Course description

The Deep Learning & Neural Networks course provides a comprehensive understanding of AI-driven technologies and their real-world applications. This course is ideal for data scientists, ML engineers, and AI enthusiasts looking to train deep learning models and deploy them in production.

Deep Learning powers self-driving cars, medical diagnosis, speech recognition, and language translation. This course teaches you to build, train, and optimize neural networks to solve complex AI problems.

What You’ll Learn:

???? Introduction to Deep Learning – Understand how deep learning differs from traditional machine learning.
???? Artificial Neural Networks (ANNs) – Learn how neurons work, backpropagation, and optimization techniques.
???? Convolutional Neural Networks (CNNs) – Master image classification, object detection, and transfer learning.
???? Recurrent Neural Networks (RNNs) & LSTMs – Build AI models for time-series prediction and natural language processing (NLP).
???? Generative Adversarial Networks (GANs) – Learn how AI generates realistic images, videos, and text.
???? Deploying AI Models – Use cloud platforms (AWS, GCP) and edge computing for real-world applications.
???? Hands-on Deep Learning Projects – Work on real-world datasets in computer vision, NLP, and reinforcement learning.

By the end of this course, you’ll be able to train, evaluate, and deploy deep learning models for AI-driven solutions.

What will i learn?

• Understand deep learning architectures & neural networks.
• Train & fine-tune AI models for various applications.
• Work with CNNs, RNNs, and GANs for computer vision & NLP.
• Optimize AI models for better accuracy and performance.
• Deploy deep learning models in real-world scenarios.

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