AI & Deep Learning with TensorFlow is a 5-day course designed to provide a detailed and comprehensive introduction to deep learning. Participants will explore the fundamentals of AI, neural networks, and TensorFlow, gaining valuable hands-on experience to build, train, and deploy deep learning models. This structured program ensures participants develop real-world skills in AI and deep learning.
This course is designed to provide participants with a deep understanding of artificial intelligence (AI) and deep learning concepts using TensorFlow, one of the most widely-used frameworks in AI development. The course offers a blend of theory and hands-on practice to equip participants with the skills needed to build, train, and deploy advanced AI models. Whether you are a beginner in machine learning or an experienced developer looking to deepen your AI expertise, this course will guide you through the tools and techniques required to solve real-world problems using AI and deep learning.
Over the duration of this course, participants will start with the fundamentals of TensorFlow, progress to understanding neural networks, and finally dive into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). With practical labs and projects, you will gain hands-on experience in creating AI-driven solutions for various applications, including image recognition, natural language processing, and predictive analytics.
- Duration: Flexible (can be tailored to client requirements)
- Delivery Modes:
- Live Offline Training: Conducted at a designated venue
- Online Training: Delivered via Zoom with live interaction
- Prerequisites:
- Basic understanding of programming (Python preferred)
- No prior experience with TensorFlow is required
- Included in the Course Fee:
- Facilitation by industry experts
- Training materials and resources
- Certificate of Successful Completion
- FREE consultation and coaching during and after the course
- Key Learning Outcomes:
- Master the fundamentals of AI, deep learning, and TensorFlow
- Develop and train neural networks for various applications
- Understand and implement CNNs, RNNs, and GANs
- Effectively deploy AI models in real-world scenarios
- Stay updated with the latest trends and advancements in AI and deep learning
Take the first step towards a fulfulling career in AI — enroll today!
Below is the module-by-module breakdown for each day of the 5-day program:
- Fundamentals of AI and Deep Learning
- Overview of AI and its applications
- Introduction to neural networks and their components
- Key concepts of deep learning and its role in AI advancements
- TensorFlow basics and environment setup
- Introduction to TensorFlow operations and data structures
- Building a simple deep learning model with TensorFlow
- Understanding Neural Network Architecture
- Perceptrons and multilayer perceptrons Activation functions and their impact on model performance Designing a neural network architecture
- Training Neural Networks
- Data preprocessing techniques for effective training
- Loss functions and their role in optimization
- Training a neural network with TensorFlow
- Convolutional Neural Networks (CNNs)
- Basics of CNNs and their applications in image processing
- Building a CNN with TensorFlow
- Training and optimizing a CNN for image classification
- Introduction to RNNs and their applications in sequential data
- Implementing RNNs with TensorFlow
- Practical examples: Time series analysis
- Transfer Learning
- Understanding transfer learning and its advantages
- Applying pre-trained models to new datasets
- Fine-tuning models for domain-specific applications
- Image classification with TensorFlow
- Natural language processing (NLP) for text analysis
- Implementing deep learning for time series forecasting
- Model Deployment
- Introduction to TensorFlow Serving
- Deploying models on cloud platforms
- Building REST APIs for AI model integration
- Evaluating model performance and accuracy
- Techniques for hyperparameter tuning
- Best practices for optimizing deep learning models
- Data Scientists
- Machine Learning Engineers
- Software Developers
- Any professional aspiring to work with AI and deep learning