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Practical Introduction To The World Of Computer Vision And Deep Learning With TensorFlow & Keras

Published by O'Reilly Media, Inc.

Beginner to intermediate content levelBeginner to intermediate

Implementing neural networks and machine learning techniques

This live event utilizes Jupyter Notebook technology

Computer vision has existed since the 1960s and has since evolved into one of the prevalent fields in machine learning and artificial intelligence. Today, solving classical computer vision problems such as face detection, pose estimation, object detection, and semantic segmentation have become trivial thanks to the advancement of deep learning.

Join CV expert Richmond Alake to walk through the theory and practical components of computer vision and deep learning while also cultivating your Python knowledge. You’ll get explanations of widely used terminologies and learn to implement the techniques for and solutions to a typical CV image classification problem using Python, the TensorFlow machine learning library, and other standard data science packages, such as NumPy and pandas.

Hands-on learning with Jupyter notebooks

All exercises and labs are provided as Jupyter notebooks—interactive documents that combine live code, equations, visualizations, and narrative text. There's nothing to install or configure; just click a link and get started! And you can revisit them anytime after class ends to practice and refine your skills.

What you’ll learn and how you can apply it

By the end of this live online course, you’ll understand:

  • Computer vision and its application in practical environments
  • The fundamental components of deep learning as a machine learning field
  • How to leverage deep learning to solve computer vision tasks
  • The structural components of deep neural networks and convolutional neural networks

And you’ll be able to:

  • Implement solutions to common computer vision tasks
  • Use machine learning libraries to implement deep learning solutions
  • Build a deep neural network that classifies images
  • Build a convolutional neural network (AlexNet) that classifies images
  • Visualize the neural network training process using TensorBoard

This live event is for you because...

  • You’re a machine learning practitioner who wants to upskill.
  • You want to explore computer vision, one of the prevalent fields within machine learning and AI.
  • You work with Python and want to understand its use in machine learning and deep learning.
  • You want to build a solid foundation for more advanced studies.
  • You want to understand the technology behind modern AI tools and applications.

Prerequisites

  • Experience developing small programs with Python
  • A basic understanding of machine learning concepts
  • Familiarity with machine learning tools and libraries such as TensorFlow, NumPy, pandas, and Matplotlib
  • Familiarity with the Jupyter Notebook or JupyterLab

Recommended preparation:

Recommended follow-up:

Schedule

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

Introduction to computer vision and deep learning (60 minutes)

  • Presentation: What are neural networks?; deep neural networks; convolutional neural networks; CNN architectures
  • Group discussion: Have you ever implemented a neural network from scratch?; What computer vision application examples can you identify?
  • Q&A
  • Break

Solving computer vision problems with deep learning models: Part I (60 minutes)

  • Presentation: Keras sequential class and activation functions; viewing details of models; loading the Fashion-MNIST dataset; partitioning dataset into test/training/validation split; dataset visualization with Matplotlib; training a deep neural network (learning rate, learning rate schedules, loss functions, optimizers); viewing model training insights with TensorBoard; evaluating a trained model
  • Jupyter notebooks: Implement a Deep Neural Network; Classify an Image with a DNN
  • Q&A
  • Break

Solving computer vision problems with deep learning models: Part II (50 minutes)

  • Presentation: Introduction to AlexNet; layers (convolutional, batch normalization, max pooling, dropout); model implementation with Keras Sequential class API; loading the CIFAR-10 dataset; partitioning dataset into test/training/validation split; preprocessing datasets; training AlexNet (viewing model details and model training insights with TensorBoard); evaluating a trained model
  • Jupyter notebooks: Implement AlexNet (CNN) from Scratch; Classify an Image with AlexNet
  • Wrap-up and Q&A (10 minutes)

Your Instructor

  • Richmond Alake

    Richmond Alake is a highly experienced Machine Learning Architect and Engineer with over five years of expertise in the field. He specializes in Computer Vision and Deep Learning and has a proven track record of successfully developing and integrating deep learning models to solve a wide range of problems, such as motion detection, object detection, and pose estimation. Throughout his career, he has worked with a diverse range of clients, including large conglomerates, financial institutions, and small startups. In addition to his professional work, Richmond also serves as an AI advisor to a number of startups in the UK and the US.

    With a background in building websites and mobile applications, Richmond is a firm believer in using technology to solve everyday problems. He has extensive knowledge of Machine Learning and has written over 200 articles on the subject, gaining over a million views. He was recognized as one of Medium's top AI writers in 2020/2021 and has collaborated with companies such as O'Reilly, BuiltIn and Nvidia to develop effective educational and informative learning materials on AI.

    Currently, Richmond Alake is a Machine Learning Architect at Slalom Build UK. As the first hire of the machine learning practice in the UK division, he is responsible for helping organizations move from machine learning research to productionisation and assisting maturing organizations in promoting AI models into existing infrastructure to drive commercial and business value. His main role as an ML Architect is to assist organizations in developing and maintaining machine learning pipelines by implementing MLOps principles, techniques, and tooling. He is well-versed in Feature Stores and has conducted internal training for Data Engineers, Data Scientists, and ML Engineers.

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Skills covered

  • Computer Vision
  • TensorFlow
  • Keras
  • Deep Learning