Deep Learning With TensorFlow

• Tensor Flow is a framework created by Google for creating Deep Learning models. Deep Learning is a category of machine learning models (=algorithms) that use multi-layer neural networks.
• Machine Learning has enabled us to build complex applications with great accuracy. Whether it has to do with images, videos, text, or even audio, Machine Learning can solve problems from a wide range. Tensor flow can be used to achieve all of these applications.

HOW CAN WE MAKE OUR MACHINES “THINK”?

Machine learning: allows computers to ‘learn’, Instead of giving the computer a set of instructions on how to do something, we give it instructions on how to learn to do something.

Deep learning: Technique for implementing Machine Learning.

Neural network: a collection of layers that transform the input in some way to produce an output.

SOME DEFINITIONS

• Tensor:-is an algebraic object that describes a (multilinear) relationship between sets of algebraic objects related to a vector space.
• Operations:- is a node in a tf. Graph that takes zero or more Tensor objects as input, and produces zero or more Tensor objects as output.
• Variables:-represents a tensor whose value can be changed by running ops on it. Specific ops allow you to read and modify the values of this tensor.
• Tensor Board:-is a tool for providing the measurements and visualizations needed during the machine learning workflow.

TYPES OF MACHINE LEARNING ALGORITHMS

TYPES OF REGRESSIONS

• Linear Regression:- a statistical regression method which is used for predictive analysis, very simple and easy algorithms which works on regression and shows the relationship between the continuous variables.
• Logistic Regression:- is another supervised learning algorithm that is used to solve the classification problems, dependent variables in a binary or discrete format such as 0 or 1.
• KNN:-a non-parametric method that, in an intuitive manner, approximates the association between independent variables and the continuous outcome by averaging the observations in the same neighborhood.

CLUSTERING

• The aim is to segregate groups with similar traits and assign them into clusters.
• Types of Clustering Methods:
• The aim is to segregate groups with similar traits and assign them into clusters.
• Types of Clustering Methods:

1. Partitioning methods.
2. Hierarchical clustering.
3. Fuzzy clustering.
4. Density-based clustering.
5. Model-based clustering.

SOME MORE ALGORITHM

• K-means:- is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster.
• SVM:- A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems.
• Neural Network:-are non-linear statistical data modeling or decision making tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. Feedforward:- Data enters at the inputs and passes through the network, layer by layer until it arrives at the outputs. During normal operation, that is when it acts as a classifier, there is no feedback between layers. This is why they are called feedforward neural networks. Backpropagation:- essence of neural net training. Error:-Abstract. Training of an artificial neural network (ANN) adjusts the internal weights of the network in order to minimize a predefined error measure.