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Deep Learning
In a very short time, deep learning has become a widely useful technique, solving and automating problems in computer vision, robotics, healthcare, physics, biology, and beyond. One of the delightful things about deep learning is its relative simplicity. Powerful deep learning software has been built to make getting started fast and easy. In a few weeks, you can understand the basics and get comfortable with the techniques. This opens up a world of creativity. You start applying it to problems that have data at hand, and you feel wonderful seeing a machine solving problems for you. However, you slowly feel yourself getting closer to a giant barrier. You built a deep learning model, but it do...
Reinforcement Learning - Principles, Concepts and Applications
Reinforcement learning (RL) is a subfield of machine learning that deals with how an agent should learn to take actions in an environment to maximize some notion of cumulative reward. In other words, reinforcement learning is a learning paradigm where an agent learns to interact with an environment by taking actions and observing the feedback it receives in the form of rewards or penalties. It is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or penalty.
Mathematical Principles in Machine Learning
Machine learning, artificial intelligence (AI), and cognitive computing are dominating conversations about how emerging advanced analytics can provide businesses with a competitive advantage to the business. There is no debate that existing business leaders are facing new and unanticipated competitors. These businesses are looking at new strategies that can prepare them for the future. While a business can try different strategies, they all come back to a fundamental truth. If you’re curious about machine learning, this book is a wonderful way to immerse yourself in key concepts, terminology, and trends. We’ve curated a list of machine learning topics for beginners, from general overviews to those with focus areas, such as statistics, deep learning, and predictive analytics. With this book on your reading list, you’ll be able to: · Determine whether a career in machine learning is right for you · Learn what skills you’ll need as a machine learning engineer or data scientist · Knowledge that can help you find and prepare for job interviews · Stay on top of the latest trends in machine learning and artificial intelligence
The Sterling Book of Ma Sarada
Here are some glimpses of the unique life of Sri Sarada Devi, the divine consort of Sri Ramakrishna Paramahamsa, combining both her human and divine roles. The life of Sri Sarada Devi, known to devotees also as the Holy Mother of Sri Sri Ma, had been a mixture of the mundane and the profoundly spiritual. She effortlessly moved from the grossly external to the supremely internal levels of existence all through her life. She faced tremendous hardships with a serenity that is almost impossible to encounter elsewhere. Each incident of her life serves as a core for contemplating on the highest truths embedded in our ancient scriptures and paradoxically in our own ordinary lives. The volume brings out all these facets of her experience from a special perspective.
Sri Ramakrishna
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Hymn to Kali
This celebrated Kaula Stotra, which is now translated from the Sanskrit for the first time, is attributed to Mahākāla Himself. The Text used is that of the edition published at Calcutta in 1899 by the Sanskrit Press Depository, with a commentary in Sanskrit by the late Mahāmahopādhyāya Kṛṣhṇanātha Nyāya-pañcānana, who was both very learned in Tantra-Śāstra and faithful to his Dharma. He thus refused the offer of a good Government Post made to him personally by a former Lieutenant-Governor on the ground that he would not accept money for imparting knowledge. Some variants in reading are supplied by this commentator. I am indebted to him for the Notes, or substance of the note...
Deep Learning for Natural Language Processing
Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natur...
