- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
A
C
D
G
L
M
N
P
R
S
T
X
- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
What is PyTorch?
PyTorch is a machine learning library used to develop and train deep learning models based on neural networks. Using graphics processing units, it is a Python-based scientific computing package. Also, it is one of the most popular Deep Learning research platforms, designed to provide maximum flexibility and speed. It offers two of the most high-level features, tensor computations with GPU acceleration and deep neural networks built on tape-based autograd systems. In addition, PyTorch is a Machine Learning framework created by Facebook in October 2016 and is based on the Torch library. PyTorch is designed to provide good flexibility and high speeds for deep neural network implementation.
Examples of PyTorch
PyTorch is known for its convenience and flexibility, with measures covering reinforcement learning, image classification, and natural language processing.
1. Computer Vision
A convolution neural network is used to develop image classification, object detection, and generative applications. PyTorch allows programmers to process images and videos to create accurate and precise computer vision models.
2. Natural Language Processing
It can be used to create language translators, language models, and chatbots. It uses RNNs, LSTMs, etc., to develop natural language and processing models.
3. Reinforcement Learning
Robotics for automation, business strategy planning, robot motion control, etc., are some of its uses. It builds models using Deep Q learning architecture.
Why is PyTorch important?
PyTorch has gained popularity among research-oriented developers since it supports dynamic training. Moreover, it is an excellent choice for a more straightforward debugging experience. TensorFlow provides a wide range of options for high-level model development and is usually considered more mature than PyTorch. Tensors are at the heart of any deep learning framework, allowing you to create, combine, and process tensors as they flow through a network (called a computation graph) through an object-oriented API.
For image classification, object detection, and generative applications, it uses image classification, object detection, and productive applications use a convolution neural network. PyTorch allows a programmer to process images and videos to develop computer vision models that are highly accurate and precise.
PyTorch vs. Other Technologies & Methodologies
PyTorch vs. Tensorflow
Two of the most popular Deep Learning frameworks today are PyTorch and TensorFlow. There has been a longstanding debate over which framework is best, with each camp having its share of fervent supporters.
PyTorch and TensorFlow have developed so rapidly over their relatively short lives that the debate landscape constantly shifts. Many outdated or incomplete information complicates the discussion of which framework will win in a given domain.
TensorFlow and PyTorch provide valuable abstractions for reducing boilerplate code and speeding up model development. PyTorch may feel more "pythonic" and has an object-oriented approach, while TensorFlow offers a variety of options.