Until a few years back, Deep learning was considered of a lesser significance when contrasted with machine learning. The rise of neural systems and Big Data has made different errands conceivable.
In 2009, Deep learning was just a rising field and just a couple of individuals remembered it as a productive zone of research. Yet, soon it picked up energy and is utilized today for a few applications.
To make it less demanding for you to learn Deep learning, I have curated rundown of youtube recordings, instructional exercises, and courses on Deep Learning. The rundown incorporates talks and instructional exercises from Deep learning summer school, summits, and gathering.
The recordings have been shortlisted for Beginners, intermediates, and specialists in Deep learning.
For all Deep learning/Data science devotees you will love the utilizations of Deep learning and illustrations isolated in a different area. There are recordings on Google Deepmind, figure out how to paint utilizing Deep learning and how Deep learning getting the hang of is making self-driving autos a reality.
Instructional exercises for Beginners on Deep Learning
1.) Tutorial: Deep Learning
This tutorial will introduce the fundamentals of deep learning, discuss applications, and close with challenges ahead.
Deep Learning allows computational models composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection, and many other domains such as drug discovery and genomics.
Deep learning discovers intricate structure in large datasets by using the back-propagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer.
Deep learning methods are representation learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level.
2.) Deep Learning – Computerphile
Google, Facebook & Amazon all use deep learning methods, but how does it work? Research Fellow & Deep Learning Expert Brais Martinez explains in this video.
3.) Deep Learning SIMPLIFIED: The Series Intro – Ep. 1
The goal of this series is to give you a road map with enough detail that you’ll understand the important concepts. his series will bring you up to speed on this fast-growing field – without any of the math or code.
Deep Learning is an important subfield of Artificial Intelligence (AI) that connects various topics like Machine Learning, Neural Networks, and Classification. The field has advanced significantly over the years due to the works of giants like Andrew Ng, Geoff Hinton, Yann LeCun, Adam Gibson, and Andrej Karpathy. Many companies have also invested heavily in Deep Learning and AI research – Google with DeepMind and its Driverless car, nVidia with CUDA and GPU computing.
The focus is on the intuition behind Deep Learning – what it is, how to use it, who’s behind it, and why it’s important. You’ll first get an overview of Deep Learning and a brief introduction of how to choose between different models. After that, we’ll discuss various Deep Learning tools including important software libraries and platforms where you can build your own Deep Nets.
4.) Deep Learning with Neural Networks and TensorFlow Introduction
In this series, the artificial neural network is a biologically-inspired methodology to conduct machine learning, intended to mimic your brain (a biological neural network).
The Neural Network was popularized up until the mid-90s when it was shown that the Support Vector Machine (SVM), using a new-to-the-public technique, the “Kernel Trick,” was capable of working with non-linearly separable datasets. With this, the SVM catapulted to the front again, leaving neural nets behind and mostly nothing interesting until about 2011, where Deep Neural Networks began to take hold and outperform the Support Vector Machine, using new techniques, huge dataset availability, and much more powerful computers.
5.) Wide & Deep Learning with TensorFlow – Machine Learning
Wide & Deep Learning combines the power of memorization and generalization by jointly training wide linear models and deep neural networks. This is an open-source implementation with an easy-to-use API in TensorFlow. It’s effective for generic large-scale regression and classification problems with sparse inputs, such as recommender systems, search, ranking problems and more.
6.) Deep Learning Demystified
In this video instructor Brandon Rohrer explains for deep neural networks with no fancy math, no computer jargon. For slides, related posts and other videos.
7.) Bay Area Deep Learning School Day 1 @ CEMEX, Stanford
Day 1 of Bay Area Deep Learning School featuring speakers Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Ruslan Salakhutdinov and Andrew Ng.
8.) Bay Area Deep Learning School Day 2 @ CEMEX, Stanford
Day 2 of Bay Area Deep Learning School featuring speakers John Schulman, Pascal Lamblin, Adam Coates, Alex Wiltschko, Quoc Le and Yoshua Bengio.
9.) Geoffrey French – Deep learning tutorial – advanced techniques
Some of the more advanced deep learning to help you get the best out of it in a practical setting. The main focus in this video is in computer vision and image processing.
This tutorial is built on the basics, demonstrating some useful techniques that are useful in a practical setting.
Prior knowledge of deep learning, machine learning, linear algebra, a bit of calculus and the NumPy/SciPy stack would be helpful for participation. Here main focus is on using the Theano toolkit along with the Lasagne neural network library.
10.) Google’s Deep Mind Explained! – Self Learning A.I.
Experience the cutting edge of the world around us in a fun relaxed atmosphere.
11.) 9 Cool Deep Learning Applications
Machine learning provides us an incredible set of tools. If you have a difficult problem at hand, you don’t need to hand craft an algorithm for it. It finds out by itself what is important about the problem and tries to solve it on its own. In this video, you’ll see a number of incredible applications of different machine learning techniques (neural networks, deep learning, convolutional neural networks and more).
Note: the fluid simulation paper is using regression forests, which is a machine learning technique, but not strictly deep learning. There are variants of it that are though (e.g., Deep Neural Decision Forests).
12.) Deep Learning Program Learns to Paint
Artificial neural networks were inspired by the human brain and simulate how neurons behave when they are shown a sensory input (e.g., images, sounds, etc). They are known to be excellent tools for image recognition, many many other problems beyond that – they also excel at weather predictions, breast cancer cell mitosis detection, brain image segmentation and toxicity prediction among many others. Deep learning means that we use an artificial neural network with multiple layers, making it even more powerful for more difficult tasks.
This time they have been shown to be apt at reproducing the artistic style of many famous painters, such as Vincent Van Gogh and Pablo Picasso among many others. All the user needs to do is provide an input photograph and a target image from which the artistic style will be learned.
13.) Deep Reinforcement Terrain Learning
In this video, a combination of deep learning and reinforcement learning is presented which has proven to be useful in solving many extremely difficult tasks. Google DeepMind built a system that can play games at a superhuman level using this technique that is also referred to as Deep Q-Learning. This time, it was used to teach digital creatures to walk and overcome challenging terrain arrangements.
This article contains a curated rundown of recordings on Deep learning and reinforcement learning. The recordings have been shortlisted on the premise of the year, see number and significance. It is sufficient of substance on the web and we expected to give the most important recordings.
Experience the rundown and wait-list the recordings which you find reasonable for you. I have attempted to include all the applicable recordings from 2016. In any case, on the off chance that I have passed up a major opportunity for any video which you think merits a specify in the rundown, don’t hesitate to include them underneath in the remarks segments. On the off chance that you are a visual student, let me realize what are your musings on the article.
My point is to help a bigger gathering of people to learn Deep learning. Anticipating your proposals.