The Best Resources to Start Your Deep Learning Journey

Are you looking to start your journey into the world of Deep Learning but unsure where to begin? With so many resources available online, it can be overwhelming to find the best ones that suit your learning style and goals. To save you time and effort, we have curated a list of some of the top resources that will help you kickstart your Deep Learning journey.

1. DeepLearning.ai – Probably the most popular introductory course right now, developed and taught by DeepLearning.ai in cooperation with Andrew Ng. This course covers all the fundamental principles behind Deep Learning, including how to develop and train models using Python and Tensorflow, as well as real-world case studies. With sub-courses on Neural Networks, Hyperparameter tuning, Convolutional Neural Networks, and more, this course is a great starting point for beginners.

2. MIT Deep Learning Lectures – A set of high-level lectures by MIT that provide a comprehensive overview of the field of Deep Learning. With open-source code alongside video lectures, this resource is ideal for beginners looking to gain a broad understanding of Deep Learning concepts. The lectures feature guest speakers from top companies like Google, Nvidia, and IBM, adding an industry perspective to the material.

3. Deep Learning with Yann LeCun – A combination of video lectures and practicums by Yann LeCun and NYU covering a wide range of topics in Deep Learning. This resource is more mathematically intensive and requires a strong background in calculus, algebra, and probabilities. Topics include convolutional networks, transformers, and graph neural networks, providing a deep dive into advanced Deep Learning concepts.

4. DeepMind x UCL Deep Learning Course – A mathematically rigorous course developed by DeepMind in collaboration with UCL, focusing on state-of-the-art research in Deep Learning. Topics covered include attention and memory in Deep Learning, advanced computer vision models, and responsible innovation in AI. This course is ideal for learners looking to explore cutting-edge advancements in Deep Learning.

5. UC Berkeley Deep Reinforcement Learning Course – A comprehensive course by UC Berkeley that covers fundamental Machine Learning concepts and advanced topics like reinforcement and generative learning. With a focus on building intuition and understanding the math behind Deep Learning, this course is a great choice for those looking for a well-rounded resource.

6. Udemy Deep Learning A-Z Course – A practical course on Udemy that offers hands-on experience with both Tensorflow and Pytorch. This resource is great for tackling real-life problems such as image recognition, stock price prediction, and recommender systems. The organized videos on the Udemy platform make it easy to follow along and apply what you learn to practical projects.

7. “Dive into Deep Learning” Interactive Book – An interactive deep learning book that covers a wide range of topics and provides code examples in Tensorflow, Pytorch, and Mxnet. This resource is great for learners who prefer a text-based approach and want to use it as a reference handbook for Deep Learning concepts.

In addition to these courses and books, we also recommend checking out “Grokkings Deep Learning” by Andrew Trask, “Deep Learning with PyTorch” by Eli Stevens, Luca Antiga, and Thomas Viehmann, and “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. These resources provide in-depth knowledge and practical insights into the world of Deep Learning.

Remember, learning Deep Learning is a journey that requires dedication and practice. By exploring these curated resources, you can gain a solid foundation in Deep Learning and accelerate your learning process. Happy learning!