Workshop: Implementing AI - Deep Learning using TensorFlow and Keras - Day 2/2

Day 2/2


Deep Learning is a branch of machine learning that utilizes neural networks. But how does a neural network work, and how does deep learning solve machine learning problems?

In this workshop, you will learn how to get started with deep learning using one of the most popular frameworks for implementing deep learning – TensorFlow. You will also use another API – Keras, which is built on top of TensorFlow, to make deep learning more user-friendly and easier.

• Introduction to Neural Networks
• Deep Learning and Neural Networks
• Perceptron and Neural Networks
• Layers, Weights and Biases
• Activation Functions
• Softmax
• ReLu
• Leaky ReLu
• Back Propagation
• Loss Functions
• Binary cross entrophy
• Categorical cross entrophy
• Mean-squared error
• Optimizers - Gradient Descent, RMSprop, Adam
• Evaluating Performance
• Convolutional Neural Network (CNN)
• Using Keras with TensorFlow
• Image Classifications
• Custom Image Recognizer
• Transfer Learning
• What is Transfer Learning?
• Using pre-trained models
• Fine-tuning pre-trained models

For Who:
You will benefit from this course if you are:

  • A developer who wants to learn deep learning and build machine learning models
  • A student planning to major in machine learning Prerequisites

Prerequisites

  • Basic programming experience
  • Understanding of basic object-oriented programming concepts

Hardware:

  • Mac / Windows laptop

Software:

  • Anaconda (Python 3.7)
  • Visual Studio Code