Topic: Deep Learning
This page features most recent and most popular posts on Deep Learning.
Latest posts on Deep Learning
- On-Device Deep Learning: PyTorch Mobile and TensorFlow Lite - Nov 22, 2021PyTorch and TensorFlow are the two leading AI/ML Frameworks. In this article, we take a look at their on-device counterparts PyTorch Mobile and TensorFlow Lite and examine them more deeply from the perspective of someone who wishes to develop and deploy models for use on mobile platforms.
- What Are NVIDIA NGC Containers & How to Get Started Using Them - Nov 15, 2021NVIDIA, the pioneer in the GPU technologies and deep learning revolution, has come up with an excellent catalog of specialized containers that they call NGC Collections. In this article, we explore their basic usage and some variations.
- Deep Learning on your phone: PyTorch C++ API for use on Mobile Platforms - Nov 12, 2021The PyTorch Deep Learning framework has a C++ API for use on mobile platforms. This article shows an end-to-end demo of how to write a simple C++ application with Deep Learning capabilities using the PyTorch C++ API such that the same code can be built for use on mobile platforms (both Android and iOS).
- Dream Come True: Building websites by thinking about them - Nov 11, 2021From the mind to the computer, make websites using your imagination!
- The Common Misconceptions About Machine Learning - Nov 9, 2021Beginners in the field can often have many misconceptions about machine learning that sometimes can be a make-it-or-break-it moment for the individual switching careers or starting fresh. This article clearly describes the ground truth realities about learning new ML skills and eventually working professionally as a machine learning engineer.
Most popular (badge-winning) recent posts on Deep Learning
- Learn To Reproduce Papers: Beginner’s Guide [Gold Blog]Step-by-step instructions on how to understand Deep Learning papers and implement the described approaches.
- Introduction to AutoEncoder and Variational AutoEncoder (VAE) [Silver Blog]Autoencoders and their variants are interesting and powerful artificial neural networks used in unsupervised learning scenarios. Learn how autoencoders perform in their different approaches and how to implement with Keras on the instructional data set of the MNIST digits.
- Introduction to PyTorch Lightning [Silver Blog]PyTorch Lightning is a high-level programming layer built on top of PyTorch. It makes building and training models faster, easier, and more reliable.
- Surpassing Trillion Parameters and GPT-3 with Switch Transformers – a path to AGI? [Silver Blog]Ever larger models churning on increasingly faster machines suggest a potential path toward smarter AI, such as with the massive GPT-3 language model. However, new, more lean, approaches are being conceived and explored that may rival these super-models, which could lead to a future with more efficient implementations of advanced AI-driven systems.
- The Machine & Deep Learning Compendium Open Book [Gold Blog]After years in the making, this extensive and comprehensive ebook resource is now available and open for data scientists and ML engineers. Learn from and contribute to this tome of valuable information to support all your work in data science from engineering to strategy to management.
- 8 Deep Learning Project Ideas for Beginners [Gold Blog]Have you studied Deep Learning techniques, but never worked on a useful project? Here, we highlight eight deep learning project ideas for beginners that will help you sharpen your skills and boost your resume.
- Not Only for Deep Learning: How GPUs Accelerate Data Science & Data Analytics [Gold Blog]Modern AI/ML systems’ success has been critically dependent on their ability to process massive amounts of raw data in a parallel fashion using task-optimized hardware. Can we leverage the power of GPU and distributed computing for regular data processing jobs too?
- Geometric foundations of Deep Learning [Gold Blog]Geometric Deep Learning is an attempt for geometric unification of a broad class of machine learning problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.
- A checklist to track your Data Science progress [Silver Blog]Whether you are just starting out in data science or already a gainfully-employed professional, always learning more to advance through state-of-the-art techniques is part of the adventure. But, it can be challenging to track of your progress and keep an eye on what's next. Follow this checklist to help you scale your expertise from entry-level to advanced.
- Data Science Books You Should Start Reading in 2021 [Gold Blog]Check out this curated list of the best data science books for any level.
- How to deploy Machine Learning/Deep Learning models to the web [Gold Blog]The full value of your deep learning models comes from enabling others to use them. Learn how to deploy your model to the web and access it as a REST API, and begin to share the power of your machine learning development with the world.
- 10 Amazing Machine Learning Projects of 2020 [Silver Blog]So much progress in AI and machine learning happened in 2020, especially in the areas of AI-generating creativity and low-to-no-code frameworks. Check out these trending and popular machine learning projects released last year, and let them inspire your work throughout 2021.
- Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall [Gold Blog]This tutorial discusses the confusion matrix, and how the precision, recall and accuracy are calculated, and how they relate to evaluating deep learning models.
- Approaching (Almost) Any Machine Learning Problem [Silver Blog]This freely-available book is a fantastic walkthrough of practical approaches to machine learning problems.
- Deep learning doesn’t need to be a black box [Silver Blog]The cultural perception of AI is often suspect because of the described challenges in knowing why a deep neural network makes its predictions. So, researchers try to crack open this "black box" after a network is trained to correlate results with inputs. But, what if the goal of explainability could be designed into the network's architecture -- before the model is trained and without reducing its predictive power? Maybe the box could stay open from the beginning.
- Building a Deep Learning Based Reverse Image Search [Silver Blog]Following the journey from unstructured data to content based image retrieval.
- DeepMind’s MuZero is One of the Most Important Deep Learning Systems Ever Created [Gold Blog]MuZero takes a unique approach to solve the problem of planning in deep learning models.
- 15 Free Data Science, Machine Learning & Statistics eBooks for 2021 [Platinum Blog]We present a curated list of 15 free eBooks compiled in a single location to close out the year.
- AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021 [Silver Blog]2020 is finally coming to a close. While likely not to register as anyone's favorite year, 2020 did have some noteworthy advancements in our field, and 2021 promises some important key trends to look forward to. As has become a year-end tradition, our collection of experts have once again contributed their thoughts. Read on to find out more.
- Learn Deep Learning with this Free Course from Yann LeCun [Gold Blog]Here is a freely-available NYU course on deep learning to check out from Yann LeCun and Alfredo Canziani, including videos, slides, and other helpful resources.
- Facebook Open Sourced New Frameworks to Advance Deep Learning Research [Silver Blog]Polygames, PyTorch3D and HiPlot are the new additions to Facebook’s open source deep learning stack.