Master Deep Learning with the 9 Best Courses and Resources for 2023

As an experienced AI practitioner, I‘ve seen firsthand how hot deep learning skills are. Elite tech firms, ambitious startups, and forward-thinking companies across sectors are all racing to tap advances in deep learning to create smart products.

The demand for deep learning talent is off the charts thanks to wide applications potential. As Linkedin‘s 2022 Emerging Jobs report revealed, machine learning engineering roles grew over 140%, 3x the average rate!

So whether you want to become an AI Research Scientist designing next-gen algorithms at OpenAI…or a Computer Vision Engineer working on self-driving cars at Tesla…now is the time to skill up!

I‘ve crafted this guide to save you months of effort by highlighting:

  • Why deep learning is red hot 🔥
  • Applications disrupting billion dollar industries 💰
  • The 9 best courses distilled from 100s of options ✅
  • Bonus resources to augment your learning 🚀
  • Tips to accelerate mastery from an insider! 🧠

Let‘s get you on the fast track to a rewarding career in deep learning!

Why Deep Learning is Taking Over the World

First things first – what even is deep learning? Here‘s a 101 explainer:

Deep learning is a technique in artificial intelligence using neural networks modeled after the human brain and containing multiple layers. These neural networks are trained on vast amounts of data to recognize complex patterns for natural language processing, computer vision, speech recognition, and more futuristic applications.

For instance, deep learning helps Facebook auto tag you and friends in photos…powers Alexa and Siri to understand verbal commands…lets doctors diagnose illnesses from medical scans with incredible accuracy.

The "deep" in deep learning refers to the depth of these neural networks in recognizing patterns and features humans would likely miss. Modern deep learning models now have 100s of layers as compared to early models having just 2-3 layers.

So while traditional coding logic hits limitations in complexity, deep learning provides an intuitive bridge between data and insight – leading to huge potential.

According to Tractica, the deep learning market already generates $12 billion in revenue but is forecasted to balloon to over $100 billion by 2025! The apps are endless but so is the need for specialized talent.

As thought leaders like Elon Musk double down on the promise of AI, mastering deep learning could prove to be the most valuable investment in your career this decade.

But before we jump into the curated courses and resources, let‘s better understand deep learning skill levels and trajectory…

Deep Learning Skills Roadmap and Career Paths

The first step to mastery is clarity on where you currently stand and where you want to reach.

Broadly speaking there are 3 levels of expertise:

Level 1 Beginners

Just getting started and working to grok basic concepts, math foundations, and tools like Python, TensorFlow.

Example Roles:

  • AI Programmer
  • Deep Learning Engineer

Level 2 Practitioners

Comfortable with neural networks and can build models for different applications with Python/R frameworks.

Example Roles:

  • Computer Vision Engineer
  • NLP Scientist
  • AI Research Assistant

Level 3 Authorities

PhD-level grasp over algorithms, architectures; publishes papers advancing SOTA models.

Example Roles:

  • Chief AI Scientist
  • Machine Learning Director
  • University Professor

The right curriculum will accelerate your progress through these levels unlocking new doors.

Most beginners should focus on building an intuitive understanding via visual resources before diving into college-grade math and research.

Practitioners should emphasize coding fluency via diverse projects in domains like NLP, computer vision, recommendations etc. This allows you to pick up new techniques rapidly.

Authorities benefit most from academic rigor to invent new techniques – leveraging research publications and conferences to steer the industry forward.

Of course, these aren‘t black and white limits. For instance, many self-taught authorities directly focus on applied skills vs formal theory.

But the major career switch points come from projects showcasing coding skills for industry hires…to research leadership for heading university labs.

Rest assured though – consistent, focused effort for as little as 5 hours per week can take you from beginner to confident practitioner level in just 6-12 months!

Building on core skills with lifelong learning tops you up as an authority. Excited yet? Let‘s find you the perfect launchpad!

9 Best Deep Learning Courses & Resources for 2023

Let‘s review the leading courses and supplemental materials most recommended for budding AI wizards in 2023:

1. Hands-On Deep Learning Foundation – Udemy

Udemy‘s Deep Learning A-ZTM course is like the gateway drug for novices looking to start an exciting AI journey.

Instructed by machine learning wizard Hadelin de Ponteves, over 349,000 students have enrolled already – and for good reason!

