Introduction to Deep Learning


Training

Introduction to Deep Learning

1. Introduction

Exegetic Analytics is a Data Science consultancy specialising in data acquisition and augmentation, data preparation, predictive analytics and machine learning. Our services are used by a range of industries from Education to Security, Food Delivery to Politics. Our consultants are based in Durban and Cape Town and we engage with clients all over the world. Our products and services are used by a multitude of industries including Aerospace, Education, Finance, Food and Transport.

Exegetic Analytics also offers training, with experienced and knowledgeable facilitators. Our courses focus on practical applications, working through examples and exercises based on real-world datasets.

All of our training packages include access to:

  • our online development environment and
  • detailed course material which participants will have continued access to even once the training has concluded.

For more information about what we do, you can refer to our website.

These are some of the companies who have benefitted from our trainning:

Take a look at our full list of courses to see what other training we have on offer.

Contact Us

If this proposal is of interest to you or you would like to hear more about what we do you can get in touch on training@exegetic.biz or +27 (0)73 805 7439.

2. Course Description

Deep Learning is a vast and convoluted topic. It’s hard to know where to start. This workshop will help you take your first steps.

We’ll introduce you to the fundamental concepts behind Deep Learning and show you how to get started building models using Python and Keras. You’ll learn some of the underlying maths (in a very non-threatening way: a PhD in Mathematics will not be required!) and work through a number of practical examples.

You’ll walk away with an appreciation for what’s possible with Deep Learning and sufficient hands-on experience to start building your own models.

All material will be available as Jupyter Notebooks.

Details

Duration 2 days
Who should attend? This workshop is aimed at people with little or no prior experience with Deep Learning. If you’re already a Deep Learning ninja, then this is not for you!
Requirements Familiarity with programming in Python. A basic understanding of Machine Learning concepts will be helpful but certainly not essential.
Setup

You’ll need the following to get the most out of the workshop:

If you have not used Jupyter Notebooks before, then read through the following resources:

Take a look at the resources below for some useful background information:

Return to our list of courses.

Course Outline

3. Course Outline

  • Deep Learning Showcase
  • Working with Google Colab
    • Jupyter Notebooks
    • Kernels & Hardware
  • Machine Learning: An Overview
    • Terminology
    • Building a Model
    • Feature Engineering
    • Lab — Feature Engineering on Synthetic Data
  • Introduction to Neural Networks
    • Biological intuition
    • Neuron Model: Weights and bias
    • Activation functions
    • Loss functions
    • Chain rule and back-propagation
    • Optimisers
    • Batch & mini-batch training
    • Where neural networks fail: images
    • Lab — Binary Classifier on Synthetic Data
  • TensorFlow
    • What is TensorFlow?
    • What is a tensor?
    • Types of tensor: constant and variable
    • Tensor operations
      • Arithmetic
      • Gradients
    • Optimisation
      • Implementing Linear Regression
    • Lab — Implementing Logistic Regression
  • Keras
    • What is Keras?
    • Optimisers
    • Loss Functions
    • Layers
      • Dense
      • Dropout
    • Models
      • Sequential and Functional API
      • Building, Compiling and Fitting
    • Callbacks
    • Accessing layers
    • Saving & loading models
    • Lab — Binary Classifier on Synthetic Data
    • Lab — Banknote Authentication
    • Lab — Breast Cancer (CNN)
  • Convolutional Neural Network
    • Understanding Convolution
      • Stride and padding
    • More layers
      • Conv2D
      • MaxPooling2D and AveragePooling2D
      • Flatten
    • Data Augmentation
    • Lab — MNIST Fashion
    • Lab — Americal Sign Language
    • Lab — Santa versus Grinch
    • Lab — Chest X-rays
  • Transfer Learning
    • What is Transfer Learning?
    • Pre-trained networks
    • Transfer Learning
    • Fine-Tuning
    • Lab — Impala versus Springbok
    • Lab — Monkey Species
    • Lab — Tutti Frutti
  • Recurrent Neural Network (RNN)
    • Recursion, internal state and memory
    • Back-propagation through time
    • Long Short-Term Memory (LSTM)
    • Lab — Time Series prediction
    • Lab — Text prediction
    • Lab — Music generation

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Training Philosophy

Our training emphasises practical skills. So, although you'll be learning concepts and theory, you'll see how everything is applied in the real world. We will work through examples and exercises based on real datasets.

Requirements

All you'll need is a computer with a browser and a decent internet connection. We'll be using an online development environment. This means that you can focus on learning and not on solving technical problems.

Of course, we are happy to help you get your local environment set up too! You can start by following these instructions.

Package

The training package includes access to
  • our online development environment and
  • detailed course material (slides and scripts).

Return to our list of courses.