Building a Convolutional Neural Network (CNN) in Python – Step 3 Lecture No. 124
Welcome to Step 3 of our series on building a Convolutional Neural Network (CNN) in Python! In this episode, we will focus on training our CNN model and evaluating its performance. In this episode, we will cover: Training the CNN Model: Learn how to train your CNN model using your prepared dataset. We’ll walk through the process of fitting the model to your training data, including setting hyperparameters like batch size and the number of epochs. Monitoring Training Progress: Understand how to use metrics and visualizations to monitor the training process, such as accuracy and loss curves. We'll also cover techniques for avoiding overfitting, such as using validation data and implementing early stopping. Evaluating Model Performance: After training, we’ll evaluate the model’s performance on a test dataset. We’ll discuss how to interpret the results and assess the effectiveness of your CNN model using metrics like accuracy, precision, recall, and F1 score. Join us as we put our CNN model to the test, providing detailed instructions and practical tips for successful training and evaluation. By the end of this video, you'll be able to assess how well your model performs and make informed decisions for further improvements. Don’t forget to like, share, and subscribe to continue with the next steps in our CNN series, where we will explore advanced topics such as model optimization and fine-tuning. #CNN #MachineLearning #DeepLearning #Python #TensorFlow #Keras #AI
Welcome to Step 3 of our series on building a Convolutional Neural Network (CNN) in Python! In this episode, we will focus on training our CNN model and evaluating its performance. In this episode, we will cover: Training the CNN Model: Learn how to train your CNN model using your prepared dataset. We’ll walk through the process of fitting the model to your training data, including setting hyperparameters like batch size and the number of epochs. Monitoring Training Progress: Understand how to use metrics and visualizations to monitor the training process, such as accuracy and loss curves. We'll also cover techniques for avoiding overfitting, such as using validation data and implementing early stopping. Evaluating Model Performance: After training, we’ll evaluate the model’s performance on a test dataset. We’ll discuss how to interpret the results and assess the effectiveness of your CNN model using metrics like accuracy, precision, recall, and F1 score. Join us as we put our CNN model to the test, providing detailed instructions and practical tips for successful training and evaluation. By the end of this video, you'll be able to assess how well your model performs and make informed decisions for further improvements. Don’t forget to like, share, and subscribe to continue with the next steps in our CNN series, where we will explore advanced topics such as model optimization and fine-tuning. #CNN #MachineLearning #DeepLearning #Python #TensorFlow #Keras #AI