Hello guys, Welcome to Africa's Largest Video Search Engine, and you are watching Recurrent Neural Networks - Ep. 9 (Deep Learning SIMPLIFIED). and this video is uploaded by DeepLearning.TV at 2015-12-21T11:00:55-08:00. We are just promoting this video only for entertainment and educational purpose only. I hop you like our Video Search Engine. [_aCuOwF1ZjU]
| Name | Recurrent Neural Networks - Ep. 9 (Deep Learning SIMPLIFIED) |
| Video Uploader | Video From DeepLearning.TV |
| Upload Date | This Video Uploaded At 21-12-2015 19:00:55 |
| Video Discription | Our previous discussions of deep net applications were limited to static patterns, but how can a net decipher and label patterns that change with time? For example, could a net be used to scan traffic footage and immediately flag a collision? Through the use of a recurrent net, these real-time interactions are now possible. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv The Recurrent Neural Net (RNN) is the brainchild of Juergen Schmidhuber and Sepp Hochreiter. The three deep nets we’ve seen thus far – MLP, DBN, and CNN – are known as feedforward networks since a signal moves in only one direction across the layers. In contrast, RNNs have a feedback loop where the net’s output is fed back into the net along with the next input. Since RNNs have just one layer of neurons, they are structurally one of the simplest types of nets. Like other nets, RNNs receive an input and produce an output. Unlike other nets, the inputs and outputs can come in a sequence. Here are some sample applications for different input-output scenarios: - Single input, sequence of outputs: image captioning - Sequence of inputs, single output: document classification - Sequence of inputs, sequence of outputs: video processing by frame, statistical forecasting of demand in Supply Chain Planning Have you ever used an RNN in one of your projects before? If so, please comment and tell us about your experience. RNNs are trained using backpropagation through time, which reintroduces the vanishing gradient problem. In fact, the problem is worse with an RNN because each time step is the equivalent of a layer in a feedforward net. Thus if the net is trained for 1000 time steps, the gradient will vanish exponentially as it would in a 1000-layer MLP. There are different approaches to address this problem, the most popular of which is gating. Gating takes the output of any time step and the next input, and performs a transformation before feeding the result back into the RNN. There are several types of gates, the LSTM being the most popular. Other approaches to address this problem include gradient clipping, steeper gates, and better optimizers. GPUs are an essential tool for training an RNN. A team at Indico compared the speed boost from using a GPU over a CPU, and found a 250-fold increase. That’s the difference between 1 day and over 8 months! A recurrent net has one additional capability – it can predict the next item in a sequence, essentially acting as a forecasting engine. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal [_aCuOwF1ZjU] |
| Category | Science & Technology |
| Tags | Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | Recurrent Nets | Recurrent Neural Network | Recurrent Neural Networks | Recurrent Neural Network Tutorial | Recurrent Neural Networks Tutorial | Deep Learning video | Deep Learning Videos | Machine Learning | Artificial Intelligence | AI | artificial neural network | artificial neural networks | GRU | LSTM | Long short term memory | gating recurrent unit | video recognition | time series | forecasting | recurrent neural nets |
