Deep Learning is one of the cornerstones of modern Artificial Intelligence advancements. The increasingly powerful computation machines called GPUs have made it possible to build vision and language models that in some (a lot) instances outperform humans. The free availability of GPU resources in the cloud has democratised (a little bit) the access to this technological revolution of our days and has bestowed upon everyone the power to develop systems that solve daily problems, enhance their business capabilities or even nurture one's soul by creating fantastic pieces of art. Language models such as GPT-3 and BERT, diffusion models such as DALL-E and computer vision models such as Visual Transformers have really had a huge impact in the field demonstrating the immense possibilities of AI and specifically Deep Learning. No doubt Deep Learning is one of the hottest topics in AI right now.

After multiple online meetings and virtual conversations, I've learned there are many ways people are dealing with suddenly working from home. I would categorize a really low desire as, "I don't want to start anything new, let's just try to get through this." And a really high desire as, "I have more free time than I used to, I should learn something new!" If and when you are looking to learn new things, I've compiled a list of deep learning resources. Below is a range of deep learning resources that can take anywhere from 5 minutes to 3 hours depending on what you're looking for.

From using a simple web cam to identify objects to training a network in the cloud, these resources will help you take advantage of all MATLAB has to offer for deep learning.

This is a list of resources I think would be useful for those who are just starting to explore the amazing Machine Learning domain of Computer Science and want to learn more about Neural Networks and their applications. The general idea behind putting these resources together and publishing this list is that when I just started I saw posts with hundreds of links without description and I simply didn't know which of them are worth spending time on. Focusing on most useful ones and giving short summaries instead is a good idea. I am not a Deep Learning expert and everything I wrote down is just my personal experience with these resources, very subjective opinion. In-depth Convolutional Neural Networks course highly recommended if one wants to learn about image recognition, Computer Vision-related problems and so on. The problemset is amazing; it has probably the best numpy tutorial I have ever seen and makes people implement algorithms they saw in lectures in pure Python numpy, which seems to be a great idea as it helps to get better understanding of how everything actually works.

If you passed high school math and can hack around in Python, I want to teach you Deep Learning. Edit: 50% Coupon Code: "mltrask" (expires August 26) I've decided to write a Deep Learning book in the same style as my blog, teaching Deep Learning from an intuitive perspective, all in Python, using only numpy. I wanted to make the lowest possible barrier to entry to learn Deep Learning. The Problem with most entry level Deep Learning resources these days is that they either assume advanced knowledge of Calculus, Linear Algebra, Differential Equations, and perhaps even Convex Optimization, or they just teach a "black box" framework like Torch, Keras, or TensorFlow (where you just hit "train" but you don't actually know what's going on under the hood). Both have their appropriate audience, but I don't believe that either are appropriate for your average python hacker looking for a 101 on the fundamentals.

If you passed high school math and can hack around in Python, I want to teach you Deep Learning. Well folks, I've decided to write a Deep Learning book in the same style as my blog, teaching Deep Learning from an intuitive perspective, all in Python, using only numpy. I wanted to make the lowest possible barrier to entry to learn Deep Learning. The Problem with most entry level Deep Learning resources these days is that they either assume advanced knowledge of Calculus, Linear Algebra, Differential Equations, and perhaps even Convex Optimization, or they just teach a "black box" framework like Torch, Keras, or TensorFlow (where you just hit "train" but you don't actually know what's going on under the hood). Both have their appropriate audience, but I don't believe that either are appropriate for your average python hacker looking for a 101 on the fundamentals.