A deep understanding of deep learning (with Python intro), is a deep learning training course in PyTorch using a scientific and practical method with the help of many examples and practice problems. By taking this course, you will gain a flexible, principled, and lasting experience in deep learning. You will learn the basic concepts with the help of this course and you will be able to easily learn new topics and trends in the future.
Note that this course is not just a summary of in-depth learning, but instead comprehensively examines why and how in-depth learning works and the many important concepts involved.
What you will learn in A deep understanding of deep learning (with Python intro):
- Theory and mathematics behind deep learning
- Build an artificial neural network
- Feedforward and convolutional network architecture
- Construction of models in the base of Torch
- Differential and coding gradient descent
- Calibration of deep network models
- Learn Python from the ground up (without the need for any coding experience)
- How and why autoencoders work
- Use transfer learning
- Improve the performance of models using regularization
- Weight optimization
- Understand image convolution using pre-trained kernels
- Use GPU for deep learning
Instructors: Mike X Cohen
Number of Courses: 265
Duration: 57 hours and 17 minutes
Prerequisites for A deep understanding of deep learning (with Python intro):
Interest in learning about deep learning!
Python / Pytorch skills are taught in the course
A Google account (google-collab is used as the Python IDE)
After Extract, watch with your favorite Player.
Version 2021/12 compared to 2021/8 has increased by 9 lessons (1 section) and duration of 2 hours and 52 minutes.
Download section 1 – 5 GB
Download Part 2 – 5 GB
Download Section 3 – 5 GB
Download Section 4 – 5 GB
Download Section 5 – 1.34 GB
file password link
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