• Software developers and engineers exploring AI model development
• Data scientists seeking hands-on experience with TensorFlow
• Machine learning enthusiasts aiming to build scalable deep learning solutions
• Researchers and students working with neural networks

• Working knowledge of Python programming
• Basic understanding of linear algebra and statistics
• Prior exposure to machine learning concepts is helpful

Course Outline:

Module 1: Introduction to AI and Neural Networks

• Overview of AI and Deep Learning concepts
  • Understanding perceptrons and multilayer neural networks
  • Activation functions, loss functions, and optimizers
  • Introduction to TensorFlow and Keras ecosystem
  • Hands-on: Setting up TensorFlow and building a basic neural network

Module 2: Model Architectures and Implementation

• Feedforward neural networks for classification
  • Convolutional Neural Networks (CNNs) for image recognition
  • Recurrent Neural Networks (RNNs) and LSTM for time series and sequence modeling
  • Overfitting, regularization, and dropout
  • Hands-on: Training and tuning CNN and RNN models

Module 3: Use Cases and Model Optimization

• Transfer learning and pre-trained models
  • Use case: Image classification using pre-trained CNNs
  • Use case: Text sentiment analysis using embedding layers
  • Model evaluation techniques: accuracy, precision, recall, F1
  • Hands-on: Hyperparameter tuning and saving/loading models
  • Deployment basics: exporting models for production use

Training material provided: Yes (Digital format)