Reading Digits in Natural Images With Unsupervised Feature Learning

Machine Learning

• Final Updated : 03 Dec, 2021

Car Learning is the subject field that gives computers the capability to learn without being explicitly programmed. ML is one of the most heady technologies that one would have ever come beyond. As information technology is axiomatic from the name, it gives the computer that makes it more similar to humans: The power to learn . Car learning is actively being used today, perhaps in many more places than one would expect.

Recent Articles on Machine Learning !

Introduction :

1. Getting Started with Auto Learning
2. An Introduction to Auto Learning
3. What is Machine Learning ?
4. Introduction to Data in Machine Learning
5. Demystifying Machine Learning
6. ML – Applications
7. Best Python libraries for Machine Learning
8. Artificial Intelligence | An Introduction
9. Car Learning and Artificial Intelligence
10. Difference between Machine learning and Artificial Intelligence
11. Agents in Artificial Intelligence
12. x Bones Machine Learning Interview Questions

Data and It'southward Processing:

1. Introduction to Data in Automobile Learning
2. Agreement Data Processing
3. Python | Create Test DataSets using Sklearn
4. Python | Generate examination datasets for Machine learning
5. Python | Data Preprocessing in Python
6. Data Cleansing
7. Feature Scaling – Part 1
8. Characteristic Scaling – Part 2
9. Python | Characterization Encoding of datasets
10. Python | One Hot Encoding of datasets
11. Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python
12. Dummy variable trap in Regression Models

Supervised learning :

1. Getting started with Nomenclature
2. Basic Concept of Nomenclature
3. Types of Regression Techniques
4. Classification vs Regression
5. ML | Types of Learning – Supervised Learning
6. Multiclass classification using scikit-learn
7. Slope Descent :
   • Gradient Descent algorithm and its variants
   • Stochastic Gradient Descent (SGD)
   • Mini-Batch Gradient Descent with Python
   • Optimization techniques for Slope Descent
   • Introduction to Momentum-based Slope Optimizer
8. Linear Regression :
   • Introduction to Linear Regression
   • Gradient Descent in Linear Regression
   • Mathematical explanation for Linear Regression working
   • Normal Equation in Linear Regression
   • Linear Regression (Python Implementation)
   • Elementary Linear-Regression using R
   • Univariate Linear Regression in Python
   • Multiple Linear Regression using Python
   • Multiple Linear Regression using R
   • Locally weighted Linear Regression
   • Generalized Linear Models
   • Python | Linear Regression using sklearn
   • Linear Regression Using Tensorflow
   • A Practical arroyo to Simple Linear Regression using R
   • Linear Regression using PyTorch
   • Pyspark | Linear regression using Apache MLlib
     
   • ML | Boston Housing Kaggle Challenge with Linear Regression
9. Python | Implementation of Polynomial Regression
10. Softmax Regression using TensorFlow
11. Logistic Regression :
    • Agreement Logistic Regression
    • Why Logistic Regression in Classification ?
    • Logistic Regression using Python
    • Cost function in Logistic Regression
    • Logistic Regression using Tensorflow
12. Naive Bayes Classifiers
13. Support Vector:
    • Support Vector Machines(SVMs) in Python
    • SVM Hyperparameter Tuning using GridSearchCV
    • Support Vector Machines(SVMs) in R
    • Using SVM to perform classification on a non-linear dataset
14. Decision Tree:
    • Decision Tree
    • Decision Tree Regression using sklearn
    • Decision Tree Introduction with example
    • Decision tree implementation using Python
    • Conclusion Tree in Software Engineering
15. Random Woods:
    • Random Forest Regression in Python
    • Ensemble Classifier
    • Voting Classifier using Sklearn
      
    • Bagging classifier

Unsupervised learning :

1. ML | Types of Learning – Unsupervised Learning
2. Supervised and Unsupervised learning
3. Clustering in Machine Learning
4. Dissimilar Types of Clustering Algorithm
5. K means Clustering – Introduction
6. Elbow Method for optimal value of grand in KMeans
7. Random Initialization Trap in G-Ways
8. ML | K-ways++ Algorithm
9. Analysis of test data using K-Means Clustering in Python
10. Mini Batch M-means clustering algorithm
11. Mean-Shift Clustering
12. DBSCAN – Density based clustering
13. Implementing DBSCAN algorithm using Sklearn
14. Fuzzy Clustering
15. Spectral Clustering
16. Eyes Clustering
17. OPTICS Clustering Implementing using Sklearn
18. Hierarchical clustering (Agglomerative and Divisive clustering)
19. Implementing Agglomerative Clustering using Sklearn
20. Gaussian Mixture Model

Reinforcement Learning:

1. Reinforcement learning
2. Reinforcement Learning Algorithm : Python Implementation using Q-learning
3. Introduction to Thompson Sampling
4. Genetic Algorithm for Reinforcement Learning
5. SARSA Reinforcement Learning
6. Q-Learning in Python

