Py Part 8 V1

So, in this video, I’ll be showing you how to use transfer learning to train a network that can properly classify those images of cats and dogs. What we’ll do here is use a pre-trained network to actually detect and extract features from the images. This is really good for solving many challenging problems in … Read more

PyTorch – Part 7

In this video, I’ll be showing you how to load image data. This is really useful for what you’ll be doing in real projects. So previously, we used MNIST. Fashion-MNIST were just toy datasets for testing your networks, but you’ll be using full-size images like you’d get from smartphone cameras and your actual projects that … Read more

Py Part 6 V1

In this video and notebook, I’ll be showing you how to save and load models. Like I said previously, you typically don’t want to have to train a new model every time you want to use it. So instead, you’ll train it once and then save it, and then if you need to use it … Read more

Py Part 5 V2

Hello, welcome back. So in this video and notebook, I’m going to be talking about inference and validation. So, inference means when we have a tree network and we’re using our network to make predictions. So neural networks have this issue where they have a tendency to perform too well on the training data, and … Read more

PyTorch – Part 4

Welcome back. So, in this notebook, you’ll be building your own neural network to classify clothing images. So, like I talked about in the last video, MNIST is actually a fairly trivial dataset these days. It’s really easy to get really high accuracy with a neural network. So instead, you’re going to be using Fashion-MNIST, … Read more

Py Part 3 V2

Hello, in this notebook, I’ll be showing you how to train a neural network with PyTorch. So, remember from the previous part we built a neural network but it wasn’t able to actually tell us what the digit was in these images. So, what we want is we want to be able to pass in … Read more

Py Part 2 V1

Hello everyone and welcome back.So,in this video, I’m going to be showing you how you actually build neural networks with PyTorch. At the end of this notebook which I’ll provide for you you’ll build your own neural network. So, let’s get started. So, the first step we import things like Normal, to import PyTorch we … Read more

Part 1 V2

Hello everyone and welcome to this lesson on deep learning with PyTorch. So, here I’ve built a bunch of Jupyter Notebooks that will lead you through actually writing the code to implement deep learning networks and Python. So, here we’re using the PyTorch framework which is somewhat newer than TensorFlow and Keras. It’s being developed … Read more

9 – 10 Visualizing Activations V1 RENDER V2

For intermediate layers, like the second convolutional layer in a CNN, visualizing the learned weights in each filter doesn’t give us easy to read information. So, how can we visualize what these deeper layers are seeing? Well, what can give us useful Information is to look at the feature maps of these layers as the … Read more

8 – 06 First Convolutional Layer T1 V1 RENDER V2

The first convolutional layer in a CNN applies a set of image filters to an input image and outputs a stack of feature maps. After such a network is trained, we can get a sense of what kinds of features this first layer has learned to extract by visualizing the learned weights for each of … Read more

7 – 05 Feature Maps V1RENDER V3

Let’s start near the beginning of this CNN. We’ve talked about how the first convolutional layer in a CNN consists of a number of filtered images that are produced as the input image is convolved with a set of image filters. These filters are just grids of weights. For a convolutional layer with four filters, … Read more

6 – 04 Feature Visualization V1 RENDER V2

Let’s look at the architecture of a classification CNN that takes in an image and outputs a predicted class for that image. You’ve seen how to train CNN’s like this on sets of labeled image data, and you’ve learned about the layers that make up a CNN. Things like convolutional layers that learned to filter … Read more

5 – 动量

So, here’s another way to solve a local minimum problem. The idea is to walk a bit fast with momentum and determination in a way that if you get stuck in a local minimum, you can, sort of, power through and get over the hump to look for a lower minimum. So let’s look at … Read more

4 – Dropout

Here’s another way to prevent overfitting. So, let’s say this is you, and one day you decide to practice sports. So, on Monday you play tennis, on Tuesday you lift weights, on Wednesday you play American football, on Thursday you play baseball, on Friday you play basketball, and on Saturday you play ping pong. Now, … Read more

3 – Pooling Layers

We’re now ready to introduce you to the second and final type of layer that we’ll need to introduce before building our own convolutional neural networks. These so-called pooling layers often take convolutional layers as input. Recall that a convolutional layer is a stack of feature maps- where we have one feature map for each … Read more

2 – Convolutional Layers (Part 2)

Consider this image of a dog. A single region in this image may have many different patterns that we want to detect. Consider this region for instance. This region has teeth, some whiskers, and a tongue. In that case, to understand this image, we need filters for detecting all three of these characteristics, one for … Read more

10 – 20 Summary Of Feature Viz V2 RENDER V2

Several approaches for understanding and visualizing convolutional networks have been developed in the literature. Partly, as a response to the common criticism that the learned features in a neural network are not interpretable. Now, you’ve seen a view of the most commonly used feature visualization techniques from looking at filter weights to looking at layer … Read more

1 – 03 Data And Lesson Outline RENDER V2

In this lesson, to learn about CNNs, we’ll first talk about the layers that make up an image classification CNN. You’ll learn how to define these layers, and what role each plays in extracting information from an input image. After learning about these layers, you’ll see how to define a CNN that aims to classify … Read more

9 – Calculating The Gradient 1

Okay. So, now we’ll do the same thing as we did before, painting our weights in the neural network to better classify our points. But we’re going to do it formally, so fasten your seat belts because math is coming. On your left, you have a single perceptron with the input vector, the weights and … Read more

8 – Backpropagation V2

So now we’re finally ready to get our hands into training a neural network. So let’s quickly recall feedforward. We have our perceptron with a point coming in labeled positive. And our equation w1x1 + w2x2 + b, where w1 and w2 are the weights and b is the bias. Now, what the perceptron does … Read more