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Articles by Jaisshree
Page 8 of 10
Implement Deep Autoencoder in PyTorch for Image Reconstructionp
Machine learning is one of the branches of artificial intelligence that involves developing Statistical models and algorithms that can enable a computer to learn from the input data and make decisions or predictions without being hard programmed. It involves training the ML algorithms with large datasets so that the machine can identify patterns and relationships in the data. What is an Autoencoder? Neural network architectures with autoencoders are used for unsupervised learning tasks. It is made up of a network of encoders and decoders that have been trained to rebuild the input data by compressing it into a lower-dimensional representation ...
Read MoreGetting Started with Transformers
Computer science and artificial intelligence's Natural Language Processing (NLP) branch focuses on how computers and human language interact. It entails the creation of models and algorithms that can analyze, comprehend, and produce human language. Numerous issues, including language translation, sentiment analysis, text summarization, speech recognition, and question-answering systems, are resolved using NLP. As the amount of digital text data continues to increase exponentially and the need to glean insights and knowledge from this data increases, these applications have grown in significance. What are Transformers in NLP? Transformers, a specific type of neural network design, have become quite popular in NLP ...
Read MoreExplaining the Language in Natural Language
If you have ever talked with chatbots and used language translation tools, then you will know that these tools work exactly like a real human. This is possible because they use Natural Language Processing (NLP) techniques to understand the natural languages that humans use for communication. However, this is quite complex because each language has a different nature and structure along with various contexts. Natural Language Processing (NLP) uses a number of techniques to get outputs as close as the natural language. Some of these techniques include − Lemmatization − It is the process that reduces the ...
Read MoreClassification of Text Documents using the Naive Bayes approach in Python
Naive Bayes algorithm is a powerful tool that one can use to classify the words of a document or text in different categories. As an example, if a document has words like ‘humid’, ‘rainy’, or ‘cloudy’, then we can use the Bayes algorithm to check if this document falls in the category of a ‘sunny day’ or a ‘rainy day’. Note that the Naive Bayes algorithm works on the assumption that the words of the two documents under comparison are independent of each other. However, given the nuances of language, it is rarely true. This is why the algorithm’s ...
Read MoreBLEU Score for Evaluating Neural Machine Translation using Python
Using NMT or Neural Machine Translation in NLP, we can translate a text from a given language to a target language. To evaluate how well the translation is performed, we use the BLEU or Bilingual Evaluation Understudy score in Python. The BLEU Score works by comparing machine translated sentences to human translated sentences, both in n-grams. Also, with the increase in sentence length, the BLEU score decreases. In general, a BLEU score is in the range from 0 to 1 and a higher value indicates a better quality. However, achieving a perfect score is very rare. Note that the ...
Read MoreWhat is Standardization in Machine Learning
A dataset is the heart of any ML model. It is of utmost importance that the data in a dataset are scaled and are within a particular range, to provide accurate results. Standardization in machine learning , a type of feature scaling ,is used to bring uniformity to the datasets , resulting in independent variables and features of the same scale and range. Standardization transforms the standard deviation to 1 and the mean to 0 . In standardization, the mean is subtracted from each data point and the result obtained is divided by the standard deviation , resulting in standardized ...
Read MoreSPSA (Simultaneous Perturbation Stochastic Approximation) Algorithm using Python
A simultaneous perturbation stochastic optimization algorithm (SPSA) finds the minimum of an objective function by simultaneously perturbing the objective function. Using SPSA, the objective function gradient is estimated by evaluating a small number of functions at random perturbations. It is particularly useful when the objective function is noisy, non-differentiable, or has many parameters. A variety of applications, such as system identification, control, and machine learning, have been successfully implemented with this algorithm. Advantages Of Using The SPSA Algorithm The SPSA has been applied in various realms such as engineering, finance, and machine learning. It has several advantages ...
Read MoreSpaceship Titanic Project using Machine Learning in Python
The original Titanic project in Machine learning is aimed at finding whether a person on the Titanic will survive or not. However, this project named the spaceship Titanic is a bit different. The problem statement here is that a spaceship has people going on a trip in space. But due to a collision, a few people need to be transported to some other dimension or planet. Now this can’t be done randomly. So, we will use a Machine Learning technique in Python to find out who will get transported and who will not. Algorithm Step 1 − ...
Read MoreSkin Cancer Detection using TensorFlow in Python
Early detection of any disease, especially cancer, is very crucial for the treatment phase. One such effort made in this direction is the use of machine learning algorithms to detect and diagnose skin cancer with the help of a machine learning framework like Tensorflow. The traditional method of cancer detection is quite time-consuming and requires professional dermatologists. However, with the help of TensorFlow, not only can this process be made fast, but more accurate and efficient. Moreover, people who do not get timely access to doctors and dermatologists, can use this meanwhile. Algorithm Step 1 − Import the ...
Read MoreRainfall Prediction using Machine Learning
The power of machine learning has enabled us to predict rainfall with several algorithms, including Random Forest and XGBoost. There are no best algorithms for predicting rainfall, every algorithm has its advantages and disadvantages. The Random Forest is efficient with small datasets, while the XGboost is efficient with large datasets. In the same way, we can categorise other algorithms based on the needs of our projects. Our goal here is to build a predictive machine-learning model of rainfall based on Random Forests. Algorithm Import all the required libraries such as Pandas, Numpy, Sklearn, and matplotlib. Load the ...
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