Imagine this: You’ve been spending your evenings scrolling through social media, when you keep hearing the buzzwords – deep learning, artificial intelligence, machine learning – thrown around like confetti. It’s got you wondering, “What are these mystical terms all about? Can they really improve my career prospects?” Well, dear reader, not only do we understand that curiosity, we’re excited to help you unravel the mystery of deep learning!
Embarking on the journey to mastering deep learning can seem intimidating at first, but worry not – we’ve got your back. In today’s post, we’ll share some of the best deep learning online courses available, designed for beginners and experts alike, to help you navigate the exciting world of AI and build the career (or even just the hobby) of your dreams. Get ready to put on your thinking caps, because we’re about to dive headfirst into the fascinating realm of deep learning.
Deep Learning Courses – Table of Contents
- Deep Learning A-Z™ 2023: Neural Networks, AI & ChatGPT Bonus
- A deep understanding of deep learning (with Python intro)
- TensorFlow Developer Certificate in 2023: Zero to Mastery
- Unsupervised Deep Learning in Python
- Complete Tensorflow 2 and Keras Deep Learning Bootcamp
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Deep Learning A-Z™ 2023: Neural Networks, AI & ChatGPT Bonus
Platform:
Udemy
Rating:
4.6 out of 5
Artificial Intelligence has shown significant growth over the years, and Deep Learning A-Z aims to equip learners with knowledge on Supervised and Unsupervised Deep Learning by providing a robust structure for mastering this complex field. The course is structured into two volumes, each focusing on three unique algorithms. A key feature of the course is its Intuition Tutorials, which help learners develop an intuitive understanding of Deep Learning algorithms. This approach allows for hands-on coding exercises that are meaningful, engaging, and enlightening, making the entire learning experience a game-changer.
Deep Learning A-Z showcases a number of real-world challenges that learners get to work on, setting it apart from courses that only make use of outdated datasets. It covers six practical challenges, including solving a Customer Churn problem using Artificial Neural Networks, Image Recognition, and Stock Price prediction with Recurrent Neural Networks. Tools integrated within the course include TensorFlow and Pytorch, two popular open-source libraries for Deep Learning. The course targets both beginners and experienced learners of Deep Learning, providing opportunities to build skills from the ground up or refresh and broaden existing knowledge.
Skills you’ll learn in this course:
- Developing an intuitive understanding of Deep Learning algorithms
- Applying Artificial Neural Networks to solve customer churn problems
- Using Convolutional Neural Networks for Image Recognition
- Implementing Recurrent Neural Networks to predict stock prices
- Utilizing Self-Organizing Maps for fraud detection
- Creating recommender systems with Boltzmann Machines
- Implementing Stacked Autoencoders for complex challenges
- Gaining hands-on experience with Python, TensorFlow, PyTorch, Keras, and other essential tools
A deep understanding of deep learning (with Python intro)
Platform:
Udemy
Rating:
4.8 out of 5
Looking to dive deep into the world of deep learning? This comprehensive course is designed for those who genuinely want to understand the ins and outs of deep learning, rather than just getting a quick overview. Throughout the course, you will gain flexible, fundamental, and lasting expertise, equipping you with a solid understanding of the concepts that will enable you to learn new topics and trends as they arise in the future. Just a heads up: this course is not for those seeking superficial knowledge from a YouTube video. Instead, it’s for individuals who genuinely want to grasp how and why deep learning works, and how to apply this knowledge to various scenarios.
The course has a lot to offer — from covering theoretical and mathematical aspects to diving into the implementations of deep learning models using Python with the PyTorch library. You’ll also get multiple explanations for concepts like transfer learning, generative modeling, convolutional neural networks, feedforward networks, GANs, and so much more. What’s more, you’ll be able to master Python through an 8+ hour tutorial, learn how to use Google Colab, and participate in numerous exercises and projects that will fortify your deep learning expertise. With clear explanations, interactive Q&A forums and visualizations to help build your intuition, it’s time to dive into the fascinating world of deep learning. So go ahead, check out the course introductory video, and see if this transformative journey is the right fit for you!
Skills you’ll learn in this course:
- Deep understanding of fundamental concepts in deep learning
- Selection and usage of metaparameters like optimizers, normalizations, and learning rates
- Evaluation of deep neural network model performance
- Modification and adaptation of existing models to solve new problems
- Python programming skills
- Implementation of deep learning models using PyTorch library
- Usage of Google Colab for running Python code and simulations
- Transfer learning, generative modeling, and various neural network architectures
TensorFlow Developer Certificate in 2023: Zero to Mastery
Platform:
Udemy
Rating:
4.6 out of 5
Ready to dive into the world of TensorFlow and become a certified expert? Look no further, as this comprehensive online course will transform you from an absolute beginner into a state-of-the-art deep learning neural network pro. Taught by a certified TensorFlow expert, the course is designed to help you pass the TensorFlow Developer Certificate exam, so you can officially join Google’s TensorFlow Certification Network, making your resume stand out and gaining access to a fantastic live online community with over 900,000 students!
