Artificial Neural Networks & Deep Learning In R
Artificial Intelligence & Machine Learning for Practical Data Science in R
Tailored for data scientists, analysts, and R enthusiasts, this program provides an in-depth understanding of artificial neural networks and their application in solving complex problems.
Key Learning Highlights:
- Fundamentals of Neural Networks: Gain a solid foundation in the basics of neural networks, understanding the architecture, activation functions, and training processes that form the backbone of artificial intelligence.
- Introduction to Deep Learning: Delve into the realm of deep learning, exploring its significance, and discovering how artificial neural networks are extended into deep architectures to handle intricate patterns and representations.
- R Programming for Deep Learning: Learn how to implement neural networks and deep learning models using the R programming language. Explore popular R libraries like 'keras' and 'tensorflow' for building and training complex models.
- Model Architecture and Design: Master the art of designing neural network architectures suitable for various tasks. Understand the choices involved in selecting the number of layers, nodes, and activation functions.
- Training and Optimization: Explore techniques for training neural networks effectively, including strategies for addressing overfitting, underfitting, and optimization algorithms for minimizing loss functions.
- Advanced Deep Learning Concepts: Delve into advanced concepts in deep learning, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transfer learning, understanding their applications and implementation in R.
- Practical Applications: Apply your knowledge through hands-on projects, solving real-world problems and gaining practical experience in implementing artificial neural networks and deep learning models in R.
Your Instructor
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The instructors provide clear explanations and practical examples, making complex concepts accessible. The hands-on projects were challenging yet rewarding, and I now feel equipped to apply deep learning techniques to my data science projects.
- Emily
As an R enthusiast, this course was a perfect fit for me. The content is well-structured, covering both fundamental concepts and advanced deep learning topics. The hands-on approach, coupled with real-world applications, made the learning experience engaging and applicable.
- Anand