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Posts

  • Informed Search

    In this final chapter you will be given a taste of more advanced hyperparameter tuning methodologies known as ''informed search''. This includes a methodology known as Coarse To Fine as well as Bayesian & Genetic hyperparameter tuning algorithms. You will learn how informed search differs from uninformed search and gain practical skills with each of the mentioned methodologies, comparing and contrasting them as you go. This is the Summary of lecture "Hyperparameter Tuning in Python", via datacamp.

    Aug 6, 2020

  • Random Search

    In this chapter you will be introduced to another popular automated hyperparameter tuning methodology called Random Search. You will learn what it is, how it works and importantly how it differs from grid search. You will learn some advantages and disadvantages of this method and when to choose this method compared to Grid Search. You will practice undertaking a Random Search with Scikit Learn as well as visualizing & interpreting the output. This is the Summary of lecture "Hyperparameter Tuning in Python", via datacamp.

    Aug 6, 2020

  • Grid search

    This chapter introduces you to a popular automated hyperparameter tuning methodology called Grid Search. You will learn what it is, how it works and practice undertaking a Grid Search using Scikit Learn. You will then learn how to analyze the output of a Grid Search & gain practical experience doing this. This is the Summary of lecture "Hyperparameter Tuning in Python", via datacamp.

    Aug 5, 2020

  • Hyperparameters and Parameters

    In this introductory chapter you will learn the difference between hyperparameters and parameters. You will practice extracting and analyzing parameters, setting hyperparameter values for several popular machine learning algorithms. Along the way you will learn some best practice tips & tricks for choosing which hyperparameters to tune and what values to set & build learning curves to analyze your hyperparameter choices. This is the Summary of lecture "Hyperparameter Tuning in Python", via datacamp.

    Aug 5, 2020

  • Diabetes Onset Detection

    In this project, it will show the Deep Neural Network with GridSearch for detecting Diabetes onset. GridSearch is used for hyperparameter tuning. The original data(PIMA indian diabetes dataset) is from UCI Open Repository.

    Aug 4, 2020

  • Understanding and Improving Deep Convolutional Networks in Keras

    There are many ways to improve training by neural networks. In this chapter, we will focus on our ability to track how well a network is doing, and explore approaches towards improving convolutional neural networks. This is the Summary of lecture "Image Processing with Keras in Python", via datacamp.

    Aug 4, 2020

  • DNA Classification

    In this project, it will show the Machine Learning Model for classifying DNA sequence. K-Nearest Neighborhood and Support Vector Machine and several algorithm for classification will be used. The original data is from UCI Machine Learning Repository.

    Aug 4, 2020

  • Going Deeper Convolutions in Keras

    Convolutional neural networks gain a lot of power when they are constructed with multiple layers (deep networks). In this chapter, you will learn how to stack multiple convolutional layers into a deep network. You will also learn how to keep track of the number of parameters, as the network grows, and how to control this number. This is the Summary of lecture "Image Processing with Keras in Python", via datacamp.

    Aug 4, 2020

  • Image Processing With Neural Networks

    Convolutional neural networks use the data that is represented in images to learn. In this chapter, we will probe data in images, and we will learn how to use Keras to train a neural network to classify objects that appear in images. This is the Summary of lecture "Image Processing with Keras in Python", via datacamp.

    Aug 3, 2020

  • Using Convolutions in Keras

    Convolutions are the fundamental building blocks of convolutional neural networks. In this chapter, you will be introducted to convolutions and learn how they operate on image data. You will also see how you incorporate convolutions into Keras neural networks. This is the Summary of lecture "Image Processing with Keras in Python", via datacamp.

    Aug 3, 2020

  • Stock Market Clustering with a KMeans algorithm

    In this project, it will show the clustering algorithm to detect similar companies based on stock market movement. The original data is from Yahoo Finance.

    Aug 2, 2020

  • Filters, Contrast, Transformation and Morphology

    You will learn to detect object shapes using edge detection filters, improve medical images with contrast enhancement and even enlarge pictures to five times its original size! You will also apply morphology to make thresholding more accurate when segmenting images and go to the next level of processing images with Python. This is the Summary of lecture "Image Processing in Python", via datacamp.

    Aug 2, 2020

  • Image restoration, Noise, Segmentation and Contours

    So far, you have done some very cool things with your image processing skills! In this chapter, you will apply image restoration to remove objects, logos, text, or damaged areas in pictures! You will also learn how to apply noise, use segmentation to speed up processing, and find elements in images by their contours. This is the Summary of lecture "Image Processing in Python", via datacamp.

    Aug 2, 2020

  • Advanced Operations, Detecting Faces and Features

    After completing this chapter, you will have a deeper knowledge of image processing as you will be able to detect edges, corners, and even faces! You will learn how to detect not just front faces but also face profiles, cat, or dogs. You will apply your skills to more complex real-world applications. Learn to master several widely used image processing techniques with very few lines of code! This is the Summary of lecture "Image Processing in Python", via datacamp.

    Aug 2, 2020

  • Introducing Image Processing and scikit-image

    Jump into digital image structures and learn to process them! Extract data, transform and analyze images using NumPy and Scikit-image. With just a few lines of code, you will convert RGB images to grayscale, get data from them, obtain histograms containing very useful information, and separate objects from the background! This is the Summary of lecture "Image Processing in Python", via datacamp.

    Jul 31, 2020