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Mar 31, 2023 · This tutorial provides an introduction to deep learning algorithms and their applications in various fields. We will cover the fundamentals of deep learning, including its underlying workings, neural network architectures, and popular frameworks used for implementation.
Jun 17, 2022 · In this tutorial, you will discover how to create your first deep learning neural network model in Python using Keras. Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples. Let’s get started.
Goal of this tutorial: Understand PyTorch’s Tensor library and neural networks at a high level. Train a small neural network to classify images. To run the tutorials below, make sure you have the torch, torchvision , and matplotlib packages installed. Tensors. In this tutorial, you will learn the basics of PyTorch tensors. Code.
Deep Learning with PyTorch — PyTorch Tutorials 2.3.0+cu121 documentation. Deep Learning Building Blocks: Affine maps, non-linearities and objectives. Deep learning consists of composing linearities with non-linearities in clever ways. The introduction of non-linearities allows for powerful models.
Jan 11, 2019 · Lex Fridman. 3.77M subscribers. Subscribed. 42K. 2.2M views 5 years ago. An introductory lecture for MIT course 6.S094 on the basics of deep learning including a few key ideas, subfields, and...
Feb 4, 2019 · Lecture videos and tutorials are open to all. As part of the MIT Deep Learning series of lectures and GitHub tutorials, we are covering the basics of using neural networks to solve problems in computer vision, natural language processing, games, autonomous driving, robotics, and beyond.
Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems.
