INTRODUCTION – Train And Evaluate Deep Learning Models
In this module, you will study basic principles of deep learning and learn how these principles help to understand the development of advanced neural network models. You will learn to build deep neural networks (DNNs) through popular frameworks such as PyTorch and TensorFlow.
Parts of the module also look at how to use CNNs or convolutional neural networks to achieve some of the latest image classification systems. At the end of this module, you could use all your knowledge and techniques effectively for many different deep-learning purposes.
Learning Objectives:
- Understand the Fundamental Principles of Deep Learning.
- Learn how to train a DNN (deep neural network) using PyTorch or TensorFlow.
- Understand how transfer learning can be adopted to train a CNN (convolutional neural network) using PyTorch or TensorFlow.
- Learn how to train a CNN (convolutional neural network) using PyTorch or TensorFlow.
PRACTICE QUIZ: KNOWLEDGE CHECK 1
1. Complete the sentence:
Deep learning is an advanced form of machine learning that aims to[…]?
- Help humans develop better learning skills.
- Emulate the way the human brain learns. (CORRECT)
- Make machines learn better and faster than humans.
Correct: That is, deep learning is an activity carried out in a manner similar to the original physiological functioning of the brain, except that instead of electrochemical stimuli, inputs such as numbers or numeric data feed into an artificial neural net.
2. In a deep learning model, the incoming nerve connections are emulated by numeric inputs which are typically identified as x.
Each x value is associated with some inputs. Which are those inputs?
- Speed
- Ratio
- Weight (CORRECT)
- Bias (CORRECT)
Correct: Feedback: The input weight (w) is used to modulate the effect of the x parameter by enhancing or diminishing it; thereby, it operates as though it were simulating a learning process.
Correct: A bias term b is added to enable a fine-grained control in the network, which shifts the activation function and improves the power of the learning model.
3. The training process of a deep neural network consists of multiple iterations.
What is the name of those iterations?
- Epochs (CORRECT)
- Eras
- Cycles
Correct: They’re called epochs.
4. During the training of a deep neural network, inside an epoch, in which step is the loss calculated?
- 3rd (CORRECT)
- 1st
- 4th
- 2nd
Correct: Comparing the predicted values with the actual values is what we mean by the third step. It is possible to compute the difference between the predicted and true values (loss). The loss would measure the error of the model.
5. True or False?
When training deep learning models, computers with GPUs perform better than computers with CPUs only.
- True (CORRECT)
- False
Correct: The functional mathematics define the linear algebra with the relevance of matrices and vectors as indispensable in deep learning and 3D graphics. Hence, the training of deep learning models achieves significant performance improvement with the use of GPUs rather than CPUs because GPUs are processor designs that are optimized for performing massive parallel processing on large amounts of data, typical in both fields.
PRACTICE QUIZ: KNOWLEDGE CHECK
1. In which AI domain are deep learning models particularly useful?
- Speech processing
- Image processing (CORRECT)
- Text processing
- Natural language processing (NLP)
Correct: In fact, deep learning is applied to all machine learning tasks or problems, but it is especially said to work wonders while dealing with data in forms of large arrays of numeric values, such as images. Machine learning models specific for image processing constitute the core of a subfield of artificial intelligence, called computer vision. Throughout the years, several advancements have been made in this field by deep learning techniques, leading to breakthroughs in areas such as object recognition, image classification, and autonomous systems.
2. How does a convolutional neural network typically work?
- By comparing features and known labels to generate a prediction
- By grouping objects based on similar features
- By extracting features from images and feeding them into a network to generate a prediction (CORRECT)
Correct: The CNN operates by recognizing relevant features from the images through convolutional layers that convolve the filter with the image itself. Filters are capable of detecting features such as edges, textures, and shapes in the image. Fully connected layers will use these features to predict or classify them. The feature extraction layers significantly reduce the dimensionality of the input images into a small number of high-level features that are very relevant for label prediction instead of individual pixel values that may run into millions. This greatly enhances the object’s identification and classification capability in images through a CNN.
3. A convolutional network consists of multiple layers.
Which are those layers?
- Scanning layers
- Curving layers
- Dropping layers (CORRECT)
- Fully connected layers (CORRECT)
- Pooling layers (CORRECT)
- Flattening layers (CORRECT)
- Convolution layers (CORRECT)
Correct: This method of training the model not to become too dependent on the specific neurons is dropout, in which during training certain neurons or feature maps in a layer are randomly “dropped” (set to zero). This effectively combats overfitting. Dropout enables the model to be quite robust in terms of not memorizing overly specific examples but rather developing broad generalizable patterns.
