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Deep Learning Journey

Description: My collected learning notebooks for Deep Learning and AI in general.

My current interest: Tensorflow, Computer Vision, Machine Learning.

What I have learned for Tensorflow Certificate Exam (You can use this as a checklist for preparation):

Resources: Daniel Bourke

(1) Tensorflow Basics

  • Build and train neural network models using 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 layers to your model.

  • Use RNNS, LSTMs 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.

  • TensorFlow Certificate Candidate Handbook 3

  • 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.

Study references:

My next plans:

  • Do more in-depth studies in Deep Learning, starting with GANs and Photogrammetry