Learn to use Python for Deep Learning with Google's latest Tensorflow 2 library and Keras!
What you'll learn:
Learn to use TensorFlow 2.0 for Deep Learning
Leverage the Keras API to quickly build models that run on Tensorflow 2
Perform Image Classification with Convolutional Neural Networks
Use Deep Learning for medical imaging
Forecast Time Series data with Recurrent Neural Networks
Use Generative Adversarial Networks (GANs) to generate images
Use deep learning for style transfer
Generate text with RNNs and Natural Language Processing
Serve Tensorflow Models through an API
Use GPUs for accelerated deep learning
This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand.
We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more!
This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!
This course covers a variety of topics, including
Pandas Data Analysis Crash Course
Data Visualization CrashCourse
Neural Network Basics
Keras Syntax Basics
Artificial Neural Networks
Densely Connected Networks
Convolutional Neural Networks
Recurrent Neural Networks
GANs - Generative Adversarial Networks
Deploying TensorFlow into Production
and much more!
Keras, a user-friendly API standard for machine learning, will be the central high-level API used to build and train models. The Keras API makes it easy to get started with TensorFlow 2. Importantly, Keras provides several model-building APIs (Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project. TensorFlow’s implementation contains enhancements including eager execution, for immediate iteration and intuitive debugging, and tf.data, for building scalable input pipelines.
TensorFlow 2 makes it easy to take new ideas from concept to code, and from model to publication. TensorFlow 2.0 incorporates a number of features that enables the definition and training of state of the art models without sacrificing speed or performance
It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!
Become a deep learning guru today! We'll see you inside the course!