What You‘ll Learn

The structured 22 hour curriculum complete with hands-on Python coding projects gives you:

  • Intuitive overview of neural networks without complex math ✅
  • Training deep learning models with Keras, Tensorflow & PyTorch ⚙️
  • Building apps for computer vision, NLP, speech recognition 📝
  • Optimizing models in domains like healthcare, finance, eCommerce 🏥💵🛒

Hadelin de Ponteves keeps the course updated with cutting-edge use cases across industries so the applicability is immense.

The theory is brought to life via practical examples across computer vision, NLP and tabular data rather than dry textbook concepts alone. This helps demystify techniques to inspire your experiments.

Who It‘s Best For

Given the beginner-friendly delivery, this course is perfectly suited for anyone looking to switch careers or acquire their first hands-on applied skills in deep learning.

The prerequisites are basic high school math and some coding experience in any language – both are covered but prior familiarity allows quicker comprehension.

Over 85,000 ratings give this an impressive 4.6 average making it a foolproof gateway.

Commitment & Credentials

With 22 hours of on-demand video content split into 349 bite-sized lessons, you can move through the content flexibly across 8-12 weeks investing 5 hours per week.

Upon completion, you will get an accredited certification to exhibit your learnings in job and university applications.

You also enjoy lifetime access in case you ever need a refresher down the road. A 30-day refund policy assures you can test drive risk-free.

👉🏻ENROLL in Deep Learning A-ZTM course

2. FastAI‘s Practical Approach for Coders

Prefer to learn while building real apps? FastAI‘s Practical Deep Learning for Coders course powered by tech visionary Jeremy Howard is just what you need.

What You‘ll Learn

This project-fueled course gives you:

  • 8 complete coding templates for tackling computer vision, NLP and tabular analytics 💻
  • Dynamic guidance from world-class experts as you code
  • Grade A results without needing math theory or PhD research!

Jeremy Howard deliberately designed the course for self-taught coders looking for quick wins.

You learn concepts like regularization, normalization and embeddings via code tricks rather than textbooks – building intuition through trial-and-error.

The community forum and direct code reviews gives you feedback each step rather than leaving you stuck. No wonder the ratings are off the charts!

Who It‘s Best For

Programmers hungry for real-world skills will love this approach over dry theory or dated tutorials.

While beginners can make progress too, you will benefit most from having 1-2 years of coding experience – ideally in Python or any OOP language with some data manipulation.

This course best suits those looking to level up to advanced practitioner roles or switch from general SWE roles to specialized AI engineering.

Commitment & Credentials

Expect to spend 5-10 hours per week for 8-12 weeks completing the 8 milestone projects with guidance.

You enjoy unlimited community forum access and can schedule expert code reviews to perfect your models.

By the end, your portfolio of practical NLP, CV and ML apps speaks louder than any certificate or test score when interviewing.

👉🏻APPLY to Join Practical Deep Learning for Coders

3. Deep Learning Specialization – Coursera

What discussion is complete without the legend himself – Professor Andrew Ng.

His Deep Learning Specialization on Coursera is a rite of passage for aspiring AI wizards globally with over 300,000 enrolled already!

What You‘ll Learn

The 5 course lineup delivers an unparalleled balance covering:

  • Foundations around linear algebra, stochastic gradient descent, backpropagation techniques 🧮
  • Building & training neural networks, CNNs, RNNs from scratch 🧱
  • Cutting edge techniques like hyperparameter tuning, embeddings, dropout 🪄
  • Python coding experience via TensorFlow, Keras and PyTorch 🐍
  • Applying state-of-the-art DL projects in NLP, Computer Vision, Speech Rec 💡
  • Bachelor certificate from deeplearning.ai upon completion 🎓

The combo of theory via Mini slide decks + Practical application Notebook projects gives you confidence in discussing mathematical basis while also coding models tackling industry datasets.

Andrew‘s simple verbal explanations followed by Jupyter notebook implementations etch concepts in your brain – backed by quizzes and assignments to validate your progress.

Who It‘s Best For

Given Andrew‘s growth mindset teaching style developed via decades training students, this specialization best suits beginners from non-math backgrounds.

The courses do assume basic Python programming experience so absolute coding newbies may want to brush up first. A year of ML experience helps but isn‘t mandatory by any means.

Over 98,000 reviews averaging a perfect 5 stars showcase the quality and positive impact of these beginner-friendly courses.

Commitment & Credentials

With over 25 weeks of content spanning nearly 200 video lectures, you can expect to put it at least 3 hours per week balanced with work/studies.

