Keras

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keras
Why Keras is Important
in Data Science and AI

Keras is a high-level deep learning API that simplifies the process of building and training neural networks.

It runs on top of TensorFlow, providing both flexibility for research and simplicity for development. Keras offers a clean, modular structure with easy-to-use APIs—ideal for beginners and professionals alike.

  • It allows for rapid prototyping of deep learning models using just a few lines of code.

  • Keras abstracts complex backend processes like tensor operations and gradient computation.

  • It supports various model architectures (Sequential, Functional, Subclassing), making it versatile for experimentation.

  • Keras integrates seamlessly with tools like TensorBoard, TFLite, and TF Serving for end-to-end workflows.

  • It supports advanced features such as custom layers, callbacks, and transfer learning.

  • The library is widely used in industry and academia, including in real-world AI applications.

  • Mastery of Keras builds a strong foundation for creating, tuning, and deploying modern deep learning systems.

keras
01

Module 1: Introduction to Keras and Deep Learning

  • What is Keras? Overview and history

  • Backend engines: TensorFlow, CNTK, Theano (legacy)

  • Installing TensorFlow and Keras

  • Why use Keras for deep learning?

02

Module 2: Keras Architecture Overview

  1. Model = Layers + Loss + Optimizer

  2. Layers API

  3. Functional vs Sequential vs Model Subclassing

  4. Keras backend tensor operations

03

Module 3: The Sequential API

  1. Creating a model using Sequential()

  2. Adding layers (Dense, Dropout, Activation)

  3. Input and output shapes

  4. Model compilation, training, evaluation

04

Module 4: Functional API

  1. Why use Functional API?

  2. Building complex models with multiple inputs/outputs

  3. Shared layers and non-linear architecture

  4. Example: Siamese Network, Autoencoder

05

Module 5: Model Compilation and Training

  1. Choosing loss functions (categorical_crossentropy, mse, etc.)

  2. Choosing optimizers (SGD, Adam, RMSprop)

  3. Evaluation metrics: accuracy, mae, etc.

  4. Training with fit(), validating with validation_split or validation_data

  5. Overfitting and underfitting

06

Module 6: Data Handling in Keras

  1. Using NumPy arrays and Pandas DataFrames

  2. Working with tf.data.Dataset

  3. Keras data generators

  4. Data augmentation with ImageDataGenerator

  5. Real-time data preprocessing

07

Module 7: Callbacks and Model Monitoring

  1. Using EarlyStopping, ModelCheckpoint, ReduceLROnPlateau

  2. TensorBoard integration

  3. Saving and loading weights

  4. Logging training history for analysis

08

Module 8: Building Deep Neural Networks

  1. Understanding activation functions
  2. Batch normalization and dropout
  3. Building deep MLP for classification
  4. Custom initialization and regularization
09

Module 9: Computer Vision with Keras

  1. Image classification with CNN

  2. Layers: Conv2D, MaxPooling2D, Flatten

  3. Transfer learning using pre-trained models: VGG16, ResNet, Inception

  4. Fine-tuning strategies

  5. Image augmentation best practices

10

Module 10: Natural Language Processing with Keras

  • Tokenization and padding with Tokenizer

  • Word embeddings: Embedding layer, GloVe

  • Text classification with RNN, LSTM, and GRU

  • Sequence-to-sequence models

  • Sentiment analysis with IMDB dataset

11

Module 11: Advanced Architectures

  1. Building Autoencoders for feature learning

  2. Building Generative models (GANs overview)

  3. Building attention layers

  4. Creating custom layers and models using subclassing

12

Module 12: Hyperparameter Tuning

  1. Using Keras Tuner for automated model search

  2. Grid search vs random search vs Bayesian optimization

  3. Best practices in tuning: batch size, layers, dropout, learning rate

13

Module 13: Model Deployment

  • Saving models (.h5, SavedModel format)

  • Exporting to TensorFlow Lite or TensorFlow.js

  • Deploying with Flask or FastAPI

  • Cloud deployment: Colab → Heroku → GCP/AWS

14

Module 14: Keras Ecosystem Tools

  1. TensorFlow Hub integration

  2. KerasCV and KerasNLP (for image/text tasks)

  3. Pretrained models and model repositories

  4. Compatibility with Hugging Face Transformers

15

Module 15: Capstone Projects

  1. Image classifier (CIFAR-10 or custom dataset)

  2. Text sentiment classifier (IMDB or Twitter)

  3. Transfer learning on medical images

  4. Deploy a deep learning model as a web app

  5. Build a simple chatbot or summarizer using LSTM

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