🐍 Improving Deep Neural Networks!
Join our Improving Deep Neural Networks course, launched in 2023. Covering various aspects, the course comprises video lectures, notes, slides, tutorials, and quizzes from multiple sources.
You'll find links to top university courses, important websites, a GitHub repository, a Reddit group, and a YouTube channel for learning. Contributions are welcome! The content is primarily sourced from Coursera's Improving Deep Neural Networks course, with plans to incorporate material from other neural network courses.
Remember, learning is a continuous process. So keep learning, creating, and sharing with others! 💻✌️
🚫 Guest access is limited. To unlock full course content, please enroll in the course.
📚Other Free Usufull Resources to Learn Improving Deep NN
Welcome to an Exciting Chapter on Deep Learning!
Explore a curated collection of links to top-rated courses and valuable resources dedicated to Deep Learning. Whether you're just starting your journey or a seasoned practitioner, there's something here for everyone to enhance their expertise in this cutting-edge field.
📚 Chapter 1:Practical Aspects of Deep Learning
In this chapter, you lear about Train / Dev / Test sets,Bias Variance,Basic Recipe for Machine Learning,Regularization,Why Regularization Reduces Overfitting?,Dropout Regularization,Other Regularization Methods,Normalizing Inputs,Vanishing-Exploding Gradients,Weight Initialization for Deep Networks,Numerical Approximation of Gradients,Gradient Checking