Deep learning is a complex concept in which each factor is not simple. Even if you are a data scientist who is already familiar with the basics of artificial neural networks, you need time to understand the concepts of accelerated convolution, recurrence, generation, and other complex concepts associated with multi-layer deep learning algorithm specifications. With the rise of deep learning innovation, this technology is beginning to face new risks – for ordinary developers, its overly complex nature makes it difficult to understand even if we study it in depth.
But I personally have confidence in this. In my opinion, at the end of this decade, the deep learning industry will greatly simplify the way its functions are delivered, meaning that ordinary developers can understand and adopt them. Below, I will discuss with you the six predictions about the future direction of deep learning.
1. Deep learning industry will adopt a core set of standardization tools
By the end of this decade, the deep learning industry will establish a core set of standardized tools. For now, deep learning experts have selected a number of tool options, most of which are open source projects. The most popular results include TensorFlow, BigDL, OpenDeep, Caffe, Theano, Torch, and MXNet.
2. Deep learning will achieve native support within Spark
The Spark community will enhance the platform's native deep learning capabilities in the next one to two years. From the speech of this Spark Summit, the entire technical community seems to be clearly pointing to TensorFLow, and BigDL, Caffe and Torch will at least be included in the support.
3. Deep learning will find a solid niche platform in the open analysis ecosystem
Most deep learning deployments are based on Spark, Hadoop, Kafka, and other open source data analytics platforms. The more invented trend indicates that we will not be able to implement the training, management, and deployment of deep learning algorithms without the complete big data analysis capabilities provided by these platforms. Specifically, Spark will become the basic platform for realizing the scale-up and acceleration of deep learning algorithms in various tools. To be sure, most deep learning developers are leveraging Spark clusters for specific tasks such as hyperparameter optimization, fast memory data training, data cleansing, and preprocessing.
4. Deep learning tools will introduce a simplified programming framework for fast coding
The application developer community will continue to adopt APIs and other programming abstractions designed to reduce the number of lines of code to quickly complete the development of core algorithm functions. Looking ahead, deep learning developers will increasingly have integrated, open, cloud-based development environments and access to a wider range of off-the-shelf and pluggable algorithm libraries. All of this will enable deep learning applications to implement API-driven development in the form of assembleable containerized microservices. Such tools will automatically implement more deep learning development pipeline capabilities while providing collaboration and sharing specifications for notebook devices. As this trend intensifies, we will see more headlines such as "Pytorch implements a proactive confrontation network."
5. Deep learning toolset will support visual development of reusable components
The Deep Learning toolset will introduce more modular capabilities for visual design, configuration, and new model training based on the original building components. Most of the reusable components will be extracted from the original project through "translation learning" to solve similar use cases. Reusable deep learning artifacts will be incorporated into standard libraries and interfaces, including feature representation, neural node stratification, weighting methods, training methods, learning rates, and other functions associated with the original model.
6. Deep learning tools will be embedded in each type of design surface
It is still too early to discuss the process of “democratization of deep learningâ€. In the next five to ten years, deep learning development tools, libraries, and languages ​​will gradually become standard components within each software development toolset. Equally important, these user-friendly deep learning development capabilities will be embedded into the generative design tools for artists, designers, architects, and creative workers from all fields. What drives this is the ease of use of deep learning tools, and its powerful capabilities will be widely used in image search, auto-tagging, simulation rendering, resolution enhancement, style conversion, graphics inspiration, and music arrangement.
With the rapid advancement of deep learning in large-scale market applications, it will become a cornerstone of many industries along with data visualization, business intelligence and predictive analytics. And various types of trial-and-learning solutions will begin to transform into a self-service cloud delivery model that will facilitate those who do not want to be exposed to the underlying technical complexity. And this is also the inevitable trend of long-term technological development.
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