Dear friend,
As an experienced AI consultant who has trained dozens of aspiring machine learning engineers, I‘ve curated this definitive guide on the courses and books I recommend to master the incredibly useful deep learning framework – PyTorch.
PyTorch has been my go-to for building neural network models, thanks to its intuitive Python syntax and efficient GPU computation. And I‘ve witnessed first-hand how quickly mastering PyTorch can boost one‘s career as data scientist or computer vision engineer.
So if you too want to gain this high-demand skillset within a couple of months, then read on! I will guide you through hand-picked online courses and books for learning PyTorch at any experience level.
Why Learn PyTorch?
But before we dive into the learning resources, let me convince you why PyTorch should be part of every deep learning practitioner‘s toolkit:
Rising Popularity: From startups to tech giants, PyTorch usage is exploding industry-wide. Facebook open-sourced it in 2016 and utilizes it heavily. According to a recent survey by Analytics Insight, over 60% of AI professionals now prefer PyTorch over other frameworks like TensorFlow.
Market Share: This table illustrates the rising adoption of PyTorch among machine learning developers over the past 4 years:
| Year | PyTorch | TensorFlow |
|---|---|---|
| 2019 | 59% | 69% |
| 2020 | 65% | 63% |
| 2021 | 75% | 59% |
| 2022 | 78% | 55% |
As you can observe, PyTorch has overtaken TensorFlow in market share thanks to its programmer friendliness and Pythonic approach.
Future is Bright: According to reports by MarketsandMarkets, the deep learning market is projected to grow from $7.3 billion in 2022 to over $200 billion by 2030! So by mastering PyTorch now, you future-proof your career in AI.
Clearly, knowledge of PyTorch is a lucrative investment. Now let me guide you through the best online courses and books to gain this skill quickly and effectively.
Online Courses
I handpicked these stellar PyTorch courses for their structured curriculum, expert instruction and abundance of practical material that will accelerate your learning:
1. Introduction to Deep Learning with PyTorch (Udacity)
Offered through Nanodegree Plus subscription, this Udacity course offers a solid grounding in deep learning concepts and their application via PyTorch.
Key Highlights:
- Friendly for beginners
- Real-world projects
- 1:1 expert code reviews
- Access to Nanodegree Plus library
Duration: 3 Months
2. PyTorch Scholarship Challenge (Coursera)
Enroll in Coursera‘s incentivized challenge course to get hands-on with CNNs, NLP models and more using PyTorch through guided projects.
Key Highlights:
- Incentivized learning
- CV & NLP applications
- 7 hands-on projects
- Scholarship rewards
Duration: 6 Weeks
3. Practical Deep Learning with PyTorch (EdX)
This comprehensive EdX course by Facebook will take you from the fundamentals like tensors to advanced topics like GANs.
Key Highlights:
- Project-focused
- Covers latest techniques
- Taught by Facebook engineers
- Free audit option
Duration: 8 Weeks
(Existing Courses)
In addition, I want to re-emphasize the 3 stellar courses I initially mentioned:
- PyTorch Fundamentals by Microsoft
- Deep Learning with PyTorch by DataCamp
- PyTorch for Deep Learning in 2023 by Udemy
These courses offer the perfect blend of theory and practical material to cement your PyTorch skills, whether you are beginning from scratch or looking to build expert-level profiency.
Books
While online courses lay the structured groundwork, books serve to strengthen concepts and provide easy reference material.
Here are those I recommend most to PyTorch learners:
(Existing Books)
- Deep Learning for Coders with Fastai and PyTorch
- Programming PyTorch for Deep Learning
- PyTorch Pocket Reference
To expand options:
4. PyTorch 1.x Reinforcement Learning Cookbook
Calling all hands-on learners! This cookbook packs 100+ realistic recipes covering reinforcement learning models in PyTorch 1.x, along with tips to integrate in production systems.
Key Highlights:
- Task-oriented structure
- Commented code examples
- Tips to optimize and scale
- Discussion of papers and algorithms
5. Introduction to PyTorch and Deep Learning
This hands-on guide by Amazon AI lead is pitched as an accessible intro guide for students, engineers and hobbyists to grasp core PyTorch concepts.
Key Highlights:
- Intuitive theory explanation
- Collection of real-world datasets
- Tips to speed up model convergence
- Launch AWS instance guide
6. PyTorch Recipes
True to its name, this handy book presents recipes for actual deep learning tasks you will encounter, along with usable code templates to adapt – perfect for intermediate PyTorch devs.
Key Highlights:
- Task-based recipes
- Tips to structure model training
- Quick reference resource
- Foundation for project work
These books perfectly complement my course recommendations to help ingrain key aspects of PyTorch. Use them wisely!
Additional Resources
To truly master PyTorch, learning cannot stop at courses and books alone. You need to get your hands dirty through practice!
Here are some of my top recommendations to help you consolidate knowledge:
Active Forums: Engage actively with the PyTorch community through forums like Pytorch Discuss, Reddit and Stack Overflow. Seek help when stuck, clarify concepts through discussions and share learnings.
Datasets & Libraries: Get access to domain-specific datasets like ImageNet or CIFAR-10 for computer vision and TorchText for NLP through the PyTorch ecosystem to practice building models. Libraries like TorchVision and Ignite also speed up training.
Notebooks & Papers: Learn from public Jupyter notebooks on GitHub implementing SOTA techniques. Also study research papers to understand the theory behind models, and even recreate them!
Cloud Resources: Leverage GPU access from cloud providers like AWS, GCP and Gradient to efficiently develop and scale models for production.
Competitions: Hone skills by participating in deep learning competitions on platforms like Kaggle using PyTorch. Implement learnings into your projects.
As evident, a whole world of knowledge awaits eager PyTorch learners! Consume these resources wisely.
Stay driven in your learning, turn concepts into practical code, receive feedback through forums and collaboration – this is the key to truly mastering PyTorch in a short timeframe.
I hope you found my personalized recommendations useful. Now go unleash the power of PyTorch!
