Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to Py Torch, you’ll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will then take a look at probability distributions using Py Torch and get acquainted with its concepts. Further you will dive into transformations and graph computations with Py Torch. Along the way you will take a look at common issues faced with neural network implementation and tensor differentiation, and get the best solutions for them.
Moving on to algorithms; you will learn how Py Torch works with supervised and unsupervised algorithms. You will see how convolutional neural networks, deep neural networks, and recurrent neural networks work using Py Torch. In conclusion you will get acquainted with natural language processing and text processing using Py Torch.
What You Will Learn
- Master tensor operations for dynamic graph-based calculations using Py Torch
- Create Py Torch transformations and graph computations for neural networks
- Carry out supervised and unsupervised learning using Py Torch
- Work with deep learning algorithms such as CNN and RNN
- Build LSTM models in Py Torch
- Use Py Torch for text processing
Who This Book Is For
Readers wanting to dive straight into programming Py Torch.
Содержание
Chapter 1: Introduction Py Torch, Tensors, Tensor Operations and Basics.- Chapter 2: Probability distributions using Py Torch.- Chapter 3: Convolutional Neural Network and RNN using Py Torch.- Chapter 4: Introduction to Neural Networks, Tensor Differentiation .- Chapter 5: Supervised Learning using Py Torch.- Chapter 6: Fine Tuning Deep Learning Algorithms using Py Torch.- Chapter 7: NLP and Text Processing using Py Torch.
Об авторе
Pradeepta Mishra is a data scientist and artificial intelligence researcher by profession, currently head of NLP, ML, and AI at Lymbyc, has expertise in designing artificial intelligence systems for performing tasks such as understanding natural language and giving recommendations based on natural language processing. He has filed two patents as an inventor, has written two books:
R Data Mining Blueprints and
R: Mining Spatial, Text, Web, and Social Media Data. There are two courses available on Udemy from his books. He has delivered a talk at the Global Data Science conference 2018, at Santa Clara, CA, USA on applications of bi-directional LSTM for time series forecasting. One of his books has been a recommended text at the HSLS Center, University of Pittsburgh, PA, USA. He has delivered a TEDx talk on ‘Can Machines Think?’, a session on the power of artificial intelligence in transforming different industries and changing job roles across industries. He has delivered 50+ tech talks on data science, machine learning, and artificial intelligence in various meet-ups, technical institutions, universities, and community arranged forums.
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