TensorFlow and Keras Basics

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).

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.

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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