Deep Learning with TensorFlow Training
Introduction to TensorFlow
Learn the fundamentals of TensorFlow, an open-source platform developed by Google for deep learning and machine learning. Understand TensorFlow's core features, architecture, and its role in building and deploying deep learning models.
Getting Started with TensorFlow
Explore how to set up and navigate TensorFlow. Learn about TensorFlow's environment, installation, and basic operations to get started with building and training models.
TensorFlow Basics and APIs
Study TensorFlow's core concepts and APIs. Learn about tensors, computational graphs, and how to use TensorFlow's high-level APIs, such as Keras, for building and training neural networks.
Building Neural Networks with TensorFlow
Learn how to build and train neural networks using TensorFlow. Explore how to create different types of layers, configure models, and perform optimization and training using TensorFlow's powerful tools.
Convolutional Neural Networks (CNNs) with TensorFlow
Dive into convolutional neural networks (CNNs) using TensorFlow. Understand how to implement CNNs for image recognition tasks, including convolutional layers, pooling, and feature extraction.
Recurrent Neural Networks (RNNs) with TensorFlow
Explore recurrent neural networks (RNNs) in TensorFlow for sequence data analysis. Learn about Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) and their applications in time series and natural language processing.
Generative Adversarial Networks (GANs) with TensorFlow
Study how to implement generative adversarial networks (GANs) using TensorFlow. Learn about the architecture of GANs, including the generator and discriminator, and their applications in generating synthetic data and images.
Transfer Learning with TensorFlow
Learn about transfer learning and how to apply pre-trained models in TensorFlow for specific tasks. Explore techniques for fine-tuning models to improve performance with new datasets.
Model Evaluation and Optimization
Discover how to evaluate and optimize deep learning models in TensorFlow. Learn about metrics, validation methods, and strategies for improving model accuracy and efficiency.
Deploying TensorFlow Models
Explore deployment options for TensorFlow models. Learn about TensorFlow Serving, TensorFlow Lite for mobile and edge devices, and how to integrate models into production environments.
Ethical Considerations in Deep Learning
Study the ethical considerations and challenges associated with deep learning. Understand issues related to fairness, transparency, and the responsible use of AI technologies in TensorFlow projects.
Case Studies and Practical Exercises
Engage in case studies and practical exercises to apply TensorFlow concepts. Work on real-world projects to build, train, and deploy deep learning models using TensorFlow.
Certification and Career Development
Prepare for TensorFlow certifications and advance your career in deep learning and AI. Get guidance on study resources, exam preparation, and career development strategies for roles involving TensorFlow.
Deep Learning with TensorFlow syllabus
Introduction to Deep Learning and TensorFlow
- Overview of Deep Learning
- Introduction to TensorFlow
- TensorFlow Architecture and Components
Foundations of Neural Networks
- Neural Network Basics
- Activation Functions
- Gradient Descent and Backpropagation
- Introduction to TensorFlow 2.x
Building and Training Neural Networks with TensorFlow
- TensorFlow Keras API
- Building Sequential and Functional Models
- Training and Evaluation
- Overfitting and Regularization
Convolutional Neural Networks (CNNs)
- Introduction to CNNs
- Building CNNs with TensorFlow
- Image Classification and Object Detection
- Transfer Learning
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
- Introduction to RNNs and LSTMs
- Sequence Prediction and Language Modeling
- Time Series Analysis
- Building RNNs and LSTMs with TensorFlow
Autoencoders and Generative Adversarial Networks (GANs)
- Introduction to Autoencoders
- Building Autoencoders with TensorFlow
- Introduction to GANs
- Building GANs with TensorFlow
Reinforcement Learning with TensorFlow
- Introduction to Reinforcement Learning
- Q-Learning and Deep Q-Learning
- Building RL Agents with TensorFlow
Advanced Topics in Deep Learning
- Attention Mechanisms
- Transformer Architecture
- BERT and NLP Applications
- Graph Neural Networks
TensorFlow Serving and Deployment
- Model Deployment Strategies
- TensorFlow Serving
- Deploying TensorFlow Models on Cloud Platforms
Hands-on Projects and Case Studies
- Practical Applications of Deep Learning
- Real-world Case Studies
- Project Presentations
Additional Considerations
- Guest Lectures or Industry Talks
- Hands-on Workshops and Labs
- Assessment Methods (Exams, Projects, Coding Assignments)
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details