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Deep Learning for Everyone

Published by Pearson

Beginner to intermediate content levelBeginner to intermediate

Understanding deep learning and machine learning foundations for data science

Over the past few years we have seen a convergence of two large-scale trends: Big Data and Big Compute. The resulting combination of large amounts of data and abundant CPU (and GPU) cycles has brought to the forefront and highlighted the power of neural network techniques and approaches that were once thought to be too impractical.

Deep Learning, as this new wave of interest has come to be known, has made impressive and unprecedented progress on applications as diverse as Natural Language Processing, Machine Translation, Computer Vision, Robotics, etc. In this lecture, students will learn, in a hands-on way, the theoretical foundations and principal ideas underlying this burgeoning field. The code structure of the implementations provided is meant to closely resemble the way the state of the art deep learning libraries Keras is structured so that by the end of the course, students will be prepared to dive deeper into the deep learning applications of their choice.

What you’ll learn and how you can apply it

  • Linear regression
  • Logistic regression
  • Perceptrons and neurons
  • Back propagation
  • Auto-encoders
  • Training deep networks

This live event is for you because...

The typical audience member will be a data scientist who is interested in mastering the concepts and ideas behind neural networks and deep learning. The primary audience will be someone that has no previous experience in neural networks or machine learning and wants to take the first grounded steps, while the secondary target audience will be people with previous experience in using neural network libraries such as Keras or Tensorflow and who wish to get a greater understanding of what’s going on “under the hood.” Throughout the course, I will guide the audience through the implementation of a subset of the functionality of Keras so that the attendants will be prepared to move on to working directly with state of the art deep learning libraries in more advanced applications.

Prerequisites

Attendees should have experience with:

  • Basic Python
  • Numpy
  • Matplotlib
  • Jupyter

Course Set-up

  • Scientific Python distribution like Anaconda

Recommended Preparation

If you need to brush up on Python:

Recommended Follow-up

After you complete the Deep Learning from Scratch Live Online Training class, you may find the following resources helpful:

Stay connected with Bruno and up-to-date on the world of data, science, and machine learning at https://data4sci.com/newsletter.

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1 - Regression (30 minutes)

  • Understand the different types of Machine Learning
  • Define Supervised Learning and Regression
  • Visualize Linear Regression
  • Implement Gradient Descent
  • Use Linear Regression

Break (10 minutes)

Segment 2 - Classification (40 minutes)

  • Define Classification
  • Visualize Logistic Regression
  • Learning Procedure
  • Implement Logistic Regression
  • Understand Classification evaluation

Break (10 minutes)

Segment 3 – Data Preparation (20 minutes)

  • Visualize decision boundaries
  • Use Data Normalization
  • Understand Overfitting
  • Define the Bias Variance Tradeoff

Break (10 minutes)

Segment 4 - Neural Networks (30 min)

  • Understand how the brain works
  • Understand Perceptrons and Forward Propagation
  • Define Activation Functions
  • Understand Back Propagation

Break (10 minutes)

Segment 5 – Recognizing Numbers (50 minutes)

  • Understand the MNIST Dataset
  • Understand Data Preparation
  • Implement Back Propagation
  • Understand the effect of the Learning Rate
  • Generalize the Code

Break (10 minutes)

Segment 6 – Advanced network applications (50 minutes)

  • Understand unsupervised Learning
  • Implement an Auto-Encoder
  • Understand Recurrent Neural Networks
  • Understand Convolutional Neural Networks
  • Explore the Learning vs Memorization Tradeoff

Your Instructor

  • Bruno Gonçalves

    Bruno Gonçalves is an author, public speaker, corporate trainer, and consultant specializing in Generative AI, Blockchain Analytics, and Machine Learning. He has a diverse background that spans academia and industry, having previously served as a Data Science fellow at NYU's Center for Data Science while on leave from his tenured faculty position at Aix-Marseille Université. Bruno earned his PhD in the Physics of Complex Systems in 2008. He later focused his research on applying Data Science and Machine Learning to the large-scale analysis of online human behavior.

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Skill covered

Data Visualization