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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 director of AI developer experience at Oracle, where he leads developer relations for the Oracle AI Database and pioneers the discipline of memory engineering, a term he coined to describe the structured design of agent memory systems. He runs the #100DaysOfAgentMemory LinkedIn series, which has reached tens of thousands of AI practitioners.

    Richmond has published over 200 technical articles with more than one million views across Medium, NVIDIA’s technical blog, and other industry publications, covering topics from agent memory systems to production LLM application development. He has spoken at the AI Engineer World’s Fair, DeveloperWeek, Confluence, and Monki Gras. Previously, he served as an AI/ML developer advocate at MongoDB, where he built MemoRizz, an open source Python agent memory library, and codeveloped a DeepLearning.AI course on prompt compression and query optimization. Richmond holds a master’s degree in computer vision, machine learning, and robotics from the University of Surrey.

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Skills covered

  • Computer Vision
  • TensorFlow
  • Keras
  • Deep Learning