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PyTorch for Deep Learning Bootcamp: Zero to Mastery (Udemy.com)

Learn How to Use PyTorch (Facebook Library) for Deep Learning with Practical Examples

Created by: Navid Shirzadi

Last updated December 2024

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What you will learn

  • Understand the basic concepts about neural network and how it works
  • Use PyTorch for Linear Regression using Multilayer Perceptron (MLP)
  • Use PyTorch for image classification using Deep Artificial Neural Network (ANN)
  • Learn how to work with different data types such as tensors and arrays
  • Use PyTorch for image classification using Convolutional Neural Network (CNN)
  • Use PyTorch for time series prediction using Recurrent Neural Network (RNN)

How it compares

Against the other 126 rated Udemy courses in Deep Learning on CourseDuck:

  • Rating4.3 out of 5higher than 18% of them
  • Reviews245#95 of 127 by number of reviews
  • Length9.3 hoursshorter than 50% of them
  • Last updatedDecember 2024more recently than 55% of them

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Course Description

Deep learning has become one of the most popular machine learning techniques in recent years, and PyTorch has emerged as a powerful and flexible tool for building deep learning models. In this course, you will learn the fundamentals of deep learning and how to implement neural networks using PyTorch.


Through a combination of lectures, hands-on coding sessions, and projects, you will gain a deep understanding of the theory behind deep learning techniques such as deep Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs). You will also learn how to train and evaluate these models using PyTorch, and how to optimize them using techniques such as stochastic gradient descent and backpropagation. During the course, I will also show you how you can use GPU instead of CPU and increase the performance of the deep learning calculation.

In this course, I will teach you everything you need to start deep learning with PyTorch such as:

  • NumPy Crash Course

  • Pandas Crash Course

  • Neural Network Theory and Intuition

  • How to Work with Torchvision datasets

  • Convolutional Neural Network (CNN)

  • Long-Short Term Memory (LSTM)

  • and much more

Since this course is designed for all levels (from beginner to advanced), we start with basic concepts and preliminary intuitions.

By the end of this course, you will have a strong foundation in deep learning with PyTorch and be able to apply these techniques to various real-world problems, such as image classification, time series analysis, and even creating your own deep learning applications.


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Instructor Details

Navid Shirzadi

Welcome! I'm Navid Shirzadi, and I'm thrilled you're here.

I’m an Applied Scientist with a Ph.D. in Engineering and over a decade of experience working with integrated energy systems, optimization, and intelligent control strategies. My background spans mathematical optimization, advanced control, and hybrid modeling techniques that blend physics‑based simulations with modern AI.

I’m deeply experienced in Python, and I build cutting‑edge machine learning, deep learning, and agentic AI models for real‑world applications. My recent work focuses on leveraging agentic AI workflows and LLM‑powered tools to automate and enhance building energy modeling, EnergyPlus pipelines, and large‑scale simulation tasks.

Throughout my career, I’ve published research on intelligent control strategies for energy systems and developed AI‑driven methods that improve efficiency, reliability, and decision‑making. My passion lies in teaching these concepts in a clear, practical, and intuitive way, helping learners build skills they can immediately apply in engineering, data science, and AI‑driven modeling.

I’m excited to share my knowledge and help you grow your expertise in AI, optimization, and energy systems.

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Reviews

4.3

245 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Robert Craven on 3/18/2026

I had never used google colab before so that was excellent learning about it. In my mind he gets something wrong (based on my past study of neural networks) but minimally, more of semantics. It is frustrating that he hasn't pre created some nicer outputs, but instead am limited to watching him do things in real-time. The real-time is excellent mind you, but prepared outputs might make it better.

By Mike Perrault on 1/13/2026

This course was a letdown. It promises a deep learning bootcamp, but what it actually delivers is a rushed surface-level walkthrough of important topics. Too often, concepts are mentioned and coded without being properly explained, so it feels more like watching someone skim the material than actually being taught it. The biggest weakness is the lack of depth and intuition. Deep learning is not a subject you can learn well from glossed-over explanations, and this course does too much hand-waving around the theory and the practical use of PyTorch. It covers a lot of ground, but in a way that leaves you with exposure rather than understanding. If you want a quick overview, it may be fine. If you want a real foundation, look elsewhere.

By Florian Otel on 1/13/2026

Good material, but sloppy -- typos in the code, sloppy code,

By Leandro Borgonovi on 7/24/2024

I liked all the chapters, but liked less the RNN/LSTM section. By comparing it with previous sections, whose explanation were very graphical and step by step, I felt it a bit rushed. I think a quick overview with some images describing what we have and what we are going to do would help a lot, especially for the implementation videos. Nevertheless, I liked the previous chapters a lot and it was easy to learn from them.

By Raymond Lu on 6/27/2024

For the time allotted, I learned a lot. Obviously it was a little fast and lots of code copying. But you kinda get the general idea behind the models discussed. So I used it as a springboard to dive deeper into items that are more interesting to me and where I need more details / clarity.

By Adonis Andino on 3/7/2024

This course is an excellent starting point for anyone venturing into deep learning model development. The practical coding exercises using pytorch were immensely helpful, allowing me to grasp concepts easily. Thanks Navid it was great!

By Hooman Shirzadi on 3/3/2023

The course was very well-organized and structured, with each module building upon the previous one. Navid's teaching style was engaging, and he did an excellent job of explaining complex concepts in a way that was easy to understand. I found myself looking forward to each lesson and eager to learn more.

By Sharvin Nagane on 3/3/2023

The course is well-structured and easy to follow, with each module building on the previous one. The video lectures are engaging and well-presented, and the course materials are comprehensive and well-organized. The instructors of the course provides clear explanations and examples to help students understand the concepts and techniques used in the course. Overall, the PyTorch for Deep Learning Bootcamp: Zero to Mastery is an excellent course for anyone looking to learn how to use PyTorch for building deep learning models. If you're looking to improve your deep learning skills, this course is definitely worth considering.

By Marjan Maleki on 3/2/2023

I am so thrilled that I decided to take this course . This is well-organized , clear from the start ,telling complex problems in simple word, wonderfull knowledge, I think it will really help my Career.

By Robert on 3/1/2023

So far so good! I will update this when I finish the course! Update on my review: This course is a great course for learning deep learning with PyTorch! The only course which explains creating neural network Class in simple words!

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Overall Score : 86 / 100