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.
Learn about Convolutional Neural Networks (CNNs), their basic architecture, and how they are used in image recognition and other real-world applications.
Learn essential data preprocessing techniques such as normalization, standardization, handling missing data, and feature engineering to prepare your data for machine learning models.
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.
Gain a comprehensive understanding of backpropagation, the key algorithm behind training neural networks, with an in-depth look at how it works, its challenges, and optimization techniques.