Overview
Deep Learning Specialization with AI Training Course is an intensive program designed to provide participants with an in-depth understanding of deep learning techniques and their applications in artificial intelligence. This specialization covers a wide range of topics, from foundational concepts to advanced deep learning algorithms, enabling participants to develop the skills necessary to build and deploy AI models effectively.
Objectives
- Understand the fundamental concepts of deep learning and its applications in AI.
- Implement various deep learning algorithms and architectures.
- Train deep neural networks using popular frameworks and libraries.
- Apply deep learning techniques to solve real-world AI problems.
- Optimize and fine-tune deep learning models for improved performance.
- Implement advanced deep learning algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
- Explore specialized areas of deep learning, such as natural language processing (NLP) and computer vision.
- Gain practical experience through hands-on exercises and projects.
Audience
- Data scientists and machine learning practitioners seeking to deepen their knowledge of deep learning and AI.
- Software engineers interested in incorporating deep learning techniques into their AI projects.
- AI enthusiasts and professionals looking to gain expertise in the field of deep learning.
- Researchers and developers involved in AI-related projects.
Pre- requisites
- Basic knowledge of machine learning concepts and algorithms
- Familiarity with programming languages such as Python
- Understanding of linear algebra and calculus is beneficial
Duration: 5 days – 35 hrs
Course Content
Day 1: Introduction to Deep Learning and Neural Networks
- Overview of deep learning and its role in AI applications
- Neural networks and their building blocks
- Activation functions and forward propagation
- Backpropagation and gradient descent
Day 2: Convolutional Neural Networks (CNNs) and Computer Vision
- Introduction to CNNs and their applications in computer vision
- Convolutional layers, pooling, and stride operations
- Object detection and image segmentation using CNNs
- Transfer learning and fine-tuning pre-trained models
Day 3: Sequence Models and Recurrent Neural Networks (RNNs)
- Introduction to RNNs and their applications in natural language processing and speech recognition
- Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
- Sentiment analysis and language translation using RNNs
- Word embeddings and language generation
Day 4: Advanced Deep Learning Techniques
- Regularization techniques for deep learning models
- Optimization algorithms and learning rate schedules
- Generative adversarial networks (GANs) and their applications
- Reinforcement learning and deep Q-networks
Day 5: Special Applications and Project
- Specialized areas of deep learning (e.g., NLP, computer vision)
- Real-world project implementation using deep learning techniques
- Model evaluation and deployment considerations
- Future trends and advancements in deep learning and AI
