Displaying 1 to 5 from 5 results

Hands-On-Deep-Learning-Algorithms-with-Python - Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow

  •    Jupyter

Deep learning is one of the most popular domains in the artificial intelligence (AI) space, which allows you to develop multi-layered models of varying complexities. This book is designed to help you grasp things, from basic deep learning algorithms to the more advanced algorithms. The book is designed in a way that first you will understand the algorithm intuitively, once you have a basic understanding of the algorithms, then you will master the underlying math behind them effortlessly and then you will learn how to implement them using TensorFlow step by step. The book covers almost all the state of the art deep learning algorithms. First, you will get a good understanding of the fundamentals of neural networks and several variants of gradient descent algorithms. Later, you will explore RNN, Bidirectional RNN, LSTM, GRU, seq2seq, CNN, capsule nets and more. Then, you will master GAN and various types of GANs and several different autoencoders.

wikimark - get a sens of it

  •    Python

wikimark goal is to give you an idea of what the text is about. You can also use your own corpus.

paragraph-vectors - :page_facing_up: A PyTorch implementation of Paragraph Vectors (doc2vec)

  •    Python

A PyTorch implementation of Paragraph Vectors (doc2vec). All models minimize the Negative Sampling objective as proposed by T. Mikolov et al. [1]. This provides scope for sparse updates (i.e. only vectors of sampled noise words are used in forward and backward passes). In addition to that, batches of training data (with noise sampling) are generated in parallel on a CPU while the model is trained on a GPU.

doc2vec_pymongo - Machine learning prediction of movies genres using Gensim's Doc2Vec and PyMongo - (Python, MongoDB)

  •    Python

A very small set of data is provided with this repository for example purposes. There are two json files that are ready to import into a MongoDB deployment. You can either construct the document yourself, or fetch existing information from movies' sites.

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