AI & Deep Learning with TensorFlow Training
Introduction to AI and TensorFlow
Artificial Intelligence (AI) and Deep Learning are advanced fields of machine learning focused on creating systems that learn from data. TensorFlow is a powerful open-source library for building and deploying machine learning models. This module provides an overview of AI concepts, deep learning fundamentals, and TensorFlow's role in these fields.
Getting Started with TensorFlow
Learn the basics of TensorFlow, including installation, setup, and core components. Explore TensorFlow’s architecture, data structures, and how to use TensorFlow for building and training machine learning models.
Building Neural Networks with TensorFlow
Discover how to build neural networks using TensorFlow. Learn about different types of layers, activation functions, and how to design and train deep learning models. Explore practical examples and hands-on exercises for building neural networks from scratch.
Model Training and Optimization
Understand the processes of training and optimizing deep learning models with TensorFlow. Learn about techniques for hyperparameter tuning, model evaluation, and performance optimization. Explore methods for improving model accuracy and efficiency.
Applications of AI and TensorFlow
Explore real-world applications of AI and TensorFlow. Learn about use cases in areas such as image recognition, natural language processing, and predictive analytics. Discover how TensorFlow is used to solve complex problems across various industries.
Ethics and Challenges in AI
Gain insights into the ethical considerations and challenges associated with AI and deep learning. Learn about issues related to data privacy, algorithmic bias, and the societal impact of AI technologies.
Advanced TensorFlow Techniques
Dive into advanced TensorFlow techniques, including transfer learning, custom model building, and deployment. Explore how to leverage TensorFlow for more complex tasks and real-world applications.
Best Practices and Future Trends
Explore best practices for working with TensorFlow and AI. Learn about model optimization, deployment strategies, and the latest advancements in AI and deep learning. Gain insights into emerging trends and the future direction of TensorFlow technologies.
AI & Deep Learning with TensorFlow Course Syllabus
Introduction to Artificial Intelligence and Deep Learning
- Overview of Artificial Intelligence (AI) and its subfields
- Introduction to Deep Learning (DL) and its applications
- Brief history and evolution of Deep Learning
Foundations of Machine Learning
- Basics of Machine Learning (ML) algorithms
- Supervised, Unsupervised, and Reinforcement Learning
- Model evaluation and performance metrics
Introduction to TensorFlow
- Introduction to TensorFlow and its ecosystem
- TensorFlow vs. other Deep Learning frameworks (PyTorch, Keras)
- Installing TensorFlow and setting up the development environment
TensorFlow Basics
- Tensors and operations in TensorFlow
- Building computational graphs
- Executing computations with TensorFlow sessions
Neural Networks with TensorFlow
- Building Neural Networks using TensorFlow
- Activation functions, loss functions, and optimization algorithms (SGD, Adam)
- Training Neural Networks with TensorFlow
Convolutional Neural Networks (CNNs)
- Understanding Convolutional Neural Networks (CNNs)
- Building CNNs for image classification tasks
- Transfer Learning with pre-trained CNN models (e.g., VGG, ResNet)
Recurrent Neural Networks (RNNs)
- Introduction to Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
- Applications of RNNs in sequence modeling (e.g., text generation, language translation)
Advanced Deep Learning Architectures
- Generative Adversarial Networks (GANs) and their applications
- Autoencoders for unsupervised learning and dimensionality reduction
- Sequence-to-Sequence models for language translation and speech recognition
Natural Language Processing (NLP) with TensorFlow
- Basics of Natural Language Processing (NLP)
- Word embeddings (Word2Vec, GloVe) and text preprocessing
- Building NLP models using TensorFlow for sentiment analysis, named entity recognition, etc.
Advanced Topics in TensorFlow
- Customizing models with TensorFlow's high-level APIs (tf.keras)
- Distributed TensorFlow for scaling Deep Learning models
- TensorFlow Serving for model deployment and serving predictions
Ethical and Social Implications of AI
- Bias and fairness in AI algorithms
- Privacy concerns and data security in Deep Learning applications
- Ethical considerations in AI research and deployment
Hands-on Projects and Case Studies
- Implementing end-to-end Deep Learning projects with TensorFlow
- Case studies of TensorFlow applications in industries such as healthcare, finance, and autonomous driving
Deployment and Productionization
- Deploying TensorFlow models in production environments
- Containerizing models with Docker and serving with Kubernetes
- Monitoring and maintaining TensorFlow models in production
Research Trends and Future Directions
- Emerging trends in Deep Learning research
- Future directions of TensorFlow and AI technologies
Career Development
- Preparation for TensorFlow Developer certification (if applicable)
- Career paths in AI and Deep Learning: Data Scientist, Machine Learning Engineer, AI Researcher
Training
Basic Level Training
Duration : 1 Month
Advance 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