Deep Learning

Explore these advanced resources, spanning from in-depth deep learning concepts to university-level courses led by esteemed AI professionals. These materials provide a profound understanding of critical concepts in the fields of ML and AI, with a focus on advanced deep learning techniques.

Courses

• Deep Learning Specialization (opens in a new tab) by Deep Learning.AI, led by Andrew Ng, offers a concise program exploring core deep learning concepts. Dive into hands-on projects, master essential ML principles, and unlock the path to a dynamic career in this exciting field. This specialization comprises five courses: Neural Networks and Deep Learning, Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization, Structuring Machine Learning Projects, Convolutional Neural Networks, and Sequence Models.

• MIT 6.S191: Introduction to Deep Learning (opens in a new tab): Introduces deep learning, covering neural networks, CNNs, and RNNs. It explores supervised and unsupervised learning, optimization, and more. It's designed for students with foundational knowledge in linear algebra, calculus, and probability theory. (website)

• Practical Deep Learning for Coders (opens in a new tab) by Fast.AI, experience comprehensive learning, explained through practical examples and code. Led by experienced mentors, this program equips developers with hands-on skills to excel in deep learning's real-world applications.

• NYU Deep Learning Course (opens in a new tab): NYU's Center for Data Science offers an advanced learning course on deep learning and representation techniques. Taught by Yann LeCun and Alfredo Canziani, it covers the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. (curriculum)

• The Full Stack Deep Learning (opens in a new tab) course is a thorough program covering all aspects of creating AI-driven products using deep neural networks. It covers various topics, such as development infrastructure & tooling, troubleshooting & testing, model monitoring, ML application, and data management, among other key areas.

• Deep Blueberry Book (opens in a new tab): This is a tiny and very focused collection to learn the basics of deep-learning architectures like CNNs, LSTMs, RNNs, VAEs, GANs, DQN, A3C and more.

Explainers

• A Friendly Introduction to Generative Adversarial Networks (GANs) (opens in a new tab): Provides an overview of how GANs work, their key components, and some examples of how they can be used.

• Predictive Learning, NIPS 2016 (opens in a new tab) by Yann LeCun, emphasizes the significance of unsupervised learning in AI model architectures, addressing the current reliance on supervised learning with human-curated data. To advance AI, he highlights the need for machines to learn from raw, unlabeled information like images, videos, and text. The goal is to equip intelligent systems with predictive models and enable them to grasp "common sense" by comprehending the physical world's constraints.

• Denoising and Variational Autoencoders (opens in a new tab): Delves into autoencoders, neural networks specializing in data compression and reconstruction. Focusing on denoising and variational autoencoders, it provides an overview of their operation, components, and real-world applications.

• Restricted Boltzmann Machines (RBM) - A friendly introduction (opens in a new tab): Offers an introduction to Restricted Boltzmann Machines (RBMs), a type of artificial neural network for unsupervised learning. It explains RBM concepts, components, and practical applications, including feature extraction and pattern recognition.

Articles

• Deep Learning in a Nutshell (opens in a new tab): Nvidia's comprehensive four-part series delves into the essence of deep learning. Uncover fundamental concepts in deep learning, explore its historical evolution, training methodologies, delve into Reinforcement Learning, and investigate the nuances of Sequence Learning. The series includes parts on: Core Concepts, Reinforcement Learning, History & Training and Sequence Learning.

Advancements

• GraphCast (opens in a new tab): DeepMind introduces GraphCast, an AI model delivering unparalleled 10-day weather predictions in under a minute. Utilizing Graph Neural Networks and trained on extensive historical and current weather data, it outperforms the High Resolution Forecast system, forecasting cyclone paths, identifying atmospheric rivers, and enabling earlier warnings. (code) (paper in Science) (View GraphCast live on ECMWF) (paper)

• GNoME (opens in a new tab) (Graph Networks for Materials Exploration), developed by DeepMind, predicts structures for over 2 million new materials, including 2.2 million crystals—equivalent to almost 800 years of knowledge. This breakthrough in materials science highlights 380,000 stable materials with potential applications in technologies like improved batteries, solar panels, and computer chips. (paper) (dataset)

Books

• The Little Book of Deep Learning (opens in a new tab) by François Fleuret, simplifies complex aspects of deep learning, such as machine learning, efficient computation, and training, including linear algebra, calculus, probabilities, optimization, signal processing, algorithmic, and high-performance computing.

• Deep Learning (opens in a new tab) by Ian Goodfellow, a comprehensive guide that explores the depths of ML. Written by a renowned expert, practical examples, and an in-depth exploration of the mathematics behind deep learning. Immerse yourself in the world of ML, uncover its real-world applications, and pave the way for a dynamic career in this evolving field.

• Deep Learning with Python (opens in a new tab) by François Chollet, designed for practicality, offering hands-on experiences with essential ML principles, accompanied by practical coding examples and proficiency. Dive into the realm of deep learning, apply it to real-world scenarios, and harness the potential of this dynamic field through coding.

• Dive into Deep Learning (opens in a new tab) is an interactive, comprehensive guide to machine learning, particularly deep learning. It covers math, code, and real data experiments. The book is designed for accessibility, teaching concepts, context, and code. It introduces new concepts through self-contained examples using real datasets and notebooks, both theoretical and practical.