Neural Networks that ResNets

Convolutional Neural Networks
About this course: This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: – Understand how to build a convolutional neural network, including recent variations such as residual networks. – Know how to apply convolutional networks to visual detection and recognition tasks. – Know to use neural style transfer to generate art. – Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization.

Who is this class for: – Learners that took the first two courses of the specialization. The third course is recommended. – Anyone that already has a solid understanding of densely connected neural networks, and wants to learn convolutional neural networks or work with image data.

Deep convolutional models: case studies

Learn about the practical tricks and methods used in deep CNNs straight from the research papers.
Learning Objectives
• Understand multiple foundational papers of convolutional neural networks
• Analyze the dimensionality reduction of a volume in a very deep network
• Understand and Implement a Residual network
• Build a deep neural network using Keras
• Implement a skip-connection in your network
• Clone a repository from github and use transfer learning

Subscribe at:
https://www.coursera.org

MIT 6.S094: Convolutional Neural Networks for End-to-End Learning of the Driving Task

This is lecture 3 of course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017. This lecture introduces computer vision, convolutional neural networks, and end-to-end learning of the driving task.

INFO:
Slides: http://bit.ly/2HdXYvf
Website: https://deeplearning.mit.edu
GitHub: https://github.com/lexfridman/mit-deep-learning
Playlist: https://goo.gl/SLCb1y

Links to individual lecture videos for the course:

Lecture 1: Introduction to Deep Learning and Self-Driving Cars

Lecture 2: Deep Reinforcement Learning for Motion Planning

Lecture 3: Convolutional Neural Networks for End-to-End Learning of the Driving Task

Lecture 4: Recurrent Neural Networks for Steering through Time

Lecture 5: Deep Learning for Human-Centered Semi-Autonomous Vehicles

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Hey guys and welcome to another fun and easy machine tutorial on Convolutional Neural Networks.

What are Convolutional Neural Networks and why are they important?

Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self-driving cars.

A ConvNet is able to recognize scenes and the system is able to suggest relevant captions for example (“a girl playing tennis”) while this image shows an example of ConvNets being used for recognizing everyday objects, humans and animals. There are also ConvNets involved in playing games like StarCraft, Mario and Doom.

ConvNets, therefore, are an important tool for most deep learning practitioners today. However, understanding ConvNets and learning to use them for the first time can sometimes be a bit daunting. But don’t worry you are in good hands here at Arduino Startups. If you are new to neural networks in general, I would recommend you check out my lecture on Artificial Neural Networks and then return to this one.

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Continual Neural Networks like twitter (LSTM / RNN) Application by having Keras is that Python

#RNN #LSTM #RecurrentNeuralNetworks #Keras #Python #DeepLearning

In this tutorial, we implement Recurrent Neural Networks with LSTM as example with keras and Tensorflow backend. The same procedure can be followed for a Simple RNN.

We implement Multi layer RNN, visualize the convergence and results. We then implement for variable sized inputs.

Recurrent Neural Networks RNN / LSTM / GRU are a very popular type of Neural Networks which captures features from time series or sequential data. It has amazing results with text and even Image Captioning.

In this example we try to predict the next digit given a sequence of digits. Same concept can be extended to text images and even music.

Find the codes here
GitHub : https://github.com/shreyans29/thesemicolon
Good Reads : http://karpathy.github.io/

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