Deep Learning Neural Networks with Chainer Training Course

This course delves into the application of AI—particularly focusing on Machine Learning and Deep Learning—within the automotive industry. It explores which technologies can be potentially deployed across various automotive scenarios, ranging from basic automation and image recognition to complex autonomous decision-making.

Understanding the fundamentals of artificial intelligence demonstrates how intelligent technology is transforming digital strategy, automation, and decision-making across enterprise operations. This course examines core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside communication, perception, and autonomous actions. It equips executives and architects to evaluate opportunities for AI-driven transformation, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

Artificial Neural Networks are computational models employed in the development of Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. These networks are widely utilized in Machine Learning (ML) applications, which serve as a practical implementation of AI. Furthermore, Deep Learning represents a specialized subset of Machine Learning.

The course 'Applied AI from Scratch in Python' empowers programmers and data analysts by providing the essential techniques required to construct machine learning solutions entirely from the ground up using Python. It delves into the fundamental principles of supervised learning, including classification and regression, as well as unsupervised learning techniques such as clustering and anomaly detection. Additionally, the course explores advanced neural network architectures. Learners will examine proven methodologies for utilizing tools like scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The program enables professionals to implement practical ML models, assess algorithmic limitations, and execute applied projects designed for real-world problem-solving.

This instructor-led, live training in India (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

By the end of this training, participants will be able to:

• Apply core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions via interaction with their surroundings. It serves as the foundation for numerous contemporary AI innovations, including self-driving cars, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to learn strategies, optimize policies, and make autonomous decisions through trial and error using reward-based learning.

This instructor-led live training (available online or onsite) targets intermediate-level developers and data scientists keen on learning and applying Deep Reinforcement Learning techniques to construct intelligent agents capable of autonomous decision-making within complex environments.

Upon completion of this training, participants will be able to:

• Comprehend the theoretical foundations and mathematical principles of Reinforcement Learning.
• Implement core RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
• Apply DRL to real-world scenarios such as gaming, robotics, and decision optimization.
• Troubleshoot, visualize, and optimize training performance using modern tools.

Format of the Course

• Interactive lectures coupled with guided discussions.
• Hands-on exercises and practical implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course (e.g., substituting PyTorch for TensorFlow), please contact us to arrange.

This course starts by providing conceptual knowledge of neural networks, machine learning algorithms, and deep learning (including algorithms and applications).

Part 1 (40%) of this training focuses more on fundamentals, but will help you choose the right technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part 2 (20%) of this training introduces Theano, a Python library that makes writing deep learning models easy.

Part 3 (40%) of the training would be extensively based on TensorFlow - the API of Google's open-source software library for Deep Learning. The examples and hands-on sessions will all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

• have a good understanding of deep neural networks (DNN), CNN and RNN
• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production-like training models, building graphs and logging

This instructor-led, live training in India (online or onsite) targets advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.

By the end of this training, participants will be able to:

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.