Hands-on Deep Learning

Deep learning seems like a daunting topic with its complex maths and advanced model architectures. But once things are broken down into digestable pieces, there is nothing to be scared of!

This course will provide you with just enough theory to understand:

what deep learning is
its building blocks
architecture of latest algorithms

So that we can together:

apply the most common models on interesting problems
get experience building DL models
deploy the models to be used in real-life

Module 1: Introduction to Deep Learning

What is Deep Learning?
What is the difference of Machine Learning and Deep Learning?
What are common techniques of Deep Learning?
Applications of Deep Learning in the real world.

Module 2: Building Blocks of Deep Learning

Introduction to Neural Networks
Meet the parameters: Bias and Weight
Activation functions
Forwards propagation
Backwards propagation

Exercise 1: Build your own NN

Introduction to Tensorflow and Keras
Importing libraries
Datasets and how to use them
Setting up your first Neural Network

Module 3: Training Deep Learning Models

Setting up hyperparameters
Dealing with over/underfitting
Solutions to unstable gradients
Lowering computation time
Hyperparameter tuning
Evaluating the model

Exercise 2: Deep NNs at Work

Setting up a complex neural network with many layers
How to make key decisions in practice
How to identify and solve issues in your network

Module 4: Data issues

Types of data: structured and unstructured
Where to find quality datasets
Where to find big datasets
Dealing with a lack of data

Module 5: Convolutional Neural Networks

How do CNNs work?
Understanding CNNs layer by layer
Common CNN architectures

Exercise 3: Object Detection Using CNNs

Plan and implement an object detection model
Set up a web app to showcase the model's capabilities

Module 6: Recurrent Neural Networks

How do RNNs work?
Common RNN architectures and components
A closer look at LSTMs
How to build better models with Attention

Exercise 4: Text generation with RNNs

How to use pre-trained models as base
Implementing a text generating model

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Hands-on Deep Learning

Deep learning is accessible to you!

You don’t need to be a mathematics graduate to understand and apply deep learning.

Table of contents

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