As we gaze at the ventures of the developing research and application of neural networks, our horizons are plotted with many a cat, spaceship, and cucumber. Ventures begin to delve into a preponderance of sorts which can be seen by the computer mind, curious. One can find themselves quite deep in layers, merely sorting the wonderful assistance that neural networks afford computer vision — weeks to hours of training that may seem intensive continues to be shifted and streamlined as more involvement and attention arises.
Much as these components layer our knowledge we begin to correlate again with that of the neural paths, from step to step, we resolve in each a point of success that extends our understanding, as do the neurons of the nets we rather enjoy. Each moment, but a bright occurrence that evolves and begins to solve. From this, we in ourselves, now, layer, the components of each of these in which we see to occur to keep relaying our understanding, not only of the machine but of self. The most brilliance of all, these matters, maybe the reflectivity such provides, in needing to map these creations.
Seeing that convolution nets build upon a keen approach to classifying, yet adding in memory through recursion, applying layers of such through repetitive training, and diving deeper with generative models. As we layer the neurons, inform the frameworks and apply these thoughts, much as our own, the solutions start to become compositions in varying form.
What is Seen
In many forms of artistic expression, whether its music, fine, or performance alike reveal each a nuance — dependent variables of the senses that are innate and present, whether attempted or rather derived through iteration. It is in which we juxtapose say a coloring book to a work by Van Gogh, in which we see the layers of mastery unfold. Separate the works are both expressive, disregarding novel, are true, and present their own signature of cadence.
From step to step, again we see these outputs as rather understood, we need not relay a way in which applied nuances of the structure are employed to derive this, though we can weigh the outputs independently. Your fridge, lined with not hand turkeys or other sentiments, may bear a different sense to an appreciation for craftsmanship, in this which we gather and relatively appreciate each as their own.
In this, it may be particularly interesting to look at the microscope upside down, or in a sense see what the computer sees, as the viability of comprehension, almost relies on it.
Inceptionism: Going Deeper into Neural Networks
Update - 13/07/2015 Images in this blog post are licensed by Google Inc. under a Creative Commons Attribution 4.0…
Take this example for instance as a rather litmus to the experience. By looking at the output of classification, or simply testing the networks ability to recreate in which we suggested to it, we can then measure its response as accuracy. Similarly, to how we sort the early drawings to masters’ works, we can then reflect in understanding in ways a part of the machine forms attention in certain complexities and reforms the creations.
It is important to note, how in which this is presented, as if all of these pieces were perfectly sorted we may draw a different understanding and merely accept the approaches at hand, but no, the iteration is meant to be understood, as it is throughout life, and in neural paths, that these moments are creations in which we must sift/refine/explore to further their ability.
AS a dynamic approach, this example brings new sorts of thinking to how one can use these technologies and begin to understand means for application.
Is Google Tensorflow Object Detection API the easiest way to implement image recognition?
Doing cool things with data!
It also nicely considers how we solve in these spaces, as different models/frameworks/datasets/hardware, all have reasonable contributions. If we are looking at these each as a component of total weight, we must understand them individually, applying the means of solving, and then deciphering how in which we do as a collective calculation to our approach.
What is Heard
As our culture shifts, inherently do the preference for receiving information. The outnumbering stack of podcasts, to that of listeners, can be a sought idea in this form. Reflective of preference, an occurrence that is not always on the sought bias but more so that of cognitive balance, seen as our own mind perceives the world alone or collectively.
With the increase of noise and stimuli across all components of our worlds, an increasing preference for one's own experience becomes an important construct. Mentioning this, we see the technological shifts around such occurrences foster these environments of change, from music to information, alike these experiences are now meant for ourselves. Satirical mentions of walking around with face obtrude or other matters, people imagine this future often as subtractive from an overall consumption. Though in many ways, with the time we have, one could see it as focused.
//The application of this context then inferences the enjoyment of additional texture, that in which the layer of appreciation for classical methods is then increased, due to accessibility of placement and spectacle, there is a return to traditional. The elasticity of preference seems to occur upon these strings, as does the next mention.
Together we have a group and isolated experiences, both in which represent their own developments of character, yet important to considered and experience as well. All of this, of what becomes a scope of understanding, or more importantly how we learn to balance and provide richer versions of both sets. Thinking about these experiences we can consider elements of this user story approach, which provides insight into how we all share collective approaches to identifying intriguing solutions in this manner.
