’re aware of the basics of machine learning and now want to build deep learning systems with TensorFlow 2.0 that are smarter, faster, more complex, and more practical, then this course is for you!
This course has the following requirements:
Jupyter Notebook, Latest Version
Operating system: Mac/Linux
Python 3.x
basic programming skills