4 min read

Deep Learning as a Service

Let us begin with the truth about deep learning, artificial intelligence, and machine learning. If you didn’t try to understand the concept of these three fast-growing technology then it is possible that in the next three years you will be living in the dinosaur era. In simple words, these technologies are going to take over the world at a fast speed so you need to learn them as soon as possible to be abreast of advancement.

In the Deep learning process, a scientist can actually design their own visions of neural networks. It could be run off while scaling out the training process of a company that will work along with auto location. In simple words, you just have to pay for the resources that are utilized in the training. This is all due to the advancement in the technologies of Artificial intelligence that is going on for over a decade now. It has fueled up the volume of data and unprecedented advancement of deep learning.

There are mainly three trends that have given rise to Deep Learning:

  • Burst in the total volume of training data
  • Accelerators advancement and utilization such as GPU
  • Training algorithms advancement and architecture of neural networks

The Deep Learning process is widely known as neural network training which is computationally intensive and complex. In this system, the perfect combination of computing, drivers, software, network, memory, and storage resources are utilized with high tuning. Additionally, if the full potential of Deep Learning must be realized then it must be accessible for the data scientists and developers. This will help them to complete their task efficiently such as neural network model training with the help of automation along with datasets (especially large), refinement and concentration of data, and cutting-edge model creation.

Neural Networks

They are the main building blocks of technology that are widely used in the process of Deep Learning. It is a simple processing unit that can store knowledge easily and also to use the same data while making out predictions. In the neural network, the brain will predict networks that in return acquire data from the environment with the help of the learning process. Then, synaptic weights are used for the connection strength that easily stores the information. They, synaptic weights, are modified in the learning process to make sure that goals are achieved.

It is usually compared with the human brain as explained by Karl Lashley, the neuropsychologist, in 1950. In comparison, the main points taken are their non-linear operations that work on information processing with computation work such as perception and pattern recognition. In turn, these networks are extremely popular in the areas of recognition of audio, speech, and image where inherently nonlinear signals are used.

SLP - Single Layer Perceptron

The perceptron is the simplest layer of weights that help in the connection of outputs and inputs. This is the main reason that this system is considered as a feed-forward network where the data only moves in a single direction and cannot be reversed. It requires synaptic weights, summing junction, input signals, and activation signals to come up with a relevant output.

SLP can easily be conceptualized into grounding and clarification into the advanced mode of a multilayer perceptron. There is a total of m weights in SLP that represents the total set of synapses which are connected together with a link between multiple layer and single layer. This helps in the explanation of the character of each feature, xj.

uk = Σ(j = i to m) wkj * xj|

In such a condition, bk bias will act as a transformation of affine to the output of the uk, adder functions that gives the total of the induced local field, vk.

vk = uk + bk


Multilayer Perceptron

Now, this is a feed-forward type of network that has a total sequence of layers on which the work is done and that are connected to each other. In this, there is an extra layer that is hidden apart from input and output. Each and every layer will consist of different neurons that are connected to each other with the help of weight links. In the input layer, the neurons represent the total dataset or their attributes while in the output layer, they will be the dataset given in a class. The hidden layer is only introduced to make the task much simpler.


This technique of Deep Learning is implemented in Watson Studio by IBM earlier this year. There is a number of different features that are showcased in deep learning such as lowering the barrier so that all users can make an entry. The model in Watson Studio is so much enhanced with cloud-native along with an environment that supports end-to-end implementation for developers, data scientists, SMEs, and business analysts to build up and train a model of AI. This can be done in semi-structured, structured, and unstructured data along with the maintenance of an organization’s rules and policies. This has helped the system a lot for making it accessible and easy to scale. In addition to this, the automation process will also help in the reduction of complexity.

Features of Deep Learning

  • Flexibility and Open –If you are using the preferred deep learning framework such as Keras, PyTorch, Tensorflow, Caffe, etc. then you need to understand that it can be managed with the help of several tools for the python library, CLI – Command Line Interface and user interface.

  • Experiment Assistant –The branch training is initiated and monitored before the comparison of cross-modal performance. On top of that, it is done in real-time without any worries of the transfer of logs and visualization of script transfer. You can focus on the neural network design without any worries of tracking or managing assets.
  • Elastic GPU Compute –Neural networks can be trained easily along with GPUs and you don’t have to worry about paying extra money. Simply, pay for the one that you are using. In addition to this, the auto-allocation process is used which means that there is no need for the closing of the cloud training instances, no management of container or clusters.

Benefits of Deep Learning

  • On-demand Intelligence –In the managed training, you simply have to focus on the optimal design of the structure of neural networks. Here, the training asset can be stored with auto-allocation properties.

  • Time Consumption –It is not only about money but also about the existing workflow and IDE. It is easy to access python, REST, CLI, etc. by gaining the balance of the visual restoring tools. This will help in designing and making the system faster and better.
  • Cloud Infrastructure –This will also help in the optimization of environment production and infrastructure running that works on the platform of the host such as IBM Watson

You need to understand that the Deep Learning process is full of mathematical terms and calculations that can help in several ways. There is no need to worry about mathematical function since they are simply about integral and algebra. Apart from the above-mentioned points, you can also understand the robust model for deep learning that will include cost function, backpropagation algorithm, and gradient descent that are used extensively for the model work. All you need to do is gain expertise and you are good to go.

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