Each course has graded assignments and quizzes for you to implement new techniques culminating in a final project. Pass all assessments to earn a accredited Deep Learning Specialization certificate from Coursera sharing Andrew Ng‘s brand cache!

The first month is fully refundable in case you change your mind but the ROI is excellent.

👉🏻START Deep Learning Specialization

4. Advanced PyTorch and TensorFlow Courses

Once you‘ve tasted blood, it‘s time to dig deeper into popular AI programming frameworks powering the latest research innovations!

I highly recommend leveling up via these stellar courses focusing on PyTorch and TensorFlow:

A. CS230: Deep Learning by Stanford University

Taught by experts from Professor Andrew Ng‘s Stanford lab, this free 12-week course on YouTube builds on the foundations with:

  • Extremely thorough curriculum spanning CNN, RNN architectures
  • Guided TensorFlow & PyTorch projects perfect for your portfolio
  • Bonus lectures diving into breakthrough research papers 📃

B. Full Stack Deep Learning Bootcamp

Offered by UC Berkeley School of Information, this advanced bootcamp gives stronger math foundation across topics like:

  • Calculus, Linear Algebra refreshed succinctly but clearly
  • Intricacies of neural networks explained visually
  • TensorFlow 2.0 syntax mastery with diverse apps
  • Capstone project on specialization of choice

Together, these courses progress you from intermediate Python fluency to advanced TensorFlow/PyTorch expertise – crucial for delivering research and industry innovation.

It does assume 2+ years of ML experience so work on the beginner courses first before attempting these to maximize comprehension.

Who It‘s Best For

These courses best benefit early-mid career developers looking to pivot or progress into senior technical roles intimately leveraging AI like:

  • Machine Learning Leads
  • Chief AI Architects
  • Heads of Data Science
  • Startup CTOs
  • AI Consultants

Commitment & Credentials

With 8-15 hours of weekly commitment spanning 8-16 weeks, these courses represent serious investment. But the career upside makes it worthwhile!

While free public access or paid certificates are offered, true evaluation will be based on your production-grade projects showcasing mastery.

👉🏻 ENROLL in Advanced Courses

5. Deep Learning Guidebook Series

For those determined to reach PhD-tier expertise in mathematical foundations, look no further than these leading textbooks specifically praised by pioneers like Elon Musk:

A. Deep Learning by Goodfellow, Bengio and Courville

B. Deep Learning with Python by François Chollet

These meticulously crafted guides explain complex concepts around:

  • Calculus, linear algebra, statistics informing algorithms 📏
  • The latest techniques like ResNet variants, generative adversarial networks 🖥
  • Myriad sample implementations via TensorFlow+Keras 📦
  • Specialized domains like computer vision and healthcare ML 🧬

With mathematical notation and programming code setting the context, these books best suit mid-late career professionals like:

  • Aspiring Chief AI Scientists
  • Principal Applied Researchers
  • Future university professors

Be ready to devote 6+ hours per week for 6-12 months with access to an AI workstation for hands-on replication at scale.

Bonus Resources to Boost your Learning 🚀

Beyond courses, here are my top recommendations to accelerate applied mastery:

YouTube Channels
📺 Siraj Raval – Great Explainer of Concepts
📺 Mat Leonard – Notes on Latest Research Papers
📺 StatQuest – Intuitive Math Foundation Guide

Online Communities
💬 FastAI Forums – troubleshoot code issues
💬 Kaggle – participate in machine learning competitions

Apps and Tools
🤖 Google Teachable Machine – Build CV Models Visually
🤖 RunwayML – Launch and Train Models Visually
🤖 PapersWithCode – Implementation references for papers

Job Boards to find AI opportunities
👔 LinkedIn AI/ML Jobs
👔 AngelList – Startups listings

Continue Learning
📚 Subscribe to Deep Learning Weekly
📚 Attend AI Conferences like NeurIPS, ICML

Set a Learning Roadmap
✍️ Define your end goal clearly – research or app dev?
✍️ Estimate timeline to reach confident practitioner level
✍️ Schedule consistent blocks to learn concepts then implement
✍️ Project Codify outcomes w/ GitHub repos to demonstrate and test

Hope these resources have given you the confidence to continue your deep learning education!

Just remember that artificial intelligence rewards perseverance.

Stick with the learning habit even by coding or reading 20 mins per day. Slow consistent progress adds up over months into incredible results!

You‘ve got this! Future you will thank today‘s grit and patience 😉

Leave a comment if you have any other questions as you skill up!

Tags: