Convolutional neural networks for image recognition in Keras and TensorFlow
What you'll learn, and how you can apply it
- Understand CNNs and how to implement them in Python, TensorFlow, and Keras
- Learn to develop CNNs, use pretrained networks like VGG16, and use them in art or practical problems like object detection in real time with an image or with a webcam
Who is this presentation for?
- You're a software engineer or programmer with a background in Python who wants to better understand CNNs and how to use them in your own projects.
- Familiarity with Python, basic mathematics, modeling, and statistics
Hardware and/or installation requirements:
- A laptop
- Complete the instructions in the README file on the GitHub repository
NOTE: You will receive an introduction to the newly released TensorFlow 2.0 in this training.
- Brief introduction to neural networks, loss functions, and optimizers
- Introduction to CNN, convolutions, and filters
- Introduction to Google Colab, TensorFlow, and Keras
- Keras layers and sequential models
- Development of first CNN with images (MNIST, black and white images of handwritten digits)
- TensorFlow 2.0 and eager execution
- Hardware acceleration with Keras and Google Colab
- CNN with MNIST with TF 2.0
- Development of a more complex CNN with the CIFAR-10 dataset (color images of several classes)
- Keras callback classes
- Keras functional APIs
- Saving and loading models and weights
- Example of saving and loading of weights and models with MNIST
- Transfer learning
- Using pretrained networks in Keras
- A practical application of transfer learning
- Development of a CNN with real images with transfer learning
- Neural style transfer
- An introduction
- Apply NST to your images
- tf.data.Dataset for data management
About your instructor
Umberto Michelucci is a cofounder and the chief AI scientist at TOELT LLC, a company aiming to develop new and modern teaching, coaching, and research methods for AI to make AI technologies and research accessible to every company and everyone. He’s an expert in numerical simulation, statistics, data science, and machine learning. In addition to several years of research experience at the George Washington University (US) and the University of Augsburg (DE), he has 15 years of practical experience in the fields of data warehouse, data science, and machine learning. His last book, Applied Deep Learning—A Case-Based Approach to Understanding Deep Neural Networks, was published by Springer in 2018, and he’s working on a new book, Convolutional and Recurrent Neural Networks Theory and Applications. He’s very active in research in the field of artificial intelligence. He publishes his research results regularly in leading journals and gives regular talks at international conferences. Umberto studied physics and mathematics. Sharing is caring—for that, he is a lecturer at the ZHAW University of Applied Sciences for deep learning and neural networks theory and applications and at the HWZ University of Applied Science for big data analysis and statistics. At Helsana Versicherung AG, he’s also responsible for research and collaborations with universities in the area of AI.
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