AppliedAI

Hari Nair "Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. So, how do I start learning DL and Deep neural networks: Let us have a clear understanding of : What neural networks, deep learning Networks and how they work? Basic optimization theory, optimization techniques, gradients and loss functions Good understand of Python  The most popular techniques are: Multilayer Perceptron Networks. Convolutional Neural Networks. Long Short-Term Memory Recurrent Neural Networks. Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements (neurones) working in parallel to solve a specific problem. Neural networks learn by example. They cannot be programmed to perform a specific task. Neural architecture search Is the process of automating...

Computer vision   has received a lot of attention recently due to the popularity of Deep learning. Traditional machine learning algorithms are still very simple. Their training requires a lot of domain expertise and human intervention when errors occur.   OpenCV is one of the most popular library for computer vision. Every Machine Learning algorithm takes a Dataset as input and learns from this data. The algorithm goes through the data and identifies patterns in the data.  Deep learning algorithms, on the other hand, learn about the task at hand through a network of neurons that map the task as a hierarchy of concepts . Gray-scale image buffer which stores  image . Each pixel’s brightness is represented by a single 8-bit number, whose range is from 0 (black) to 255 (white) Deep learning algorithms also perform better when more data is given, which is not typical of machine learning algorithms.   The best applications of Googl...

"A picture is worth a thousand words - Python and Charts" Data visualization is the graphical representation of  data. Python provides an easy way (charts, graphs, and maps) to see and understand trends, outliers, and patterns in data. Transform data into visualizations Extract information and better understand the data Customize and organize visualizations Add interactivity to charts Matplotlib is a plotting library which produces charts in a variety of formats and interactive environments across platforms. Installing packages The following examples uses 2 basic charting libraries How to install plotlib and seaborn charting libraries pip install plotlib pip install seaborn Example-1 A simple chart using Randon numbers c1.py import matplotlib.pyplot as plt import numpy as np x = np.random.random((10, 1)) print(x) plt.plot(x, '*-') plt.show() Output Example-2 # Example-2.py import matplotlib.pyplot as plt # Sales in...