UNIT-5-ML

In this post, you can see the Introduction to Artificial Neural Networks and Deep Learning. This content was created according to the JNTUK R20 syllabus.

Well! Let's get into a brief introduction to artificial neural networks. A neural network is a method that was developed by drawing inspiration from the biological neurons of the human brain, or, as we can describe it, a set of algorithms that helps us recognize or detect patterns and relationships. ANN was first introduced in 1943 by neurophysiologist Warren McCulloch and mathematician Walter Pitts. There are numerous applications of ANN in medical diagnosis, image recognition, speech recognition, machine translation, etc.

Ultimately, ANN consists of three main layers: the input layer, the hidden layer, and the output layer.

Input Layer: takes input along with certain weights and biases.

Hidden Layer: This layer is the heart of the model. In this layer, input taken from the previous layer is processed by various layers. It evaluates the input using the activation function and delivers an output.

Output Layer: The output delivered from the hidden layer was received by the output layer, which displays the final output.

Back propagation: if an error occurs in the output, then the model backtracks to the input layer, updates the weights, and again processes them through the hidden layer to deliver the correct output.

The perceptron is the simplest form of neural network.

In this guide, you can learn how to install TensorFlow, which is an end-to-end open-source platform for deep learning. Using this, you can deploy your own model for your project. Rather than machine learning, deep learning, which is a subset of machine learning, can deal with large and complex datasets by using this type of open-source framework. TensoFlow was developed by the Google Brains team in 2015.  It was mainly used for deploying high-end machine learning applications and deep learning applications.  Keras is also an open-source library to build machine learning models and deep learning models. Compared to tensorflow keras used for small applications by individual developers, tensorflow consists of inbuilt keras in it to provide flexibility to users and developers.

In loading and preprocessing data with TensorFlow, you can see six major phases: load data,data augmentation, normalization, shuffling of data, splitting into batches, Caching these six phases will help you maintain the data in the correct format.

Load data: In this phase, it deals with how data can be loaded into a model and how it can be partitioned into training, validation, and test data.

Data Augmentation: This phase is nothing but the transforming of data from its original shape to another shape that can be flexible to model to perform the task.

Normalization: In this phase, it can be used to normalize the data, which is transformed in data augmentation using the transform function.

shuffling of data: This method can enhance the prevention of model overfitting by using data that is shuffled every time.

splitting into batches: this can split the dataset into small batches to prevent the overfitting and underfitting of the deep learning model.

Caching is used to declare the memory size of batches at the time of shuffling.

 

What was covered in the material??

• introduction of ANN

• classifications of ANN

• layers of ANN and backpropagation

• MultiLayerPerceptron using Keras

• How do I install TensorFlow?

• Loading and preprocessing data with TensorFlow's six phases
  

link to the material: UNIT-5-ML

ML-UNIT-4

ML-UNIT-4

Welcome to letslearningcse.blogspot.com,

Note: This material was prepared based on the JNTU R20 syllabus for AI and ML.

Now, I am going to give you a brief introduction to ML Unit 4 [Unsupervised Learning].

Let's get into the topic!! As we all know, machine learning is basically classified into three types. Please take a look at the flow chart below.

 

Now, I hope you get a little bit of clarity about the concept of what we are going to discuss.

What is unsupervised learning?

Unsupervised learning is a type of machine learning technique that enables a model to be trained on unlabeled data and predict patterns based on the trained data without any human interference.

Ex: recommendation systems, sentiment analysis, search engines

It has two types of techniques:

• clustering 
• association

Association:

It is a type of unsupervised learning that is used to find out the dependency of one data item on another data item, which can lead to building a recommendation system and sentiment analysis.

clustering:

Clustering is nothing but the grouping of similar objects into a single cluster.

It is mainly used for statistical data analysis.

It has two types of clustering scenarios:

• Soft clustering: In soft clustering, data points can't belong to only one cluster. We can't make a single cluster.
  

• Hard clustering: in hard clustering, data points can belong to only one cluster. It can make a single cluster of items.
  

Ex: k-means, dbscan, agglomerative, hierarchical clustering, etc.

Clustering Methods:

• Partition clustering or centroid clustering
• Density-based clustering
• Distribution-based clustering
• Hierarchical clustering

Applications of clustering:

• Customer segmentation
• data analysis
• Dimensionality Reduction
• Feature Engineering
• outlier detection
• semi-supervised learning
• search engines
• image segmentation

Image segmentation:

It is a process that splits the picture or image into numerous regions that belong to the same label. It splits the image, pixel by pixel.

It has four types of image segmentation techniques:

• Color Segmentation
• Instance Segmentation
• Semantic Segmentation
• Panoptic Segmentation

Color segmentation is a type of image segmentation technique. In this method, pixels having the same color are assigned to the same segment or class label.

Instance segmentation is a type of image segmentation technique. In this method, even objects belonging to the same category can be assigned different class labels.

Semantic segmentation is a type of image segmentation technique. This method was quite opposite to instance segmentation. In this method, objects belonging to the same category are assigned the same class label.

Panoptic segmentation is a type of image segmentation technique. This method is a combination of instance segmentation and semantic segmentation. Instance segmentation can be used for images that have countable classes, whereas semantic segmentation can be used for uncountable objects.

image can be identified by:

• height of the image
• width of the image
• number of channels

gaussian mixture:

It is used to organize the data by checking its similarities and differences between them.

dimensionality reduction:

It is used to reduce the variables in the training portion and helps the core essence of machine learning models.

What are you going to see in this material?

• about clustering algorithms 
• K-means clustering algorithm
• DBSCAN clustering algorithm 
• image segmentation
• gaussian mixture
• dimensionality reduction
• types of PCA 

link for the material: ML-UNIT-4