Learn TensorFlow and Deep Learning Fundamentals with Python - Code-First Introduction Part 2/2
Dive into the second part of an extensive video series on TensorFlow and deep learning fundamentals using Python. Continue your journey with hands-on coding exercises in Google Colab, exploring non-linear functions, neural networks with non-linear activation functions, and multi-layer models. Learn to optimize your models by tweaking learning rates, using callbacks, and analyzing loss curves. Delve into multi-class classification, preparing and visualizing data, building and improving models, and evaluating their performance. Master essential techniques like creating confusion matrices, normalizing data, and interpreting model patterns. Access additional resources, including the full course, GitHub repository for code and materials, and community discussions. Perfect for those who have completed part one and are ready to deepen their understanding of TensorFlow and deep learning concepts.