Recent advances in neural networks have enabled computers to better see and understand the world. They can recognize school buses and zebras and can tell the difference between multi-stereers and your absurd terriers. We now know what it takes to train these neural networks well, but we don't know so much about how they're actually computing their final answers. We developed this interactive deep visualization toolbox to shine light into these black boxes, showing what happens inside of neural nets. In the top left corner, we show the input to the network, which can be a still image or video from a webcam. These black squares in the middle show the activations on a single layer of a network, in this case the popular deep neural network called AlexNet running in cafe. By interacting with the network, we can see what some of the neurons are doing. For example, on this first layer, a unit in the center responds strongly to light dark edges. Its neighbor, one neuron over, responds to edges in the opposite direction, dark to light. Using optimization, we can synthetically produce images that light up each neuron on this layer to see what each neuron is looking for. We can scroll through every layer in the network to see what it does, including convolution, pooling, and normalization layers. We can switch back and forth between showing the actual activations and showing images synthesized to produce high activation. But the time we get to the fifth convolutional layer, the features being computed represent abstract concepts. For example, this neuron seems to respond to faces. We can further investigate this neuron by showing a few different types of information. First, we can artificially create optimized images using new regularization techniques that are described in our paper. These synthetic images show that this neuron fires in response to a face and shoulders. We can also plot the images from the training set that activate this neuron the most, as well as pixels from those images most responsible for the high activations, computed via the decongolution technique. This feature responds to multiple faces in different locations. And by looking at the decong, we can see that it would respond more strongly if we had to see the decong, more strongly if we had even darker eyes and rose your lips. We can also confirm that it carries about the head and shoulders that ignores the arms and torso. We can even see that it fires to some extent for cat faces. Using back prop or decong, we can see that this unit depends most strongly on a couple units in the previous layer con 4, and on about a dozen or so in con 3. Now let's look at another neuron on this layer. So what's this unit doing? From the top 9 images, we might conclude that it fires for different types of clothing. But examining the synthetic images shows that it may be detecting not clothing per se, but wrinkles. In the live plot, we can see that it's activated by my shirt, and, smoothing out half of my shirt, causes that hack of the activations to decrease. Finally, here's another interesting neuron. This one has learned to look for printed text in a variety of sizes, colors, and fonts. This is pretty cool because we never asked the network to look for wrinkles or text or faces. The only labels we provided were at the very last layer, so the only reason the network learned features like text and faces in the middle was to support final decisions at that last layer. For example, the text detector may provide good evidence that a rectangle is in fact a book seen on edge, and detecting many books next to each other might be a good way of detecting a bookcase, which was one of the categories we trained in that to recognize. In this video, we've shown some of the features of the DeepViz toolbox, and a few of the things we've learned by using it. You can download the toolbox of this URL and explore for yourself. If you'd like to share what you find, you can use the hashtag deepviz. Thanks for listening, and we look forward to seeing what you discover.