A Visual Learners Guide to Building Neural Networks Using Keras
What you’ll learn
- At the end of the course you’ll understand how to create an end to end deep learning model using the Keras Library in Python.
- In the course we will walk through every line of code so you’ll be able to understand the model and the process.
- You will also receive a completed Jupyter Notebook filled with models and references.
- You’ll also learn how to adjust key parameters of the model in order to get better performance out of it.
- You’ll need to understand the basics of Python.
Welcome toA Gentle Introduction to Deep Learning Using Keras.
Keras is a powerful easy-to-use Python library for developing and evaluating deep learning models.
It wraps the efficient numerical computation libraries Theano and TensorFlow and allows you to define and train neural network models in a few short lines of code.
In this course, we are going to build an end-to-end Python machine learning project. You’ll learn how to use Kerasto build and tune a deep neural network.
Keras is quickly becoming the de facto tool to do deep learning in Python, especially for beginners. Its minimalist, modular approach makes it simple to get deep neural networks up and running.
A Jupyter notebook is a web app that allows you to write and annotate Python code interactively. It’s a great way to experiment, do research, and share what you are working on.
In this course all of the tutorials will be created using jupyter notebooks. In the preview lessons we install Python. Check them out. They are completely free.
We will also gently introduce you to the vernacular of deep learning. For example, a deep neural network is simply a neural network with more than one hidden layer. That’s it.
Actually, a hidden layer really means “not an input or an output.”
Why all the hype around deep learning? While much of the hype in the IT world is just that, the hype around deep learning may be the real thing. Recently, deep learning models have been outperforming every other kind of machine learning model.
You’ll get hands on experience with the process of machine learning. The processinvolves importing data, cleaning the data, training and testing, pre-processing and feature engineering.
We are going to define new terms but we willskip the math and theory for now.
Thanks for your interest in A Gentle Introduction to Deep Learning Using Keras.
See you in the course!!!!
Who this course is for:
- If you’re interested in Machine Learning and Deep Learning then this course is for you.