Correct: Most CNNs terminate in a completely connected network, where the features extracted will be inputted into a layer that will then go through one or more hidden layers, eventually producing the predicted values in the output layer.
Correct: In this regard, pooling or downsampling layers are also used, since they keep those important distinctions identified by the feature value, but reduce the number of feature values extracted from the image.
Correct: Flattening layer converts feature vector feature maps into a flattened 1D vector value suitable for input to a fully connected layer.
Correct: One of the principal layer types is a convolutional layer that extracts important features in images.
4. Which convolutional network layer helps ensure that the model doesn’t become over-dependent on the training images?
- Dropping layer (CORRECT)
- Flattening layer
- Fully connected layer
Correct: Overfitting is one of the major disadvantages in CNN models. Overfitting, in general terms, is that a model performs very well on the training data but fails to generalize characteristics of previously unseen data. One of the various techniques to mitigate overfitting is to include dropout layers in which a certain percentage of feature maps would randomly drop, or eliminate, during training.
5. True or False?
A convolutional layer applies multiple filter kernels, where each filter produces a different feature map, and all of the feature maps are passed onto the next layer of the network.
- True (CORRECT)
- False
Correct: A convolution layer performs convolution with an image by means of a filter. Such a filter uses a kernel, in form of a matrix of weight values, that moves along the image and results into this extraction of pertinent features.
QUIZ: TEST PREP
1. You developed a deep neural network to train a classification model that predicts to which of the four classes an observation belongs based on 8 numeric features.
Which of the following statements is true about the network architecture?
- The network layer should contain four hidden layers
- The input layer should contain four nodes
- The output layer should contain four nodes (CORRECT)
Correct: There should be an output node for each possible class value on the output layer, with each node indicating the probability that the input belongs to that specific class.
2. You are training a deep neural network. You configured the training process to use 35 epochs.
What effect does this have on the behavior of the model?
- The training data is split into 35 subsets, and each subset is passed through the network.
- The first 35 rows of data are used to train the model, and the remaining rows are used to validate it.
- The entire training dataset is passed through the network 35 times. (CORRECT)
Correct: The number of epochs represents how many times the complete dataset is traversed through the entire model during the learning process.
3. When creating a deep neural network, if you increase the Learning Rate parameter, what effect will this setting have?
- More hidden layers are added to the network.
- Larger adjustments are made to weight values during backpropagation. (CORRECT)
- More records are included in each batch passed through the network.
Correct: Raising learning rates composes backpropagation’s action in strides that affect weight advices more, with the advantage being hastening of training and the disadvantage concern overshoot of the best position in optimization.
4. When creating a convolutional neural network, if you want to reduce the size of the feature maps that are generated by a convolutional layer, what should you do?
- Reduce the size of the filter kernel used in the convolutional layer.
- Add a pooling layer after the convolutional layer. (CORRECT)
- Increase the number of filters in the convolutional layer.
Correct: Pooling layer is instrumented to reduce the number of features in a feature map by down sampling thus reducing the computational load and control overfitting while preserving the most salient features.
5. True or False?
The feature extraction layers apply convolutional filters and pooling to emphasize edges, corners, and other patterns in the images that can be used to differentiate them, and in theory should work for any set of images with the same dimensions as the input layer of the network.
- True (CORRECT)
- False
Correct: The layers for feature extraction apply convolutional filters and pooling which mark edges, inspect corners, and extract all kinds of patterns from the images which will help in distinguishing them. In principle, it should work for all the images equal to the input dimension of the network.
6. This layer is a principal layer type that extracts important features in images. It works by applying a filter to images. What type of layer is this?
- Flattening layer
- Convulsion layer (CORRECT)
- Pooling layer
Correct: A Convolutional layer is one of the important layers for building a CNN. This layer extracts important features from the images by their application of filter (or kernel) over-
7. After extracting feature values from images, this layer is used to reduce the number of feature values while retaining the key differentiating features that have been extracted.
- Flattening layer
- Convulsion layer
- Pooling layer (CORRECT)
Correct: Pooling (or down sampled) layers are utilized after extracting feature values from images, in order to reduce the number of features while retaining the most significant distinctive characteristics that have been discovered.
CONCLUSION – Train And Evaluate Deep Learning Models
This completes an intense core module that opened the deep learning dimension. equips all the tools necessary to develop deep neural network models using PyTorch and TensorFlow. It brings one the practical experience of developing convolutional neural networks for image classification and helps to further seal understanding of the real-world application of this technology. By the end of this module, you would be in an excellent position to use these state-of-the-art technologies in future projects and research, greatly improving one’s admission into the world of deep learning.