Multiclass image classification with Tensorflow - simple example скачать в хорошем качестве

Multiclass image classification with Tensorflow - simple example

Welcome to our YouTube video on "Multiclass Image Classification with TensorFlow"! In this tutorial, we dive into the exciting world of image classification using the powerful TensorFlow framework. Are you interested in building intelligent systems that can automatically recognize and categorize images? Then this video is perfect for you! We'll guide you step-by-step through the process of creating a multiclass image classifier using TensorFlow, a popular deep learning library. We'll provide a comprehensive overview of the TensorFlow workflow for building an image classifier. You'll learn how to preprocess your image dataset, split it into training and testing sets, and transform the images into a format suitable for deep learning models. Once the preprocessing is complete, we'll delve into the architecture of our multiclass image classification model. We'll explore popular deep learning architectures like convolutional neural networks (CNNs) and discuss their strengths in tackling image classification tasks. You'll discover how to design and train your own CNN model using TensorFlow, and we'll provide tips and tricks to improve its accuracy and generalization. We'll also demonstrate techniques for visualizing the model's predictions and understanding its decision-making process. Throughout the video, we'll showcase practical examples and provide code snippets that you can follow along with. Don't forget to like the video, subscribe to our channel, and hit the notification bell to stay updated on future tutorials. Let's get started on building your image classification skills with TensorFlow today! Code from the video https://colab.research.google.com/dri... https://github.com/myredex/tensorflow... 00:00 - Introduction 00:13 - Data for image classification 00:32 - Plan of the video 00:58 - Download and unzip data 03:02 - Import helper functions 03:44 - Split uziped images to train, test, validation subfolders 05:08 - Create class names 06:40 - Visualize random image 10:23 - Data set preparation from folders 13:21 - Create model with Tensorflow 14:17 - Add layers 15:47 - Compile the model 16:10 - Fit the model 17:05 - Train the model 17:25 - Evaluate the model 17:51 - Plot learning curves 18:57 - Make a prediction