English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 3h 18m | 979 MB
PyTorch is an open source deep learning framework originally developed by the AI teams at Facebook. PyTorch offers high-level APIs which make it easy to build neural networks and great support for distributed training and prediction.
PyTorch is an open source, deep learning framework which is a popular alternative to TensorFlow and Apache MXNet. PyTorch APIs follow a Python-native approach which, along with dynamic graph execution, make it very intuitive to work with for Python developers and data scientists. In this course, Building Deep Learning Models Using PyTorch, you will learn to work with PyTorch and all the libraries that it has to offer, from first principles – starting with Torch tensors, dynamic computation graphs, and the autograd library, to compute gradients. You’ll start off by understanding the basics of training a neural network, the forward and backward passes, and gradient computation. You will use these concepts to build simple neural networks to predict automobile prices, as well as who survived and who did not on the Titanic. Next, you’ll move on to image classification using convolutional neural networks; you’ll study the role of convolutional and pooling layers and the basic structure of a CNN, you’ll then build a CNN to classify images from the Cifar-10 dataset. You’ll also see how you can leverage the power of transfer learning by using pre-trained models for image classification. Finally, you’ll get to work with recurrent neural networks for sequence data, seeing how the dynamic computation graph execution in PyTorch makes building RNNs very simple. You’ll use RNNs with long memory cells to predict gender using baby names. At the end of this course, you will be comfortable using PyTorch libraries and APIs to leverage pre-trained models that PyTorch offers and also to build your own custom model for your specific use case.
Table of Contents
Course Overview
1 Course Overview
Introduction to PyTorch
2 Module Overview
3 Prerequisites and Course Outline
4 Neurons and Neural Networks
5 Introducing PyTorch
6 Installing PyTorch
7 Tensors
8 Creating and Working with PyTorch Tensors
9 Operations with Tensors
10 The Computation Graph
11 Gradient Descent
12 Forward and Backward Passes
Building Simple Neural Networks
13 Module Overview
14 Understanding Gradients
15 Introducing Autograd
16 Reverse-mode Automatic Differentiation to Calculate Gradients
17 Linear Model Using Autograd
18 Exploring the Automobile Price Prediction Dataset
19 Price Prediction Using a Fully Connected Neural Network
20 Optimizers
21 Neural Networks for Classification
22 Exploring the Titanic Dataset for Classification
23 Training the Neural Network
24 Plotting Accuracy and Loss Metrics
Building an Image Classification Model
25 Module Overview
26 Perceiving an Image
27 Convolutional Layers
28 Pooling Layers
29 CNN Architectures
30 Batch Normalization
31 Exploring The CIFAR10 Dataset
32 Demo Building and Training the CNN
33 Predictions on Test Data
34 Transfer Learning
35 ResNet Pretrained Model – Data Exploration
36 ResNet Pretrained Model – Data Exploration Helper Functions
37 ResNet Pretrained Model – Training and Prediction
38 Using a Pretrained Model with Frozen Layers
Building a Text Classification Model
39 Module Overview
40 Recurrent Neurons
41 Unrolling RNN Memory Cells Through Time
42 Long Memory Cells
43 Gender Prediction of Names RNN Structure
44 Prepare the Names Dataset
45 Building the RNN
46 Training the RNN
47 Confusion Matrix
48 Plotting Name Predictions in a Confusion Matrix
49 Summary and Further Study
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