This video is an overall package to understand Dropout in Neural Network and then implement it in Python from scratch.

By the end of the book, you’ll pack everything into a complete Python deep learning library, creating your own class hierarchy of layers, activation functions, and neural network architectures ...

These deep neural networks are capable of learning complex patterns and relationships in data, making them well-suited for tasks such as image and speech recognition, natural language processing ...

Neural networks are now applied across the spectrum of AI applications while deep learning is reserved for more specialized or advanced AI use cases. Written by eWEEK content and product ...

Pattern Recognition and Machine Learning, Christopher Bishop Deep Learning: Methods and Applications, Deng and Yu Wikipedia is often a useful resource. In the second half of the course, we’ll discuss ...

Explore the role of activation functions in deep learning and how they help neural networks learn complex patterns.

Artificial neural networks are better than other methods for more complicated tasks like image recognition, and the key to their success is their hidden layers. We'll talk about how the math of ...

Deep Neural Networks are the more computationally powerful cousins to regular neural networks. Learn exactly what DNNs are and why they are the hottest topic in machine learning research. The term ...

Born in the 1950s, the concept of an artificial neural network has progressed considerably. Today, known as “deep learning”, its uses have expanded to many areas, including finance.