Download A deep understanding of deep learning (with Python intro) Course For Free
Udemy - A deep understanding of deep learning (with Python intro)
What you’ll learn
The theory and math underlying deep learning
How to build artificial neural networks
Architectures of feedforward and convolutional networks
Building models in PyTorch
The calculus and code of gradient descent
Fine-tuning deep network models
Learn Python from scratch (no prior coding experience necessary)
How and why autoencoders work
How to use transfer learning
Improving model performance using regularization
Optimizing weight initializations
Understand image convolution using predefined and learned kernels
Whether deep learning models are understandable or mysterious black-boxes!
Using GPUs for deep learning (much faster than CPUs!)
Description
Deep learning is increasingly dominating technology and has major implications for society.
From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology.
But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data.
Deep learning is now used in most areas of technology, business, and entertainment. And it’s becoming more important every year.
What is this Deep understanding of deep learning (with Python intro) course all about?
Simply put: The purpose of this course is to provide a deep-dive into deep learning. You will gain flexible, fundamental, and lasting expertise on deep learning. You will have a deep understanding of the fundamental concepts in deep learning, so that you will be able to learn new topics and trends that emerge in the future.
Please note: This is not a course for someone who wants a quick overview of deep learning with a few solved examples. Instead, this course is designed for people who really want to understand how and why deep learning works; when and how to select metaparameters like optimizers, normalizations, and learning rates; how to evaluate the performance of deep neural network models; and how to modify and adapt existing models to solve new problems.
Download Links :
Part 1
Part 2
Part 3
Part 4
