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MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING
Because of the advancements that have been made in machine learning, the world is being changed in ways that are difficult to conceive. If you stop for a second and take a good look around, you'll see that the area of data science is everywhere you turn. Take, for example, Alexa from Amazon; she is an artificial intelligence that has been developed to be as simple and straightforward to use as is humanly conceivable. There are many other digital assistants similar to Alexa, such as Google Assistant, Cortana, and so on. Alexa is not the only one of its sort. Therefore, the question of why they were formed in the first place is the most crucial one to ask; the question of how they developed is...
MORPHOLOGICAL IMAGE PROCESSING AND INTENSITY TRANSFORMATIONS
The newspaper business was one of the pioneers in the usage of digital photographs and was one of the first businesses to do so. It was also one of the first businesses to make use of images, which were first transported by photographs, which were initially delivered by an underwater cable between London and New York. This connection was made between London and New York. These pictures were sent between the two locations on several occasions, making their way between both of them in a back and forth manner. When the Bartlane cable image transmission system was first put into operation in the early 1920s, the amount of time necessary to transfer a picture across the Atlantic was reduced from ...
DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION
Deep learning has developed as a useful approach for data mining tasks such as unsupervised feature learning and representation. This is thanks to its ability to learn from examples with no prior guidance. Unsupervised learning is the process of discovering patterns and structures in unlabeled data without the use of any explicit labels or annotations. This type of learning does not require the data to be annotated or labelled. This is especially helpful in situations in which labelled data are few or nonexistent. Unsupervised feature learning and representation have seen widespread application of deep learning methods such as auto encoders and generative adversarial networks (GANs). These a...
THE INDIAN LISTENER
The Indian Listener (fortnightly programme journal of AIR in English) published by The Indian State Broadcasting Service,Bombay ,started on 22 December, 1935 and was the successor to the Indian Radio Times in english, which was published beginning in July 16 of 1927. From 22 August ,1937 onwards, it was published by All India Radio,New Delhi.From July 3 ,1949,it was turned into a weekly journal. Later,The Indian listener became "Akashvani" in January 5, 1958. It was made a fortnightly again on July 1,1983. It used to serve the listener as a bradshaw of broadcasting ,and give listener the useful information in an interesting manner about programmes,who writes them,take part in them and produc...
AKASHVANI
"Akashvani" (English) is a programme journal of ALL INDIA RADIO, it was formerly known as The Indian Listener. It used to serve the listener as a bradshaw of broadcasting ,and give listener the useful information in an interesting manner about programmes, who writes them, take part in them and produce them along with photographs of performing artists. It also contains the information of major changes in the policy and service of the organisation. The Indian Listener (fortnightly programme journal of AIR in English) published by The Indian State Broadcasting Service, Bombay, started on 22 December, 1935 and was the successor to the Indian Radio Times in English, which was published beginning ...
AKASHVANI
"Akashvani" (English) is a programme journal of ALL INDIA RADIO, it was formerly known as The Indian Listener. It used to serve the listener as a bradshaw of broadcasting ,and give listener the useful information in an interesting manner about programmes, who writes them, take part in them and produce them along with photographs of performing artists. It also contains the information of major changes in the policy and service of the organisation. The Indian Listener (fortnightly programme journal of AIR in English) published by The Indian State Broadcasting Service, Bombay, started on 22 December, 1935 and was the successor to the Indian Radio Times in English, which was published beginning ...
Karan Ghelo
In the grip of lust, Raja Karan Vaghela abducts the beautiful Roopsundari, his prime minister Madhav’s wife. Fuelled by a desire for revenge, Madhav escapes to Delhi and persuades Sultan Alauddin Khilji to invade Gujarat and destroy Patan fort. This unleashes a dramatic chain of events that forever ends Rajput rule in Gujarat, heralding the dawn of a new age. Rich in psychological insight and imbued with a poetic vision, Karan Ghelo tells the spellbinding tale of a man who tragically failed his land and its people.
Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1
This volume includes 74 papers presented at ICTIS 2017: Second International Conference on Information and Communication Technology for Intelligent Systems. The conference was held on 25th and 26th March 2017, in Ahmedabad, India and organized jointly by the Associated Chambers of Commerce and Industry of India (ASSOCHAM) Gujarat Chapter, the G R Foundation, the Association of Computer Machinery, Ahmedabad Chapter and supported by the Computer Society of India Division IV – Communication and Division V – Education and Research. The papers featured mainly focus on information and communications technology (ICT) for computation, algorithms and data analytics. The fundamentals of various data analytics and algorithms discussed are useful to researchers in the field.
Practical Machine Learning and Image Processing
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled...
