What is Deep Learning and its Advantages By dataottam Guest Mandeep Kaur
Advantages of Deep Learning and its Tendency
Deep Learning is an area of machine learning which overcomes machine learning limitations. We can say, it is an advanced technique of machine learning because of deep neural networks and better results. The deep learning model can learn directly from dataset like images, voice or text. These deep neural networks (DNN) are artificial networks inspired by the human brain with the tendency to solve complex problems with state of art of accuracy. These came into existence after ANN (Artificial Neural Networks) got some issues of over fitting and computation time. Thus DNN is made prone to over fitting due to added layers. There are various artificial intelligence services available in India. In this article we will discuss basics of deep learning and its advantages.
Why Deep Learning in India is important: Deep Learning is sub-domain of machine learning which makes machines behave autonomously. Results of Deep Learning are reaching an accuracy level which other technologies are not able to provide. Sometimes state-of-art accuracy is achieved which exceeds human-level performance. Automated driving, stock market prediction, Sports gaming prediction and other medical research tasks are performed using this technique. That’s why top machine learning companies are integrating deep learning into their business model applications. For product recommendation system or sale management, neural networks are used to get high conversion of customers and generate better lead generation.
Benefits of Deep Learning: Deep Learning has huge advances in computational resources such as CPU and GPU in which complex and deeper models can be applied within a small time span. It has tendency to learn as a human learns. Neural networks offered this ability to the model. It helps numerous organizations to create exciting AI applications. To accomplish goals of AI, its core which represents machine learning and deep learning is implemented. This is due to increased digital data. To teach and train models, we need to feed heavy amount of data for training purpose which can be done achieved with GPU without wasting much time. Deep Learning made AI an effective field but need more advancement. In addition to it, further research is being done in this area.
Applications of Deep Learning: Deep Learning has provided a lot of solutions to real-world problems. In recent years, it has reached so many advances due to the breakthrough of different neural networks techniques. It is used in various applications of research, finance, sales, and security areas. Speech recognition, object detection, image recognition, natural language processing, trading system, recommendation system and self-driving cars are some deep learning applications. All this is possible due to standard deep learning libraries such as tensorflow, theano, keras which leads to prepare creative models for deep learning. Although it has been a great achievement in this field that massive amount of computational power is attained in real time. There are various artificial intelligence companies that transformed machine learning from computing to manufacturing, transportation, sales, finance, and healthcare as well. More conceptual innovation is provided due to deep networks.
Conclusion: Machine Learning and Deep Learning both are the emerging technologies these days. Different models of neural networks such as recurrent, convolutional, LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) and GAN (Generative Adversarial Networks) are applied to many problems like Image recognition, natural language processing, and object detection. All these were improved after introducing the concept of deep learning in AI. If one understands the core principal behind deep neural networks then it can be easily applied to different complex problems. Deep Learning has overcome difficult challenges and achieved complex nature human intelligence. In final words, we can say that the profound insights of this technology are inevitable. Different techniques are also being developed to train deep networks.