Dimensionality Reduction :

1. Introduction to Dimensionality Reduction
2. Introduction to Kernel PCA
3. Principal Component Analysis(PCA)
4. Principal Component Analysis with Python
5. Low-Rank Approximations
6. Overview of Linear Discriminant Assay (LDA)
7. Mathematical Explanation of Linear Discriminant Analysis (LDA)
8. Generalized Discriminant Analysis (GDA)
9. Independent Component Analysis
10. Feature Mapping
11. Extra Tree Classifier for Feature Choice
12. Chi-Foursquare Test for Characteristic Selection – Mathematical Caption
13. ML | T-distributed Stochastic Neighbour Embedding (t-SNE) Algorithm
14. Python | How and where to use Characteristic Scaling?
15. Parameters for Characteristic Pick
16. Underfitting and Overfitting in Auto Learning

Natural Linguistic communication Processing :

1. Introduction to Tongue Processing
2. Text Preprocessing in Python | Fix – 1
3. Text Preprocessing in Python | Prepare 2
4. Removing stop words with NLTK in Python
5. Tokenize text using NLTK in python
6. How tokenizing text, sentence, words works
7. Introduction to Stemming
8. Stemming words with NLTK
9. Lemmatization with NLTK
10. Lemmatization with TextBlob
11. How to get synonyms/antonyms from NLTK WordNet in Python?

Neural Networks :

1. Introduction to Bogus Neutral Networks | Ready one
2. Introduction to Artificial Neural Network | Gear up 2
3. Introduction to ANN (Artificial Neural Networks) | Set up 3 (Hybrid Systems)
4. Introduction to ANN | Prepare iv (Network Architectures)
5. Activation functions
6. Implementing Artificial Neural Network grooming procedure in Python
7. A single neuron neural network in Python
8. Convolutional Neural Networks
   • Introduction to Convolution Neural Network
   • Introduction to Pooling Layer
   • Introduction to Padding
   • Types of padding in convolution layer
   • Applying Convolutional Neural Network on mnist dataset
9. Recurrent Neural Networks
   • Introduction to Recurrent Neural Network
   • Recurrent Neural Networks Explanation
   • seq2seq model
   • Introduction to Long Brusk Term Memory
   • Long Curt Term Retentivity Networks Explanation
   • Gated Recurrent Unit Networks(GAN)
   • Text Generation using Gated Recurrent Unit Networks
10. GANs – Generative Adversarial Network
    • Introduction to Generative Adversarial Network
    • Generative Adversarial Networks (GANs)
    • Apply Cases of Generative Adversarial Networks
    • Edifice a Generative Adversarial Network using Keras
    • Modal Collapse in GANs
11. Introduction to Deep Q-Learning
12. Implementing Deep Q-Learning using Tensorflow

ML – Deployment :

1. Deploy your Auto Learning web app (Streamlit) on Heroku
2. Deploy a Machine Learning Model using Streamlit Library
3. Deploy Motorcar Learning Model using Flask
4. Python – Create UIs for prototyping Machine Learning model with Gradio
5. How to Prepare Data Earlier Deploying a Car Learning Model?
6. https://www.geeksforgeeks.org/deploying-ml-models-as-api-using-fastapi/?ref=rp
7. Deploying Scrapy spider on ScrapingHub

ML – Applications :

1. Rainfall prediction using Linear regression
2. Identifying handwritten digits using Logistic Regression in PyTorch
3. Kaggle Chest Cancer Wisconsin Diagnosis using Logistic Regression
4. Python | Implementation of Picture show Recommender System
5. Support Vector Machine to recognize facial features in C++
6. Decision Trees – Fake (Counterfeit) Money Puzzle (12 Coin Puzzle)
7. Credit Card Fraud Detection
8. NLP assay of Restaurant reviews
9. Applying Multinomial Naive Bayes to NLP Issues
10. Image compression using Thousand-ways clustering
11. Deep learning | Image Caption Generation using the Avengers EndGames Characters
12. How Does Google Use Machine Learning?
13. How Does NASA Use Automobile Learning?
14. 5 Heed-Blowing Ways Facebook Uses Machine Learning
    
15. Targeted Advertising using Car Learning
16. How Car Learning Is Used past Famous Companies?

Misc :

1. Blueprint Recognition | Introduction
2. Calculate Efficiency Of Binary Classifier
3. Logistic Regression v/s Determination Tree Classification
4. R vs Python in Datascience
5. Explanation of Primal Functions involved in A3C algorithm
6. Differential Privacy and Deep Learning
7. Artificial intelligence vs Machine Learning vs Deep Learning
8. Introduction to Multi-Task Learning(MTL) for Deep Learning
9. Top 10 Algorithms every Machine Learning Engineer should know
10. Azure Virtual Automobile for Machine Learning
11. thirty minutes to machine learning
12. What is AutoML in Machine Learning?
13. Defoliation Matrix in Machine Learning

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