/AI101-Tensorflow_Certification_Playground

Primary LanguagePython

TF Cert trials

Weights and Biases project of this learning

https://wandb.ai/supersecurehuman/Tensorflow%20Certification%20practice/table

Covered the following from Handbook

Basics Part

  • Use TensorFlow 2.x.
  • Save and Load Models
  • 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.

CNN Part

  • 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.

NLP Part

  • 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)

Time Series

  • Train, tune and use time series, sequence and prediction models.
  • Train models to predict values for both uni-variate and multivariate time series.
  • Prepare data for time series learning.
  • Understand Mean Absolute 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.