COURSE

Deep Learning Fundamentals with Leonardo De Marchi

Topics covered at DEEP-LEARNING-LM-03
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Overview
Join Damjan Vujnovic for this Advanced JavaScript Workshop

Gain the tools and knowledge you need to begin developing your own Deep Learning projects in this introductory course with Leonardo De Marchi.

Take a look at Deep Learning concepts with Keras by analysing an image recognition project and learning to develop the model from start to finish. Examine the business needs of a project and design a solution, create a multi layer network and get an intro to some more sophisticated practices including implementing different types of networks for image recognition, using dropouts and random noise to improve results, selecting the proper architecture and using pre-trained models.




Online Learning at Skills Matter

This course will be offered virtually over 4 half-day sessions.

Our virtual courses offer the same expert-led, hands-on experience we've offered since 2013 — only now we’re making it accessible from the comfort of your own home (office).

You'll join Leonardo and participants from around the globe in a virtual classroom where you'll utilise a variety of collaboration tools(e.g. Zoom, Slack, and Notion.so) to get to grips with fundamentals of Deep Learning.

Learn how to:


Understand the theory behind neural networks

Work with Keras and Pytorch

Create a basic Deep Learning setup


Complete an image recognition task end to end

Debug and tune the network

Use some more advanced concepts and tricks of the trade


About the Author

Leonardo De Marchi

Leonardo De Marchi is Lead Data Scientist for Badoo, the World's largest dating website, and is a consultant for the European Union Commission, helping to develop a European strategy for Big Data. He holds a Master's Degree in Artificial Intelligence and has worked as a Data Scientist with the New York Knicks, and Manchester United FC, as well as large social networks including JustGiving.

Programme


Day 1

  • DL Basics
    • Introduction to Deep Learning
    • Key ML concepts and terminology
    • Examples in industry and research
    • Formalizing your own DL problem
  • Feedforward Neural Networks
    • Training neural networks, optimization methods, error back-propagation
    • Introduction to Keras and Pytorch
  • Lab 0: Getting Started with GPUs and cloud computing
    • Quickly set up a machine with Deep Learning and NVIDIA Docker
    • Set up additional libraries like keras-viz
    • Quick demo on how to use tensorboard
  • TensorFlow, Keras and Pytorch
    • Introduction
    • Overview
    • Pros and Cons
    • How to use the right tool
  • Lab 1: Getting Started with Keras
    • Basic concepts
    • Terminology
    • Exercise
  • Lab 2: Implement and train a feed-forward neural network in Keras
    • Tackling the problem of facial expression recognition
  • Convolutional Neural Networks (CNNs)
    • Understanding the convolutional architecture
    • Convolutional and pooling layers
    • Applications to image classification
  • Lab 3: Implementing CNN using Keras A
    • Extending a feed-forward network with convolutional and pooling layers
    • Using CNNs for image data

Day 2

  • Lab 3 continued: Implementing CNN using Keras B
    • Recap of the main concepts
    • Lab solutions
  • Recurrent Neural Networks
    • Understanding recurrent architectures
    • Elman, LSTM and GRU units
    • Bi-directional architectures
    • Combining RNNs with convolutional and feed-forward layers
    • Applications to speech
    • Biological sequences and information retrieval
  • Lab 4: Implementing RNNs using Keras
    • Implementing and training RNNs using LSTM units on a simple natural language processing task
  • Practical tricks of the trade
    • Using pre-trained networks
    • Transfer learning
    • Visual debugging of DNNs
  • Lab 5: Practical tricks of the trade
    • Practicing concepts of the previous theoretical session
  • Closing remarks and feedback
    • Introduction to Multi-Armed bandit and Reinforcement Learning
Audience

Audience

This course is intended for Data Scientists, Analysts and Developers who are interested in Deep Learning as a way to save time and resources. It will provide all basic information to get started straight away.

Prerequisites

Delegates should have basic Python knowledge. Machine Learning knowledge is advantageous, but not required.