TensorFlow-Course

This is my code repository for the Course TensorFlow Developer Certificate in 2023: Zero to Mastery , published by Daniel Bourke.

0 — TensorFlow Fundamentals

Architecture of a regression model
Input shapes and output shapes
- X: features/data (inputs)
- y: labels (outputs)
Creating custom data to view and fit
Steps in modelling
- Creating a model
- Compiling a model - Defining a loss function - Setting up an optimizer - Creating evaluation metrics
- Fitting a model (getting it to find patterns in our data)
Evaluating a model
- Visualizng the model ("visualize, visualize, visualize")
- Looking at training curves
- Compare predictions to ground truth (using our evaluation metrics)
Saving a model (so we can use it later)
Loading a model

Architecture of a classification model
Input shapes and output shapes
- X: features/data (inputs)
- y: labels (outputs)
  - "What class do the inputs belong to?"
Creating custom data to view and fit
Steps in modelling for binary and mutliclass classification
- Creating a model
- Compiling a model
  - Defining a loss function
  - Setting up an optimizer
    - Finding the best learning rate
  - Creating evaluation metrics
- Fitting a model (getting it to find patterns in our data)
- Improving a model
The power of non-linearity
Evaluating classification models
- Visualizng the model ("visualize, visualize, visualize")
- Looking at training curves
- Compare predictions to ground truth (using our evaluation metrics)

Getting a dataset to work with
Architecture of a convolutional neural network
A quick end-to-end example (what we're working towards)
Steps in modelling for binary image classification with CNNs
- Becoming one with the data
- Preparing data for modelling
- Creating a CNN model (starting with a baseline)
- Fitting a model (getting it to find patterns in our data)
- Evaluating a model
- Improving a model
- Making a prediction with a trained model
Steps in modelling for multi-class image classification with CNNs
- Same as above (but this time with a different dataset)

Introduce transfer learning (a way to beat all of our old self-built models)
Using a smaller dataset to experiment faster (10% of training samples of 10 classes of food)
Build a transfer learning feature extraction model using TensorFlow Hub
Introduce the TensorBoard callback to track model training results
Compare model results using TensorBoard.

Introduce fine-tuning, a type of transfer learning to modify a pre-trained model to be more suited to your data
Using the Keras Functional API (a differnt way to build models in Keras)
Using a smaller dataset to experiment faster (e.g. 1-10% of training samples of 10 classes of food)
Data augmentation (how to make your training dataset more diverse without adding more data)
Running a series of modelling experiments on our Food Vision data
- Model 0: a transfer learning model using the Keras Functional API
- Model 1: a feature extraction transfer learning model on 1% of the data with data augmentation
- Model 2: a feature extraction transfer learning model on 10% of the data with data augmentation
- Model 3: a fine-tuned transfer learning model on 10% of the data
- Model 4: a fine-tuned transfer learning model on 100% of the data
Introduce the ModelCheckpoint callback to save intermediate training results
Compare model experiments results using TensorBoard

Downloading and preparing 10% of the Food101 data (10% of training data)
Training a feature extraction transfer learning model on 10% of the Food101 training data
Fine-tuning our feature extraction model
Saving and loaded our trained model
Evaluating the performance of our Food Vision model trained on 10% of the training data
- Finding our model's most wrong predictions
Making predictions with our Food Vision model on custom images of food

Using TensorFlow Datasets to download and explore data
Creating preprocessing function for our data
Batching & preparing datasets for modelling (making our datasets run fast)
Creating modelling callbacks
Setting up mixed precision training
Building a feature extraction model (see transfer learning part 1: feature extraction)
Fine-tuning the feature extraction model (see transfer learning part 2: fine-tuning)
Viewing training results on TensorBoard

Downloading a text dataset
Visualizing text data
Converting text into numbers using tokenization
Turning our tokenized text into an embedding
Modelling a text dataset
- Starting with a baseline (TF-IDF)
- Building several deep learning text models
  - Dense, LSTM, GRU, Conv1D, Transfer learning
Comparing the performance of each our models
Combining our models into an ensemble
Saving and loading a trained model
Find the most wrong predictions