1. Keras for Beginners: Building Your First Neural Network
| UPDATED
A beginner-friendly guide on using Keras to implement a simple Neural Network in Python.
Keras for Beginners
Keras is a simple-to-use but powerful deep learning library for Python. This 3-post series, written for beginners, provides a simple way for anyone to get started solving real machine learning problems. We’ll start with an introduction to classic Neural Networks for complete beginners before delving into two popular variants: Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs).
For each of each these types of networks, we’ll:
- Introduce a (simple) real-world problem
- Preprocess the dataset
- Build a model for the problem
- Train and test the model
- Brainstorm ideas to keep improving the model
At the end of each post, you’ll have the full code to accomplish everything listed above.
Background
This series requires ZERO prior experience with Keras or Tensorflow. However, background in the following topics may be helpful:
- Neural Networks in general (how do they work, what are they for?). I recommend my Neural Networks from Scratch series to get started here.
- Python - basic familiarity with the language will help you move through the series faster.
- Experience with other deep-learning libraries, which will help you ramp up faster on Keras.
The Series
Without further ado:
2. Keras for Beginners: Implementing a Convolutional Neural Network
| UPDATED
A beginner-friendly guide on using Keras to implement a simple Convolutional Neural Network (CNN) in Python.
3. Keras for Beginners: Implementing a Recurrent Neural Network
A beginner-friendly guide on using Keras to implement a simple Recurrent Neural Network (RNN) in Python.
What Next?
Still eager to learn? Some more things you can do include:
- Apply neural networks to Visual Question Answering (VQA), an interesting Computer Vision problem.
- Try your hand at using Neural Networks to approach a Kaggle data science competition.
- Take one of many good Neural Networks courses on Coursera.
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Thanks for reading!