TensorFlow 2.0 Beginner Tutorials

Dive into a comprehensive 7-hour tutorial series on TensorFlow 2.0 for beginners, designed to build a strong foundation for creating machine learning projects. Learn installation and setup, tensor basics, neural networks, convolutional neural networks, regularization techniques, RNNs, GRUs, LSTMs, and advanced topics like model subclassing, custom layers, transfer learning, and data augmentation. Explore practical implementations using TensorFlow, guided by concise explanations and references to theoretical concepts. Progress through hands-on examples, including custom datasets, callbacks, and training loops, culminating in a beginner project on skin cancer classification. Master TensorBoard for visualization and gain the skills to develop your own machine learning applications.

TensorFlow Tutorial 1 - Installation and Setup Deep Learning Environment (Anaconda and PyCharm).
TensorFlow Tutorial 2 - Tensor Basics.
TensorFlow Tutorial 3 - Neural Networks with Sequential and Functional API.
TensorFlow Tutorial 4 - Convolutional Neural Networks with Sequential and Functional API.
TensorFlow Tutorial 5 - Adding Regularization with L2 and Dropout.
TensorFlow Tutorial 6 - RNNs, GRUs, LSTMs and Bidirectionality.
TensorFlow Tutorial 7 - More in Depth Example on Functional API.
TensorFlow Tutorial 8 - Model Subclassing with Keras.
TensorFlow Tutorial 9 - Custom Layers.
TensorFlow Tutorial 10 - Saving and Loading Models.
TensorFlow Tutorial 11 - Transfer Learning, Fine Tuning and TensorFlow Hub.
TensorFlow Tutorial 12 - TensorFlow Datasets.
TensorFlow Tutorial 13 - Data Augmentation.
TensorFlow Tutorial 14 - Callbacks with Keras and Writing Custom Callbacks.
TensorFlow Tutorial 15 - Customizing Model.Fit.
TensorFlow Tutorial 16 - Custom Training Loops.
TensorFlow Tutorial 17 - Complete TensorBoard Guide.
TensorFlow Tutorial 18 - Custom Dataset for Images.
TensorFlow Tutorial 19 - Custom Dataset for Text with TextLineDataset.
TensorFlow Tutorial 20 - Classifying Skin Cancer [BEGINNER PROJECT EXAMPLE].