Deep Learning Essentials Course

The Deep Learning Essentials Course, by Oxford Training Centre, is a one-week detailed training that outlines how to teach the new area of deep learning concepts and techniques. The course is very important to those who wish to enhance their skills in the field of deep learning, neural networks, and artificial intelligence. Over the week, learners will be introduced to the basics of deep learning, explore some of the machine learning and deep learning algorithms, and finally get hands-on experience using popular frameworks such as TensorFlow and Keras. Whether you’re a complete beginner or have some experience in the field, this course is designed to provide you with the necessary toolset to dive into the world of deep learning and AI.

Objectives and target group

Objectives

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

• Have a solid understanding of the basics: artificial neural networks, the principles of deep learning, and machine learning models in general.
• Understand in what ways neural networks can be structured and trained for a particular task, discussing supervised and unsupervised methods of learning.
• Become familiar with some of these models, including Convolutional Neural Networks and Recurrent Neural Networks.
• Get hands-on experience with deep learning frameworks in TensorFlow and Keras in building and training deep learning models.
• Study higher-order deep learning concepts in computer vision and natural language processing using deep learning methods.
• Learn how to apply deep learning techniques on real-world applications such as AI solutions for big data, data science, and so on.
•  Develop hands-on experience in creating and deploying deep learning models using Python.

Target Group

This course is designed for:

• Beginners in Deep Learning: If you’re new to deep learning, this course will provide you with the foundational knowledge to understand neural networks and start building your own models.
• Data Science Enthusiasts: If you’re interested in applying deep learning in data science and AI fields, this course offers insights into deep learning applications, data processing, and model evaluation.
• AI and Machine Learning Professionals: Individuals looking to expand their skill set and gain expertise in deep learning techniques such as CNNs, RNNs, and TensorFlow.
• Non-Coders: Those with limited programming experience who want to learn the core concepts and practical applications of deep learning without getting into heavy coding.
• Students and Researchers: Anyone looking to gain a deeper understanding of deep learning methods and how they can be applied in real-world problems, especially in AI and big data contexts.

Content

This course covers a wide range of topics designed to provide a comprehensive understanding of deep learning, from basic concepts to more advanced applications:

1. Introduction to Deep Learning
   • What is deep learning? Overview and importance.
   • Difference between machine learning and deep learning.
   • Core components: Neural Networks and Artificial Neural Networks (ANN).
2. Deep Learning Basics and Fundamentals
   • Understanding the structure and architecture of neural networks.
   • Training deep learning models with various datasets.
   • Supervised vs. Unsupervised Learning.
   • Introduction to popular deep learning algorithms.
3. Deep Learning Models
   • Convolutional Neural Networks (CNN) for image processing and computer vision.
   • Recurrent Neural Networks (RNN) and applications in time series analysis and sequence prediction.
   • Introduction to Generative Adversarial Networks (GANs) and their uses in data generation.
4. Deep Learning Frameworks
   • Hands-on tutorials using TensorFlow and Keras to build deep learning models.
   • Working with Python for data manipulation and model training.
   • Overview of other deep learning frameworks and their use cases.
5. Applications of Deep Learning
   • Implementing deep learning in computer vision, image recognition, and natural language processing (NLP).
   • Leveraging deep learning for AI solutions in big data and data science.
   • Understanding neural network architectures and their application in real-world problems.
   • Case studies on deep learning applications in industries such as healthcare, finance, and robotics.
6. Advanced Deep Learning Techniques
   • Exploring deep learning in reinforcement learning and AI-based decision-making.
   • Understanding advanced neural network techniques for model optimization.
   • Exploring big data and deep learning: How to handle large-scale data with deep learning models.
7. Practical Sessions
   • Hands-on tutorials and exercises to implement what you’ve learned.
   • Developing deep learning models from scratch using Python.
   • Training deep learning models with real-world datasets and fine-tuning them for optimal performance.
8. Deep Learning for Students and Non-Coders
   • Simple techniques to start building neural networks with minimal coding.
   • Introduction to AI and deep learning concepts for students and individuals with no coding background.