Keras: Deep Learning in Python

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Build complex deep learning algorithms easily in Python

414 students enrolled

What Will I Learn?

- Use Keras for classification and regression in typical data science problems
- Use Keras for image classification
- Define Convolutional neural networks
- Train LSTM models for sequences
- Process the data in order to achieve to the specific shape that Keras expects for each problem
- Code neural networks directly in Theano using tensor multiplications
- Understand what are the different layers that we have in Keras
- Design neural networks that mitigate the effect of overfitting using specific layers
- Understand how backpropagation and stochastic gradient descent work

Requirements

- Python
- Some previous experience with data science/machine learning in Python is desirable
- Basic data processing in Excel
- Some knowledge on probability is advisable

Description

**Do you want to build complex deep learning models in Keras? Do you want to use neural networks for classifying images, predicting prices, and classifying samples in several categories?**

Keras is the most powerful library for building neural networks models in Python. In this course we review the central techniques in Keras, with many real life examples. We focus on the practical computational implementations, and we avoid using any math.

The student is required to be familiar with Python, and machine learning; Some general knowledge on statistics and probability is recommended, but not strictly necessary.

Among the many examples presented here, we use neural networks to tag images belonging to the River Thames, or the street; to classify edible and poisonous mushrooms, to predict the sales of several video games for multiple regions, to identify bolts and nuts in images, etc.

We use most of our examples on Windows, but we show how to set up an AWS machine, and run our examples there. In terms of the course curriculum, we cover most of what Keras can actually do: such as the Sequential model, the model API, Convolutional neural nets, LSTM nets, etc. We also show how to actually bypass Keras, and build the models directly in Theano/Tensorflow syntax (although this is quite complex!)

After taking this course, you should feel comfortable building neural nets for time sequences, images classification, pure classification and/or regression. All the lectures here can be downloaded and come with the corresponding material.

Who is the target audience?

- Students beginning with machine learning but who already are comfortable with Python
- Business analytics professionals aiming to expand their toolkit of analytical techniques

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