Keras Tutorials

Keras Tutorials Roadmap

Section 1: Introduction to Keras

  • What is Keras?
    • High-level neural networks API written in Python.
    • Runs on top of TensorFlow, CNTK, or Theano.
    • Designed for fast experimentation and ease of use.
  • Installing Keras:
    • Installing TensorFlow (which includes Keras): pip install tensorflow.
    • Verifying the installation.
  • Your First Neural Network in Keras:
    • Loading a dataset (e.g., MNIST).
    • Building a simple Sequential model.
    • Compiling, training, and evaluating the model.

Section 2: Core Concepts

  • Keras Models:
    • Sequential API.
    • Functional API.
    • Model subclassing.
  • Layers:
    • Dense, Dropout, Conv2D, MaxPooling2D, Flatten, etc.
    • Custom layers with subclassing.
  • Activations:
    • ReLU, Sigmoid, Tanh, Softmax.
    • Custom activation functions.
  • Loss Functions:
    • Mean Squared Error, Binary Crossentropy, Categorical Crossentropy, etc.
  • Optimizers:
    • SGD, Adam, RMSprop, etc.
    • Adjusting learning rates and other hyperparameters.

Section 3: Training and Evaluation

  • Compiling Models:
    • Choosing loss, optimizer, and metrics.
  • Training with fit():
    • Epochs, batch size, and validation.
    • Callbacks (ModelCheckpoint, EarlyStopping, TensorBoard).
  • Evaluating and Predicting:
    • evaluate() and predict() methods.

Section 4: Working with Data

  • Loading and Preprocessing Data:
    • Using Keras datasets (MNIST, CIFAR-10, IMDB).
    • Normalization, reshaping, one-hot encoding.
  • Using ImageDataGenerator and tf.data:
    • Data augmentation.
    • Building input pipelines with tf.data.Dataset.

Section 5: Model Saving and Deployment

  • Saving Models:
    • Saving entire model or weights only.
    • Formats: HDF5, SavedModel.
  • Loading Models:
    • load_model() and load_weights().
  • Exporting for Deployment:
    • TF Lite, TensorFlow.js, TensorFlow Serving.

Section 6: Advanced Topics

  • Custom Training Loops:
    • Using GradientTape for fine control.
  • Callbacks and Monitoring:
    • Early stopping, learning rate scheduling, TensorBoard logging.
  • Transfer Learning:
    • Using pretrained models (MobileNet, VGG, ResNet).
    • Fine-tuning vs. feature extraction.
  • Custom Layers and Models:
    • Subclassing Layer and Model.

Section 7: Real-World Projects

  • Image classification (e.g., Cats vs. Dogs).
  • Text sentiment analysis (IMDB, Twitter).
  • Time series forecasting.
  • Object detection and segmentation.

Section 8: Keras Ecosystem & Resources

  • Useful Keras Extensions:
    • keras-tuner for hyperparameter search.
    • TF Hub, TF Model Garden.
  • Learning Resources:
    • Keras documentation.
    • TensorFlow tutorials and guides.
    • Community blogs and GitHub projects.