Learn to use Python for Deep Learning with Google’s latest Tensorflow 2 library and Keras!
What you’ll learn
Complete Tensorflow 2 and Keras Deep Learning Bootcamp Course Site
- 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
- Know how to code in Python
- Some math basics such as derivatives
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!
We also have plenty of exercises to test your new skills along the way!
This course covers a variety of topics, including
- NumPy Crash Course
- Pandas Data Analysis Crash Course
- Data Visualization Crash Course
- Neural Network Basics
- TensorFlow 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!
1. 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
Who this course is for:
- Python developers interested in learning about TensorFlow 2 for deep learning and artificial intelligence