The following video presents What is a Neural Network – Ep. 2 (Deep Learning SIMPLIFIED):
“With plenty of machine learning tools currently available, why would you ever choose an artificial neural network over all the rest? This clip and the next could open your eyes to their awesome capabilities! You’ll get a closer look at neural nets without any of the math or code – just what they are and how they work. Soon you’ll understand why they are such a powerful tool!…
Deep Learning is primarily about neural networks, where a network is an interconnected web of nodes and edges. Neural nets were designed to perform complex tasks, such as the task of placing objects into categories based on a few attributes. This process, known as classification, is the focus of our series….“
The following video presents 3 reasons to go Deep – Ep. 3 (Deep Learning SIMPLIFIED):
“With so many alternatives available, why are neural nets used for Deep Learning? Neural nets excel at complex pattern recognition and they can be trained quickly with GPUs.
Historically, computers have only been useful for tasks that we can explain with a detailed list of instructions. As such, they tend to fail in applications where the task at hand is fuzzy, such as recognizing patterns. Neural Networks fill this gap in our computational abilities by advancing machine perception – that is, they allow computers to start to making complex judgements about environmental inputs. Most of the recent hype in the field of AI has been due to progress in the application of deep neural networks.
Neural nets tend to be too computationally expensive for data with simple patterns….. As the pattern complexity increases, neural nets start to outperform other machine learning methods. At the highest levels of pattern complexity – high-resolution images for example – neural nets with a small number of layers will require a number of nodes that grows exponentially with the number of unique patterns. Even then, the net would likely take excessive time to train, or simply would fail to produce accurate results…..
As a result, deep nets are essentially the only practical choice for highly complex patterns such as the human face. The reason is that different parts of the net can detect simpler patterns and then combine them together to detect a more complex pattern. For example, a convolutional net can detect simple features like edges, which can be combined to form facial features like the nose and eyes, which are then combined to form a face (Credit: Andrew Ng). Deep nets can do this accurately – in fact, a deep net from Google beat a human for the first time at pattern recognition.
However, the strength of deep nets is coupled with an important cost – computational power. The resources required to effectively train a deep net were prohibitive in the early years of neural networks. However, thanks to advances in high-performance GPUs of the last decade, this is no longer an issue. Complex nets that once would have taken months to train, now only take days.“
This is an update to the previous blog, Sony’s Neural Network Libraries are Available in Open Source (No Cost) for Creating Deep Learning Programs for Artificial Intelligence: Applications Include Image & Voice Recognition, Machine Translation, Signal Processing and Robotics.
Sony’s Neural Network Console is a deep learning tool for training, evaluating, and designing neural networks.
- In June 2017, Sony released its Neural Network Libraries as open source software.
- Recently, Sony released an app, Neural Network Console V1.0.0 for Windows 8.1/10_64bit, to make deep learning even more intuitive. It is hoping to spur more deep learning-related development in the industrial world by providing this newly developed console software that can use its GUI (graphical user interface) to operate the core libraries as well as design, train and evaluate neural network models.
The key highlights of Sony’s Neural Network Console include:
- Hands-on: Start using deep learning with an intuitive user interface.
- Drag and drop for trial and error: Use a rich variety of neural layers to design your cutting-edge network. A comfortable UI will help you implement new ideas instantly.
- Automatic Structure Search: Tired of fine-tuning your network by hand? Neural Network Console can automatically search for a lightweight, high-performance neural network structure for you.
- Train and examine with a click of a button: After designing a network, training the network using our Neural Network Libraries is a simple click away. View the progress and performance in real time.* The Neural Network Libraries are the core libraries of the Neural Network Console.
- Manage Your Training History: Browse your history of the neural networks you have trained. View the performance of each network at a glance.
The following video presents Introduction of Neural Network Console:
“A deep learning tool for training, evaluating, and designing neural networks. Start using deep learning with an intuitive user interface.“
Sony Unveils Neural Network Console, an Integrated Development Environment Using Deep Learning for AI Creation
Visualizing Neural Networking Structures for Efficient Program Creation
Sony Network Communications Inc.
Tokyo – August 17, 2017 – Sony Corporation today announced that it has made its Neural Network Console software, which provides an integrated development environment for creating deep-learning based programs, available free of charge. Program engineers and designers will now be able to make use of this software, which comes with a full-fledged GUI (graphical user interface), to develop deep learning programs and incorporate them into their products and services, all the while taking advantage of its intuitive user interface to efficiently carry out neural network design, learning, and evaluation. Following Sony’s move in June 2017 to make its core libraries (Neural Network Libraries) available in open source, it is hoping to spur more deep learning-related development in the industrial world by providing this software that can use its GUI to operate the core libraries.
Deep learning refers to a form of machine learning that uses neural networks modeled after the human brain. By making the switch to deep learning-based machine learning, the past few years have seen a rapid improvement in image and voice recognition technology, even outperforming humans in certain areas. Compared to conventional forms of machine learning, deep learning is especially notable for its high versatility, with applications in a wide variety of fields besides image and voice recognition, including machine translation, signal processing, and robotics. As proposals are made to expand the scope of deep learning to fields where machine learning has not been traditionally used, there has been an accompanying surge in the number of deep learning developers.
The work of neural network design is very important for deep learning program development. Programmers construct the neural network best suited to the task at hand, such as image or voice recognition, and load it into a product or service after optimizing the network’s performance through a series of trials.
Currently, when creating conventional deep learning programs, neural networks are constructed by writing the program code and combining function blocks. However, by using this newly developed console software, this function block concept can be simply expressed through the GUI. On the console software screen, the different layers (function blocks) are prepared beforehand in the shape of ready-made components. Neural networks can then be constructed by simply rearranging these components through the GUI, greatly improving the efficiency of program development. Moreover, novice deep learning developers can visually confirm the functions of the core library and build up their proficiency in a short amount of time.
The primary features of the console software
Intuitive User Interface
Carry out neural network design, learning, and evaluation through an intuitive graphical user interface. The slew of support features provided by this console software allows developers to focus their attention on development work.
Edit Neural Networks by Simply Dragging and Dropping
Design cutting-edge neural networks through the software’s rich layering feature. The ability to effortlessly and immediately see your new ideas reflected in the GUI facilitates experimentation and streamlines the neural network creation process.
Check Progress and Results in Real-time Through Speedy, Automated Learning
Once the network has been designed, training begins with the push of a button. Progress and results can then be checked in real-time right from your screen.
Review Past Progress Through Centralized Control
Review dozens of previously learned and designed neural networks and compare their features all in the same place.
Automatic Optimization of Neural Network Designs
The work of “chaining,” or searching for smaller and higher-performing neural networks, can be left to the console’s tools. Choose the neural network that is best suited to your project’s needs from among the optimized search results.
This policy forms one part of Sony’s AI environmental improvement initiative. In a world where more goods and services are expected to make use of artificial intelligence to provide higher levels of convenience, Sony is providing an integrated development environment in the hope that a wider range of developers and researchers will build on its programs, and with the aim of contributing to the development of society.