Free New Resources for Beginners to Learn Deep Learning
Intrigued by encouraging your comprehension of neural networks and deep learning, well beyond the fundamental early on instructional exercises and recordings out there? This post incorporates 10 particular video-based choices for doing only that, all in all comprising of numerous, numerous hours of bits of knowledge. On the off chance that you as of now have some essential information of neural networks, it might be a great opportunity to hop in and handle some further advanced ideas.
Below are the 10 Free New Resources for Enhancing Your understanding of neural networks and deep learning, all in all comprising of numerous, numerous hours of bits of knowledge.
1.) Heroes of Deep Learning Videos ( 7 Heroes)
Below are some of the Videos from Andrew Ng’s Interviews on Deep Learning.
1.) Andrew Ng interviews with Geoffrey Hinton
2.) Andrew Ng interviews Ian Goodfellow
3.) Andrew Ng interviews Yoshua Bengio
4.) Andrew Ng interviews Pieter Abbeel
5.) Andrew Ng interviews Head of Baidu Research, Yuanqing Lin
6.) Andrew Ng interviews Andrej Karpathy
7.) Andrew Ng interviews Director of AI research at Apple Ruslan S
2.) Deep Learning Book Overview Videos
a.) Deep Learning Chapter 1 Introduction presented by Ian Goodfellow
b.) Deep Learning Chapter 6 Deep Feedforward Networks presented by Timothee Cour
3.) Tutorial: Deep Learning for Objects and Scenes – Video series
Learning Deep Representations for Visual Recognition by Kaiming He Deep Learning for Object Detection and Ross Girshick (Facebook AI Research)
Depiction: Deep learning is turning into the main thrust for the visual recognition models in computer vision field.The half-day instructional exercise will concentrate on giving a high-level summary of the recent work on deep learning models for visual recognition of objects and scenes.
The objective is to share a portion of the lessons and encounters learned by the speakers who spent significant time in different subjects of visual recognition. The instructional exercise will be made out of four lectures given by every single one of the speakers. Each address will cover one particular subject of Deep learning for visual recognition, from general deep representation architecture and network interoperability, object detection, scene parsing, to video detection. These four lectures will be bound together into an intelligible instructional exercise of late advance in visual recognition, for the wide groups of onlookers in recognition.
4.) Popular Machine learning & Deep learning videos series (200 Videos)
5.) Deep Learning Summer School Talks
Deep neural networks that learn to represent data in various layers of increasing abstraction have drastically enhanced the best in class for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning finds complicated structure in expansive datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning.
The Deep Learning Summer School (DLSS) is meant for graduate understudies and industrial designers and analysts who as of now have some fundamental information of machine learning (and conceivably yet not really of deep learning) and wish to take in more about this quickly developing field of research.
You can see slides of above video Here.
6.) MIT 6.S094: Deep Learning for Self-Driving Cars – Video series
7.) Intro to Deep Learning (Udacity Nanodegree) – Video series
8.) Deep Learning: Theory, Algorithms, and Applications. Berlin, June 2017
The workshop goes for uniting leading scientists in deep learning and related areas within machine learning, artificial intelligence, mathematics, statistics, and neuroscience. No formal accommodation is required. Members are welcome to introduce their as of late published work and in addition work in advance, and to share their vision and points of view for the field.
9.) Deep Learning For Coders Video Series
10.) NIPS 2016 Spotlight Videos
A standout amongst other approaches to encourage your understanding of what’s happening in neural network research is to see what research is being presented at conferences.These are short, to-the-point spotlight recordings on various research papers and topics introduced at NIPS 2016.