Throughout the course, you’ll experience hands-on and project-based learning, covering everything from TensorFlow fundamentals to advanced neural networks, computer vision, natural language processing, and time series forecasting. Not only will you build machine learning models and projects mimicking real-life scenarios, but you’ll also learn from scratch and gain essential skills needed to develop modern deep learning solutions encountered by big tech companies. So, if you’re ready to join the rapidly growing machine learning industry and earn up to $204,000 USD a year as a TensorFlow expert, enroll now and start your journey towards becoming a Google Certified Developer!
Skills you’ll learn in this course:
- Building and training deep learning neural networks with TensorFlow
- Developing skills to diagnose and solve regression and classification problems
- Creating computer vision and convolutional neural networks
- Utilizing transfer learning and fine-tuning pre-trained models
- Preprocessing and handling natural language text for neural networks
- Building and evaluating models with RNNs, LSTMs, GRUs, and CNNs for NLP tasks
- Understanding and applying time series fundamentals in TensorFlow
- Practical implementation through hands-on projects, including Food Vision and SkimLit.
Unsupervised Deep Learning in Python
Platform:
Udemy
Rating:
4.7 out of 5
Are you ready for the next step in the deep learning, data science, and machine learning series? This course is perfect for you. Delving into unsupervised deep learning, you’ll start by exploring principal components analysis (PCA) and t-SNE (t-distributed stochastic neighbor embedding), both popular techniques for dimensionality reduction. After mastering these concepts, prepare to dive into the world of autoencoders – a special type of unsupervised neural network. From understanding how autoencoders work to forming a deep stack of autoencoders for better supervised deep neural network performance, you’re about to gain some serious deep-learning knowledge.
But wait, there’s more! You’ll also study restricted Boltzmann machines (RBMs), another popular unsupervised neural network. You’ll learn about Gibbs sampling, a special case of Markov Chain Monte Carlo, and Contrastive Divergence or CD-k methods. Finally, discover how PCA and t-SNE can be used to visualize the features learned by autoencoders and RBMs to find patterns even without labels. This course does require prior understanding of calculus, linear algebra, and Python coding, as well as knowledge of Numpy, Theano, and Tensorflow. Aimed at those interested in more than just using a library/API, this course focuses on building and understanding deep learning models from scratch, giving you a thorough and in-depth understanding of the concepts involved.
Skills you’ll learn in this course:
- Principal components analysis (PCA)
- t-SNE (t-distributed stochastic neighbor embedding)
- Autoencoder understanding and implementation
- Restricted Boltzmann machines (RBMs) and pretraining
- Gibbs sampling and Contrastive Divergence (CD-k)
- Understanding and minimizing free energy
- Visualization and feature interpretation using PCA and t-SNE
- Building and understanding deep learning models from scratch
Complete Tensorflow 2 and Keras Deep Learning Bootcamp
Platform:
Udemy
Rating:
4.7 out of 5
In this comprehensive course, you’ll be guided through using Google’s TensorFlow 2 framework to create artificial neural networks for deep learning in an easy-to-understand manner. Focused on leveraging the Keras API (TensorFlow 2.0’s official API) for quick model building, the course covers various application scenarios such as forecasting home prices, classifying medical images, predicting future sales data, and even generating artificial text. Balancing theory and practical implementation, the course offers complete Jupyter notebook guides, reference slides, notes, and exercises to test your skills along the way.
The curriculum covers a wide range of topics, including a crash course on NumPy, Pandas data analysis, and Data Visualization, as well as diving into the basics of Neural Networks, TensorFlow, and Keras Syntax. You’ll also explore advanced topics such as Convolutional Neural Networks, Recurrent Neural Networks, AutoEncoders, and Generative Adversarial Networks (GANs), preparing you to deploy TensorFlow into production. By using Keras, you’ll benefit from the user-friendly API standard for machine learning and TensorFlow 2’s enhancements, like eager execution and scalable input pipelines through tf.data. Join this course and become a deep learning expert today!
Skills you’ll learn in this course:
- Master TensorFlow 2 framework and Keras API
- Forecast future price homes with built models
- Classify medical images with deep learning algorithms
- Create models to predict future sales data
- Generate complete new text artificially
- Understand Neural Network Basics
- Deploy TensorFlow models into production
- Implement Convolutional Neural Networks, Recurrent Neural Networks, AutoEncoders, and GANs.
And so, dear reader, that brings us to the end of our journey through deep learning online courses. After diving into the various options available, it’s evident that there is no shortage of resources for those eager to expand their knowledge in this fascinating field. From beginner to advanced courses, free to paid, and varied teaching methods, the online world offers something for everyone.
As you venture deeper into the world of deep learning, remember to keep an open mind and savor the learning experience. Embrace the challenges that come with mastering new skills and appreciate how far you’ve come. After all, continuous learning and growth are the keys to success in any field. Along the way, don’t forget to share your knowledge, collaborate with others, and stay curious. Who knows? Your newly-acquired deep learning expertise might just unlock new doors and shape the future of technology. Happy learning!