Workshop: Implementing AI - Deep Learning using TensorFlow and Keras

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.

Topics

  • 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
  • Common Types of Neural Networks
    • Convolutional Neural Network (CNN)
    • Recurrent Neural Network (RNN)
  • What is TensorFlow?
    • What is a Tensor?
    • Basic TensorFlow Operations
    • Graph and Session
    • Mathematical OperationsMatrices
    • Variables and Constants
    • Placeholders
    • Visualizing your graph using TensorBoard
    • Building a Perceptron using TensorFlow
    • Using Keras with TensorFlow
    • Image Classifications
    • Text 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