AI & Deep Learning Training
Introduction to AI and Deep Learning
Artificial Intelligence (AI) and Deep Learning are rapidly evolving fields within machine learning that focus on creating systems that can learn and make decisions. This module provides an overview of AI concepts, deep learning fundamentals, and their applications.
Fundamentals of AI
Learn about the basic principles of Artificial Intelligence, including machine learning, neural networks, and natural language processing. Explore key AI concepts and their impact on various industries.
Introduction to Deep Learning
Discover the fundamentals of Deep Learning, including neural networks, activation functions, and training techniques. Learn about the architecture of deep neural networks and how they are used for tasks such as image and speech recognition.
Building and Training Models
Understand how to build and train deep learning models using popular frameworks such as TensorFlow and PyTorch. Learn about model design, hyperparameter tuning, and evaluation metrics.
Applications of AI and Deep Learning
Explore the real-world applications of AI and Deep Learning. Learn about use cases in areas such as healthcare, finance, and autonomous systems. Discover how AI and deep learning technologies are transforming various industries.
Ethics and Challenges
Gain insights into the ethical considerations and challenges associated with AI and Deep Learning. Learn about data privacy, algorithmic bias, and the societal impact of AI technologies.
Tools and Techniques
Discover the tools and techniques used in AI and Deep Learning. Learn about programming languages, development environments, and software libraries that support AI research and development. Explore how to leverage these tools for building and deploying AI solutions.
Best Practices and Future Trends
Explore best practices for working with AI and Deep Learning. Learn about model optimization, deployment strategies, and ongoing advancements in the field. Gain insights into emerging trends and the future of AI and deep learning technologies.
AI & Deep Learning Course Syllabus
Introduction to Artificial Intelligence
- Definition and brief history of AI
- AI applications across industries
- Types of AI: Narrow vs. General AI
Machine Learning Basics
- Overview of Machine Learning (ML)
- Types of ML algorithms: Supervised, Unsupervised, Reinforcement Learning
- Feature engineering and data preprocessing
Deep Learning Fundamentals
- Introduction to Deep Learning (DL)
- Neural networks: Perceptron, Multi-layer Perceptron (MLP)
- Activation functions and loss functions
Neural Networks
- Basics of Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN) for image processing
- Recurrent Neural Networks (RNN) for sequence data
Training Neural Networks
- Backpropagation algorithm
- Optimizers: SGD, Adam, RMSprop
- Overfitting and regularization techniques (dropout, batch normalization)
Advanced Deep Learning Architectures
- Transfer Learning and fine-tuning pre-trained models
- Autoencoders for unsupervised learning
- Generative Adversarial Networks (GANs) for generating new data
Natural Language Processing (NLP)
- Basics of NLP and its applications
- Word embeddings: Word2Vec, GloVe
- Recurrent Neural Networks (RNN) and LSTM for text data
Computer Vision
- Introduction to Computer Vision tasks
- Image classification and object detection
- Deep Learning frameworks for CV: TensorFlow, PyTorch
Ethical and Social Implications of AI
- Bias and fairness in AI algorithms
- Privacy concerns and data security
- AI regulation and responsible AI practices
AI in Practice
- Case studies of AI applications in healthcare, finance, autonomous vehicles, etc.
- Industry trends and future directions in AI research and development
Hands-on Projects
- Implementing supervised and unsupervised learning algorithms
- Building neural networks for image classification and NLP tasks
- Working with real-world datasets and evaluating model performance
Tools and Libraries
- Overview of popular Deep Learning frameworks: TensorFlow, PyTorch, Keras
- Hands-on experience with Jupyter Notebooks or Google Colab
Deployment and Scalability
- Deploying Deep Learning models in production environments
- Scalability considerations and cloud deployment options
AI Development Lifecycle
- Planning and defining AI projects
- Iterative development, testing, and validation
- Monitoring and maintenance of AI systems
Career Path
- Career opportunities in AI: 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