More TensorFlow tutorials

Starts with TensorFlow basics and advances to complex architectures like residual networks and variational autoencoders, as shown in the tutorial table from basics.py to variational_autoencoder.py.

Includes implementations of modern techniques such as batch normalization, leaky rectifiers, and variational autoencoders, covering topics beyond basic neural networks.

Provides both Python source code and Jupyter notebooks for each tutorial, offering flexibility for different learning preferences and environments, as noted in the directory structure.

Emphasizes practical implementation over theoretical exposition, allowing learners to build working models quickly, as stated in the philosophy section.