Everyone thinks all these concepts are one and the same. But it has a huge variation, to put in simple words, lets take facial recognition example, AI is used to recognize people’s emotions in pictures, machine learning algorithms would input multiple images of human faces into the system. Deep learning will recognize patterns in the faces and emotions they share based on the images.
|Mimics or replicates human intelligence.||Allows machine to learn on its own. It learns from the data set and makes prediction depending on the scenario.||Deep learning algorithms attempt to model high level abstractions in data to determine high level meaning. It is more effective compared to ML.|
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Chat bots : Replacement of humans by chat bots. They feed the manual conversations to the AI, and it is trained to interact to human. Ex. Companies providing chat support by bots instead of human.
Sentiment analysis : Find the buying behavior and patterns of people and sales predictions.
Self driven cars : Making commute easier by having AI driven cars.
Facial expression recognition : Your facial expression recognition is identified and analyzed by a computer.
Image Tagging : Identify particular object in the group of images.
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There are different types of learning when it is concerned to machine learning. They include –
|Supervised learning||Unsupervised learning||Reinforcement learning|
|Definition||Training set has both predictors and predictions.||Training set has only predictors in the data set.||They can establish state-of-art results on any task.|
|Algorithm||Linear and logistic regression, Support vector machine, Naive Bayes||K-Means, Clustering algorithm, Dimensionality reduction algorithms||Q-Learning, State-Action-Reward-State-Action (SARSA), Deep Q Network (DQN)|
|Uses||Image recognition, speech recognition, forecasting||Pre-process the data, pre-train supervised learning algorithms.||Warehouses, Inventory management, delivery management, Power system, Financial systems.|
Object detection, Image segmentation are popular concepts in AI and deep learning. Object deduction is used to find the class and coordinates of the particular object in the image. Image segmentation is used to identify the object or information within the multiple items on the image.
Python is used to do web development, scrapping, math operations. The math operations are performed using NumPy, SciPy, Pandas etc.. Few web development frameworks are django, Flask, Cherry, Pyramid. It is famous for doing automation tasks.
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Go to anaconda.org
Anaconda – a suite of libraries as well as python. Jupyterlab, an extended version of iPython is used as an IDE for python. Install the Python version 3.6 which has deep learning libraries. Download it and install.
Numpy : A library which has all the computations.Matrix multiplication, convolution, addition, arrays, subtraction and some many other functions.
Scipy : An addition to numpy which has advanced functions like convolution, create variation, histograms.
Pandas : Most important library, converts every data to table(data frame) in runtime. Joining, merging, subset of data, viewing the data are possible.
Matplotlib & Seaborn : To visualize data, it is used to create plots, modify plots, histogram, line. Pie, bar charts can be created.
Tensorflow : 1.7 is the latest version, launched by Google. It is a library for deep learning. It uses a system of multi-layered nodes as a model to quickly set up and deploy, it has neural networks in the base. We can make 100 layers of the model. It supports both GPU and CPU. GPU’s are 20x faster than CPU.
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To read a file on python follow these steps:
Go to tensorflow.org
Using Anaconda installing python can be found in the website.
Tensorflow basic is of order 0 for simple number, order 1 is like a matrix of one row and so on.
Keras is another library on top of tensorflow. Keras uses tensorflow as backend. Go to keras.io/installation for installing with the command: pip install keras.
Tensorflow Objects :
It has 5 different important terminologies –
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