TensorFlow-for-Deep-Learning

TensorFlow is a Google-created software library that's used for deep learning and machine learning. It's a popular choice for deep learning because of its ability to combine various machine learning and deep learning models and algorithms into a single interface. TensorFlow also offers a high-level API, which means that complex coding isn't required to prepare a neural network.

00_tensorflow_fundamentals (What we're going to cover broadly):

• TensorFlow basics and fundamentals
• Preprocessing data (getting it into tensors)
• Building and using pretrained deep learning models
• Fitting a model to the data (learning patterns)
• Making predictions with a model (using patterns)
• Evaluating model predictions
• Saving and loading models
• Using a trained model to make predictions on our custom data

01_neural_network_regression_in_tensorflow (What we're going to cover broadly):

• Architecture of a neural network regression model
• Input shapes and output shapes of a regression model (features and labels)
• Creating custom data to view and fit
• Steps in modelling : Creating a model, compiling it, fitting it and evaluating the model
• Different evaluation methods
• Saving and loading models

02_neural_network_classification_in_tensorflow (What we're going to cover broadly):

• Architecture of a neural network classification model
• Input shapes and output shapes of a classification model (features and labels)
• Creating custom data to view and fit
• Steps in modelling : Creating a model, compiling it, fitting it and evaluating the model
• Different classification evaluation methods
• Saving and loading models

03_introduction_to_computer_vision_with_tensorflow (What we're going to cover broadly):

• Getting a dataset to work with
• Architecture of a convolutional neural network (CNN) with TensorFlow
• An end-to-end binary image classification problem
• Steps in modelling with CNNs : Creating a CNN, compiling it, fitting it and evaluating the model
• An end-to-end multi-class image classification problem
• Making predictions on our own custom images

04_transfer_learning_in_tensorflow_part_1_feature_extraction (What we're going to cover broadly):

• Introducing transfer learning with TensorFlow
• Using a small dataset to experiment faster (10% of training samples)
• Building a transfer learning feature extraction model using TensorFlow Hub
• Using TensorBoard to track modelling experiments and results

05_transfer_learning_in_tensorflow_part_2_fine_tuning (What we're going to cover broadly):

• Introducing fine-tuning transfer learning with TensorFlow
• Introducing the Keras Functional API to build models
• Using a smaller dataset to experiment faster
• Data augmentation (making our training set more diverse by adding not other samples , yet modifying present data)
• Running a series of experiments on our food vision data
• Introducing the ModelCheckpoint callback to save intermediate training results

06_transfer_learning_in_tensorflow_part_3_scaling_up (What we're going to cover broadly):

• Downloading and preparing 10% of all Food101 classes (7500+ training images)
• Training a transfer learning feature extraction model
• Fine-tuning our feature extraction model to beat the original Food101 paper with only 10% of the data
• Evaluating Food Vision mini's predictions
• Finding most wrong predictions (on the test dataset)
• Making predictions with Food Vision mini on our own custom images

07_introduction_to_NLP_in_tensorflow (What we're going to cover broadly):

• Downloading and preparing a text dataset
• How to prepare a text data for modelling (tokenization and embedding)
• Setting up multiple modelling experiments with recurrent neural networks (RNNs)
• Building a text feature extraction model using TensorFlow Hub
• Finding the most wrong prediction examples
• Using a model we've built to make predictions on text from the wild