Keras Courses

What is Keras?

Keras is a deep learning library designed to enable fast experimentation. It can run on top of Theanos, Tensorflow, or CNTK and uses Python as the primary language. It's built to allow for access to complex machine learning tasks by removing framework barriers. Keras 2 was released in 2017 to update the system further. Keras enables a stack of layers and reduces the time you spend building your training data. It runs concurrently with standard deep learning systems. It supports convolutional and recurrent networks while using an API designed for human accessibility. It allows you to prototype and experiment quickly, and it runs seamlessly on GPU and CPU. Keras documentation is provided on Github and https://keras.io.

Learn Keras

Keras is fast becoming a requirement for working in data science and machine learning. It creates a backend environment that speeds innovation by relieving the pressure on users to choose and maintain a framework to build deep learning models. Developers use Keras to define and train neural network models, but use only a few lines of code. It simplifies the process, and the guiding principle is a user-friendly application.

Keras Courses and Certifications

You can get started with deep learning fundamentals in IBM's course offered in partnership with edx.org. You'll build your first deep learning model using the Keras library from the moment you install Keras to prototyping. From there, you can expand to IBM's full certification on deep learning. You'll move through use cases, including high-level models like computer vision, plus several popular libraries including Tensorflow, NumPy, and PyTorch. If you're curious about computer vision specifically, Microsoft's course can give you everything you need to know. You'll learn how Python packages (Scikit-learn or PIL) apply to computer vision and how the Keras model makes it easier to develop these programs.

Ignite Your Career with Keras

Keras provides training data for easy use so that building these sequential models requires less labor upfront. The library runs concurrently with popular frameworks and languages, high-level API's are written for humans. Deep neural networks have so much potential, and learning Keras gives you a leg up in building these complex networks. It makes things like the TensorFlow backend more accessible and makes it so much easier to run models. Reduce the amount of time you spend messing around with complex programs and build a more straightforward system for deploying your deep learning models.