Deep Learning with Python

What is Deep Learning?

• Introduction to Deep Learning and examples of real-world problems that it solves.

Introduction to Keras

• Keras is built on top of Tensorflow; and makes it easier to use. In this section, you'll learn how to use Keras to solve a range of problems.

Introduction to Tensorflow

• In this section, we'll build deep-learning models that use Tensorflow, and consider the pros and cons of using Tensorflow directly versus using Keras.

Introduction to PyTorch

• In this section, we'll build deep-learning models that use PyTorch

Convolutional Neural Networks

• CNNs are commonly used to analyse visual imagery.

Recurrent Neural Networks

• We'll use a RNN for handwriting recognition.

Auto Encoders

• An autoencoder can be trained to ignore signal noise. Common uses include image denoising, image compression, and, in some cases, even generation of image data.

Reinforcement Learning

• Reinforcement learning is a machine learning training method based on rewarding desired behaviours and/or punishing undesired ones.

Transformers

• The "T" in ChatGPT. More and more deep-learning projects are using Transformers these days, and they're being successfully applied to a wide range of problems.

The Future of ML and AI

• Discussion surrounding the current "state of the art" as well as regulatory and ethical considerations when building solutions that use deep learning.