Deep Learning for Robotics

Editor's Notes

• #2 A little background of who we are, what we do.
• #4 Like to see a show of hands: who has heard of some or most of these terminologies who uses DL for their day-to-day work?
• #5 In this talk, we will cover several topics as follows. Start with …. After each of the topics, I will be happy to pause the take any questions you have.
• #6 In the last four years, deep learning has made its way into the heart of almost every AI application. And across all these domains, deep learning architectures have been very successful, blowing away the competition. On the surface, it seems almost unreasonably effective across these domains. First image: Tumor positive, negative
• #7 End-to-end object localization system. Model on the left is able to learn to map each of the pixel into a category. Model on the right is able to generate bounding boxes for the precise location of the detected objects.
• #8 Video recognition using 3D convolution.
• #9 End to end Speech Recognition using Deep Learning Why is this cool? - ASR systems around for a long time - End to end means starting with raw spectrogram data, a deep neural net can produce text - without any hand-engineering of features - network learns features from data
• #10 Combine Deep network with RL, a network is able to learn to play games from scratch. Similar technology being developed further by DeepMind became the algorithm behind AlphaGo.
• #11 Things like HOG and SIFT features. Nearest neighbor clustering, L2 distance. Able to replace the nonlinear classifier by a linear classifier, because the multi layer model is nonlinear.
• #12 Historical perspective Input → designed features → output Input → designed features → SVM → output Input → learned features → SVM → output Input → levels of learned features → output One key features of DL is that the model tends to be a end-to-end system, in the sense that, for example, if we want to classify an image, we pass the NxN raw pixels directly to a deep neural network as input, and we provide the desired output labels, and if all goes well, the model will automatically find the right features from the data. * conceptual shift in thinking, from: "how do you engineer the best features?" to "how do you guide the model towards finding the best features? but many old practices from machine learning still apply! The end-to-end nature of deep learning allows it a wide range of applicability. One could substitue videos instead of images, for example – or speech, or text, .. The output could be object identity, hair style, or a set of optimal moves in a video games.
• #14 With DL: Features are learned from data rather than hand-engineered - at multiple levels of representation - L1 lines/bands - L3 intricate patterns (honeycomb) - L5 faces A method for extracting features at multiple levels of abstraction Features are discovered from data Performance improves with more data Network can express complex transformations High degree of representational power
• #16 Here are just some of the examples where the Nervana platform has been applied to real-world problems such as Detecting tumors in healthcare, counting plants in agricultural robotics, finding oil rich regions in seismic data, building better speech interfaces in cars, building a timeseries search engine for finance, and engineering better organisms through amino acid sequence analysis
• #17 In this talk, we will cover several topics as follows. Start with …. After each of the topics, I will be happy to pause the take any questions you have.
• #18 The DL progress on computer vision obviously help robotic vision as well. For models that can do image segmentation and object localization, they definitely help a robot or an agent with scene understand and navigate around an environment.
• #21 Inevitably, the natural language processing is an instrumental piece as well. A module as part of the robot should able to understand human command and execute on the command
• #27 In this talk, we will cover several topics as follows. Start with …. After each of the topics, I will be happy to pause the take any questions you have.
• #29 As one network approximating the Q value, and output layer represents values for each action, the algorithm deals with discrete and finite-set of actions only.
• #32 Code makes it look real
• #34 In this talk, we will cover several topics as follows. Start with …. After each of the topics, I will be happy to pause the take any questions you have.
• #39 So where do the various frameworks match on to these attributes?
• #40 Training is the pain point, and we are 2-3x faster than any other framework out there. That is today; when are new chip comes out we are going to be 10-20x faster than the nearest competitor. Models that originally would take you 1 month to train will take <2 days to train. This is a gamechanger. Training speed = performance.. The more iterations you can fit the better tuned your model will be for performance. In order to leverage this speed, you need resources for both training and inference. There is a reason the industry is moving to a cloud solution – it is much more cost and labor-efficient then trying to build an internal on-premise compute cluster, especially one that can scale with your computing and inference demands and return results with low latency.For example, image you wanted to deploy 100 trained models – you can buy 100 GPU boxes for each model to do inference, or you can use a DL cloud solution like us to flexibly handle the resources and scale with demand. We also provide enterprise-level customer support, which is actually very important. You are paying us; if you run into issues getting your model to train or have a bug fix that is needed for your model, we are here to provide fast support and guidance. Or if you need a speed-up and there is a GPU kernel we can write that can increase your speed by 50%, we are here. Good luck submitting an issue to an open-source framework such as caffe or waiting for Google to get back to you. This will save customers a lot of time and headache in the long run, especially folks that are relatively new to deep learning.
• #43 don’t have to make a model from scratch - many examples of pre-trained models mention Yinyin-Fast-RCNN, Sathish C3D, babI