Just Tensorflow

Different Tensorflow exercises. Exploring the possibilities of python ML library and studying by practice.

SKILLS CHECKLIST:

1. Build and train neural network models using TensorFlow 2.x

• Use TensorFlow 2.x.
• Build, compile and train machine learning (ML) models using TensorFlow.
• Preprocess data to get it ready for use in a model.
• Use models to predict results.
• Build sequential models with multiple layers.
• Build and train models for binary classification.
• Build and train models for multi-class categorization.
• Plot loss and accuracy of a trained model.
• Identify strategies to prevent overfitting, including augmentation and dropout.
• Use pretrained models (transfer learning).
• Extract features from pre-trained models.
• Ensure that inputs to a model are in the correct shape.
• Ensure that you can match test data to the input shape of a neural network.
• Ensure you can match output data of a neural network to specified input shape for test data.
• Understand batch loading of data.
• Use callbacks to trigger the end of training cycles.
• Use datasets from different sources.
• Use datasets in different formats, including json and csv.
• Use datasets from tf.data.datasets.

2. Image classification

• Define Convolutional neural networks with Conv2D and pooling layers.
• Build and train models to process real-world image datasets.
• Understand how to use convolutions to improve your neural network.
• Use real-world images in different shapes and sizes
• Use image augmentation to prevent overfitting.
• Use ImageDataGenerator.
• Understand how ImageDataGenerator labels images based on the directory structure

3. Natural language processing (NLP)

• Build natural language processing systems using TensorFlow.
• Prepare text to use in TensorFlow models.
• Build models that identify the category of a piece of text using binary categorization
• Build models that identify the category of a piece of text using multi-class categorization
• Use word embeddings in your TensorFlow model.
• Use LSTMs in your model to classify text for either binary or multi-class categorization.
• Add RNN and GRU layers to your model.
• Use RNNS, LSTMs, GRUs and CNNs in models that work with text.
• Train LSTMs on existing text to generate text (such as songs and poetry)

4. Time series, sequences and predictions

• Train, tune and use time series, sequence and prediction models.
• Prepare data for time series learning.
• Understand Mean Average Error (MAE) and how it can be used to evaluate accuracy of sequence models.
• Use RNNs and CNNs for time series, sequence and forecasting models.
• Identify when to use trailing versus centred windows.
• Use TensorFlow for forecasting.
• Prepare features and labels.
• Identify and compensate for sequence bias.
• Adjust the learning rate dynamically in time series, sequence and prediction models.