Overview
TensorFlow for Deep Learning Training Course is designed to provide participants with a comprehensive understanding of TensorFlow and its applications in deep learning. This course covers essential concepts, hands-on exercises, and real-world examples to enable participants to build and deploy deep learning models using TensorFlow. Participants will gain practical skills in implementing various neural networks, optimizing models, and leveraging TensorFlow’s advanced features for deep learning tasks.
Objectives
- Understand the fundamentals of deep learning and its applications.
- Implement neural networks and deep learning models using TensorFlow.
- Train and evaluate deep learning models for various tasks.
- Optimize and fine-tune deep learning models for improved performance.
- Utilize TensorFlow’s advanced features and APIs for custom model development.
- Deploy deep learning models and integrate them into real-world applications.
- Gain hands-on experience through practical exercises and projects.
Audience
- Data scientists and machine learning practitioners interested in deep learning with TensorFlow.
- Software engineers seeking to develop expertise in building and deploying deep learning models.
- AI enthusiasts and researchers looking to apply TensorFlow for their projects.
- Professionals seeking to enhance their skills in deep learning frameworks and techniques.
Pre- requisites
- Basic knowledge of machine learning and deep learning concepts.
- Familiarity with Python programming language.
- Understanding of linear algebra and calculus is beneficial.
Duration: 5 days – 35 hrs
Course Content
Day 1: Introduction to TensorFlow and Deep Learning Fundamentals
- Overview of TensorFlow and its features
- Introduction to deep learning and neural networks
- TensorFlow installation and environment setup
- TensorFlow basics: tensors, operations, and variables
Day 2: Building and Training Neural Networks with TensorFlow
- TensorFlow Keras API for building and training models
- Multilayer perceptron (MLP) and activation functions
- Convolutional neural networks (CNNs) for computer vision tasks
- Recurrent neural networks (RNNs) for sequential data
Day 3: Advanced Deep Learning Techniques with TensorFlow
- Transfer learning and fine-tuning pre-trained models
- Autoencoders and generative models
- Reinforcement learning with TensorFlow
- Natural language processing (NLP) with TensorFlow
Day 4: Optimizing Deep Learning Models with TensorFlow
- Regularization techniques for improving model performance
- Hyperparameter tuning and model evaluation
- Model deployment and serving with TensorFlow Serving
- TensorFlow Extended (TFX) for production pipelines
Day 5: Real-world Projects and Advanced Topics
- Real-world project implementation using TensorFlow
- Advanced topics in TensorFlow: distributed training, GPU acceleration
- TensorFlow for computer vision applications
- Ethical considerations in deep learning and AI
