A deep understanding of deep learning (with Python intro)

Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems.

4.78 (5198 reviews)

Udemy

platform

English

language

Data Science

What you will 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!)

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Comidoc Review

Our Verdict

A Deep Understanding of Deep Learning (with Python intro) offers a comprehensive and thorough exploration of deep learning's theoretical foundations, making it a great choice for anyone looking to solidify their understanding of both math and practical applications. However, the course's length might intimidate some learners, and occasional library-switching could prove slightly confusing for beginners. Despite this, the engaging teaching style and high-quality resources make it a valuable asset in developing necessary deep learning skills.

What We Liked

The course stands out for its in-depth coverage of the theory and math behind deep learning.
Excellent use of examples and practice problems to reinforce understanding.
Instructor's clear and concise explanations greatly enhance learning.
High-quality production valued by detail-oriented learners.

Potential Drawbacks

Some learners may find the course overly long, which can affect their commitment.
Occasional juggling between PyTorch, NumPy, and SciKit-Learn might be challenging for beginners.
Few users mentioned the lack of advanced topics such as transformers and reinforcement learning.