TensorFlow and Keras Basics

Aim

Program Objectives

Program Structure

1. Module 1: Introduction to TensorFlow and Keras

   Section 1.1: Introduction to TensorFlow

   • Subsection 1.1.1: What is TensorFlow?
     • Overview of TensorFlow as a deep learning framework.
     • Benefits of using TensorFlow: Flexibility, scalability, and performance.
   • Subsection 1.1.2: Installing TensorFlow
     • Installing TensorFlow on various platforms (Windows, macOS, Linux).
     • Setting up TensorFlow in Jupyter notebooks and Google Colab.
   • Subsection 1.1.3: TensorFlow Architecture
     • Understanding tensors and operations in TensorFlow.
     • Basics of TensorFlow computational graph and sessions.

   Section 1.2: Introduction to Keras

   • Subsection 1.2.1: What is Keras?
     • Keras as a high-level API for building deep learning models.
     • Differences between Keras and TensorFlow.
     • Why Keras simplifies TensorFlow.
   • Subsection 1.2.2: Installing Keras
     • Installing Keras and TensorFlow together.
     • Setting up Keras in a local environment or cloud platform.
   • Subsection 1.2.3: Keras Workflow
     • Understanding Keras’ simple and user-friendly interface.
     • Overview of Keras models: Sequential and Functional API.

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   Module 2: Core Concepts of TensorFlow and Keras

   Section 2.1: Tensors in TensorFlow

   • Subsection 2.1.1: Understanding Tensors
     • What is a tensor? Types of tensors in TensorFlow.
     • Operations on tensors: addition, multiplication, and reshaping.
     • TensorFlow tensor objects vs. NumPy arrays.
   • Subsection 2.1.2: Basic TensorFlow Operations
     • Creating tensors in TensorFlow: tf.constant(), tf.Variable().
     • Performing operations on tensors: reshaping, slicing, and broadcasting.

   Section 2.2: Building a Basic Neural Network with Keras

   • Subsection 2.2.1: Keras Sequential Model
     • Overview of the Sequential model.
     • Creating a simple neural network with one hidden layer.
     • Adding activation functions (ReLU, sigmoid, etc.) to layers.
   • Subsection 2.2.2: Compiling the Model
     • Understanding the model compilation step: loss functions, optimizers, and metrics.
     • Commonly used optimizers: SGD, Adam, RMSprop.
     • Common loss functions: Mean Squared Error (MSE), Binary Cross-Entropy, Categorical Cross-Entropy.
   • Subsection 2.2.3: Model Training and Evaluation
     • Training the model using model.fit().
     • Evaluating model performance with model.evaluate().
     • Understanding training metrics (accuracy, loss).

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   Module 3: Advanced Techniques with TensorFlow and Keras

   Section 3.1: Model Overfitting and Regularization

   • Subsection 3.1.1: Overfitting in Neural Networks
     • What is overfitting, and how it impacts model performance.
     • Symptoms of overfitting and underfitting in machine learning models.
   • Subsection 3.1.2: Techniques to Prevent Overfitting
     • Regularization methods: L2 Regularization, L1 Regularization.
     • Dropout layer: Understanding the concept and how to use it in Keras.

   Section 3.2: Convolutional Neural Networks (CNNs) in Keras

   • Subsection 3.2.1: Introduction to CNNs
     • Understanding Convolutional layers, pooling layers, and fully connected layers.
     • Architecture of a simple CNN for image classification.
   • Subsection 3.2.2: Building a CNN with Keras
     • Defining convolutional layers with Conv2D.
     • Using MaxPooling2D for spatial reduction.
     • Adding dropout layers and dense layers for classification.
   • Subsection 3.2.3: Model Training and Evaluation
     • Training CNN models on image datasets (e.g., CIFAR-10, MNIST).
     • Evaluating CNN models on accuracy and loss metrics.

   Section 3.3: Recurrent Neural Networks (RNNs) in Keras

   • Subsection 3.3.1: Introduction to RNNs
     • The architecture of Recurrent Neural Networks.
     • Understanding how RNNs are suited for sequential data (e.g., time series, text).
   • Subsection 3.3.2: Building RNNs with Keras
     • Using SimpleRNN and LSTM layers for sequential data.
     • Understanding the difference between RNN, LSTM, and GRU.
   • Subsection 3.3.3: Training and Evaluating RNN Models
     • Training RNN models on sequential data (e.g., time-series forecasting, text generation).
     • Evaluating model performance using metrics like accuracy and loss.

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   Module 4: Model Evaluation and Fine-Tuning

   Section 4.1: Model Evaluation and Hyperparameter Tuning

   • Subsection 4.1.1: Cross-validation Techniques
     • What is cross-validation?
     • Implementing K-fold cross-validation in TensorFlow.
     • Evaluating model stability using cross-validation.
   • Subsection 4.1.2: Hyperparameter Tuning
     • Importance of tuning hyperparameters for model performance.
     • Manual vs. Automated hyperparameter tuning.
     • Grid search and random search using Keras and TensorFlow.

   Section 4.2: Transfer Learning with Keras

   • Subsection 4.2.1: What is Transfer Learning?
     • Overview of transfer learning and its benefits for deep learning tasks.
     • Using pre-trained models (e.g., VGG, ResNet, Inception) for new tasks.
   • Subsection 4.2.2: Implementing Transfer Learning in Keras
     • Loading pre-trained models using tf.keras.applications.
     • Fine-tuning the pre-trained model for specific tasks (e.g., image classification).

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