I PRE-TRAINING ASSESSMENT (PTA)
The PTA is a short assessment of 14 mostly Yes/No questions. The PTA is administered at the very start of the user training or even before the user training. The goal of the PTA is to assess what level the users are at as a group, as well as individually.Unsupervised pre-training initializes a discriminative neural net from one which was trained using an unsupervised criterion, such as a deep belief network or a deep autoencoder. This method can sometimes help with both the optimization and the overfitting issues.
training: • Greedy layer-wise: Train layers sequentially starting from bottom. (input) layer. • Unsupervised: Each layer learns a higher-level representation of. the layer below.
A stacked autoencoder is a neural network consisting of multiple layers of sparse autoencoders in which the outputs of each layer is wired to the inputs of the successive layer. The features from the stacked autoencoder can be used for classification problems by feeding a(n) to a softmax classifier.
Semi-supervised machine learning is a combination of supervised and unsupervised machine learning methods. In semi-supervised learning, an algorithm learns from a dataset that includes both labeled and unlabeled data, usually mostly unlabeled.
From Deep Learning Course Wiki. Fine tuning is a process to take a network model that has already been trained for a given task, and make it perform a second similar task.
Unsupervised Pre-training Acts as a Regularizer
As stated in the introduction, we believe that greedy layer-wise unsupervised pre-training overcomes the challenges of deep learning by introducing a useful prior to the supervised fine-tuning training procedure.VGG16 is a convolutional neural network model proposed by K. Zisserman from the University of Oxford in the paper “Very Deep Convolutional Networks for Large-Scale Image Recognition”. The model achieves 92.7% top-5 test accuracy in ImageNet, which is a dataset of over 14 million images belonging to 1000 classes.
- 10 Ways to Improve Transfer of Learning.
- Focus on the relevance of what you're learning.
- Take time to reflect and self-explain.
- Use a variety of learning media.
- Change things up as often as possible.
- Identify any gaps in your knowledge.
- Establish clear learning goals.
- Practise generalising.
Better ImageNet networks provide better penultimate layer features for transfer learning with linear classi- fication (r = 0.99), and better performance when the entire network is fine-tuned (r = 0.96).
Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task. Transfer learning is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned.
Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks.
A competition-winning model for this task is the
VGG model by researchers at Oxford.
Develop a Simple Photo Classifier
- Get a Sample Image. First, we need an image we can classify.
- Load the VGG Model.
- Load and Prepare Image.
- Make a Prediction.
- Interpret Prediction.
Transfer learning is when a model developed for one task is reused for a model on a second task. Fine tuning is one approach to transfer learning, and it is very popular in computer vision and NLP. The most common example given is when a model is trained on ImageNet is fine-tuned on a second task.
TensorFlow is an open source software library for numerical computation using data-flow graphs. TensorFlow is cross-platform. It runs on nearly everything: GPUs and CPUs—including mobile and embedded platforms—and even tensor processing units (TPUs), which are specialized hardware to do tensor math on.
The Softmax classifier uses the cross-entropy loss. The Softmax classifier gets its name from the softmax function, which is used to squash the raw class scores into normalized positive values that sum to one, so that the cross-entropy loss can be applied.
The ResNet-50 model consists of 5 stages each with a convolution and Identity block. Each convolution block has 3 convolution layers and each identity block also has 3 convolution layers. The ResNet-50 has over 23 million trainable parameters.
The “VGG-16” network has less weight with column “A”, whereas the “VGG-19” network has more weight in terms of column “C” respectively. The size of the “VGG-16” network in terms of fully connected nodes is 533 MB. and the size of the “VGG-19” network in terms of fully connected nodes is 574 MB.
The Keras deep learning neural network library provides the capability to fit models using image data augmentation via the ImageDataGenerator class. Image data augmentation is used to expand the training dataset in order to improve the performance and ability of the model to generalize.
VGG16 is a convolution neural net (CNN ) architecture which was used to win ILSVR(Imagenet) competition in 2014. It is considered to be one of the excellent vision model architecture till date.
GoogLeNet is a convolutional neural network that is 22 layers deep. You can load a pretrained version of the network trained on either the ImageNet [1] or Places365 [2] [3] data sets. The network trained on ImageNet classifies images into 1000 object categories, such as keyboard, mouse, pencil, and many animals.
The model can switch to a lower batch size or higher learning rate anytime to achieve better test accuracy. larger batch sizes make larger gradient steps than smaller batch sizes for the same number of samples seen. large batch size means the model makes very large gradient updates and very small gradient updates.
Layer freezing means layer weights of a trained model are not changed when they are reused in a subsequent downstream task - they remain frozen. Essentially when backprop is done during training these layers weights are untouched. So in transfer learning, we reuse by freezing or fine tuning the layers of a model.
Fine tuning is a process to take a network model that has already been trained for a given task, and make it perform a second similar task.
How Do I Get Started?
- Step 1: Adjust Mindset. Believe you can practice and apply machine learning.
- Step 2: Pick a Process. Use a systemic process to work through problems.
- Step 3: Pick a Tool. Select a tool for your level and map it onto your process.
- Step 4: Practice on Datasets.
- Step 5: Build a Portfolio.
The steps are:
- Clean the data by removing outliers and treating missing data.
- Identify a parametric or nonparametric predictive modeling approach to use.
- Preprocess the data into a form suitable for the chosen modeling algorithm.
- Specify a subset of the data to be used for training the model.
Choosing the Best Algorithm for your Classification Model.
- •Read the Data.
- • Create Dependent and Independent Datasets based on our Dependent and Independent features.
- •Split the Data into Training and Testing sets.
- • Train our Model for different Classification Algorithms namely XGB Classifier, Decision Tree, SVM Classifier, Random Forest Classifier.
- •Select the Best Algorithm.
Why use Data Model?
- Ensures that all data objects required by the database are accurately represented.
- A data model helps design the database at the conceptual, physical and logical levels.
- Data Model structure helps to define the relational tables, primary and foreign keys and stored procedures.
Model: A machine learning model can be a mathematical representation of a real-world process. The learning algorithm finds patterns in the training data such that the input parameters correspond to the target. The output of the training process is a machine learning model which you can then use to make predictions.
Ideation
- Align on the problem. As discussed, machine learning needs to be used to solve a real business problem.
- Choose an objective function. Based on the problem, decide what the goal of the model should be.
- Define quality metrics. How would you measure the model's quality?
- Brainstorm potential inputs.
The process of prediction engineering is captured in three steps:
- Identify a business need that can be solved with available data.
- Translate the business need into a supervised machine learning problem.
- Create label times from historical data.
