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Training a Convolutional Neural Network (CNN) on the MNIST Dataset

Learn how to train a Convolutional Neural Network (CNN) using TensorFlow on the MNIST dataset, a benchmark dataset for image classification.

Practical Guide to Data Augmentation in Deep Learning

Learn essential data augmentation techniques like flipping, rotation, and more to boost the performance and robustness of deep learning models in tasks like image recognition.

Introduction to Convolutional Neural Networks (CNNs)

Learn about Convolutional Neural Networks (CNNs), their basic architecture, and how they are used in image recognition and other real-world applications.

Data Preprocessing for Machine Learning

Learn essential data preprocessing techniques such as normalization, standardization, handling missing data, and feature engineering to prepare your data for machine learning models.

Using TensorFlow for Time Series Forecasting

Learn how to build time series forecasting models using TensorFlow, with a beginner-friendly introduction to Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and practical tips for improving predictions.