To explore more around human-centered design, interfaces of the future, and the experiences around these solutions that are important and bring more value in these settings, you can see the full approach here:
Applying human-centered design to emerging technologies
VR, AR, and digital assistant present exciting opportunities for the future, but how can we ensure we’re designing for…
Considering the human-centered and the elements outside of visual imagery we come to understand that the auditory persuasion bears its own. That in which is modularized by the brain and composited in different subsets. A layer for measuring experiences that are heard, as seen, auditory imagery. This brings forth importance in a correlation of something mentioned earlier as viewing the entire composition semantically, but more so in this sense, we want to focus on where in which we see such experiences arising and how machine intelligence relates. Understanding the auditory process has its own set of phenomena we can explore how to approach this with the evolution of the musical liking.
Sounds are a curious sensation of the human experience, imparticular to our own, though collectively interwoven into the mind in a way in which is. From vibration, we experience the subtleties of correlation, the notes, the sound of a voice that aligns each to a similar, but their own in a way we perceive. What we experience from such a manner is that of this auditory imagery we experience and its encoding into our mind.
Earning a rapport with this sense, we can fathom its depth and begin to build from it, though nature will show in which some may not have been granted such, or along the way develop less of an instinct for the science. What we can learn, is that each of these voice/instrument alike is objects in which we craft these combinations of note to achieve an orchestrated solution. Along the path, we may as well begin to understand how this preference has grown out of what can be understood as the “production effect”, through instinctually aligning, audio with the visual contributions and practice over time to contribute to further retention. As input layers, the auditory form, and repeated experience works as an RNN to capture these contributions over time.
Understanding a manner in which these vibrations occur, that sound creates imagery, and alike they compare to build compositions in which we can naturally devise — forms of creation, pleasant in their own, to our nature, affords us an opportunity to perceive them, various modules. It is in these means, in which we begin to gather how we can perceive such in different measures.
Beethoven for contrast learned such early, and adapted his form, to vigorously redefine a way in which he could endure. From plotting these mental notes, images, of auditory experiences, in his early compositions, could then use the vibration of these pieces of knowledge and their construction, to write after the loss of his sense. We have subjects such as the 5th symphony to understand that as these vibrations resonate, they build upon layers in our mind.
As these existing principles align, the development and application, develop, towards that of digital sequences, often a reference to a sense of dynamically constructed harmony in which, the notes play upon our natural sense of appreciation. We see MIDI notes as a means of aligning the machine intelligence with the musical component of this understanding. With alignment in the arrangement and explored exposure, we can see that these frameworks not only begin to understand but interpret their own auditory measures as well.
Composing Music With Recurrent Neural Networks
(Update: A paper based on this work has been accepted at EvoMusArt 2017! See here for more details.) It's hard not to…
As this example explores, relational models, between notes and nodes, we can see that individual networks themselves become the weighted dependencies in which transcribe the utterances at be. We can see in these instances, the understanding of substance, in this manner, music, applies to the architecture of the model in which we solve. The velocity in which each note approaches carries a cadence transcribed by the nodes, their layering, and the model in which defers each to the next recursively, until the output layer in which manages the final measures. Applying LTSM ( Long Short Term Memory) in this case carries each layer as a sift for the next, as in image classification, the CNN becomes particularly more accurate and develops a higher level through each iteration, so do the RNN in this application, instead of forwarding the reference through to each step.
We can see in this example how sequences of occurrence become learned in the RNN sense with the viability of LTSM in shifting the gradients and applying new functions to perform such a task. The application of Restricted Boltzmann Machine (RBM) methods in concurrence with the RNN structure sorts the variables to connect and define the generation of notes across the neuron path. This model considers visible and hidden variables that connect to define the patterns in which envelope upon the gradient graphs to structure to the interpretation of the inputs. In this means, music theory is taught through stochastic models of variance in which begins to develop the machine's perception of bias towards notes/intervals/harmony as it develops.
Deeper into Neural Networks (-), Object Detection API (-), Human-centered design for emerging technologies [highlight video](-), Human-centered design for emerging technologies [article] (-), Production effect (-), Composing music with recurrent neural networks (-), Modeling temporal dependencies in high level sequences (-), Restricted Boltzmann Machine (-)