Deep Learning With PyTorch Training
Python and Selenium WebDriver Automating web applications for testing is the main emphasis of the training. It teaches participants how to develop effective Python test scripts, build reliable automated test frameworks, and interact with web elements using Selenium WebDriver. This course covers web automation basics, browser control, data-driven testing, and Python automation best practices.
- 10+ Courses
- 30+ Projects
- 400 Hours
Deep Learning With PyTorch Training is suitable for the following target audiences:
Software Testers and QA Engineers: Software testers and QA engineers are testing experts who wish to learn Python and Selenium for web automation testing.
Developers interested in Testing: Developers who want to learn more about Python and Selenium automation testing.
Transitioning to Automation: Manual testers are aiming to switch to automated testing to improve their job chances.
IT professionals and test automation enthusiasts: People working in the IT industry or interested in automation who wish to become proficient in Python and Selenium WebDriver to increase testing effectiveness.
Automation Test Engineer: Creating and running Python and Selenium-based automated test scripts to guarantee the caliber of web apps.
QA Automation Engineer: Creating web application automated testing frameworks and tactics to guarantee excellent software quality.
Software Development Engineer in Test (SDET): The Software Development Engineer in Test (SDET) creates automated testing solutions by combining development and testing expertise.
Test Analyst/Software Tester: Software testers and test analysts use Python and Selenium to test web applications and ensure effective test cycles.
Selenium and Python experts are in high demand in the USA and Canada due to the increasing need for automation testing, particularly in sectors like technology, finance, healthcare, and e-commerce. They offer competitive pay and excellent prospects for career advancement.
- Overview of AI, Machine Learning, and Deep Learning
- Fundamentals of neural networks
- Setting up the development environment, PyTorch, and Python
- Tensors and fundamental functions
- Concepts of GPU versus CPU training
- Practical: Basic neural networks and tensor operations
- Multilayer networks and perceptrons
- Functions of activation (ReLU, Sigmoid, Tanh)
- Propagation both forward and backward
- Fundamentals of optimization and loss functions
- Using the torchon module
- Practical: Image classification using a basic datasetÂ
- Variants of gradient descent
- Optimisers (RMSprop, Adam, and SGD)
- Underfitting versus overfitting
- Regularisation strategies (L2, Dropout)
- Metrics for model evaluation
- Practical: Boost model efficiencyÂ
- Fundamentals of image processing
- Pooling, convolution, and feature maps
- Overview of popular CNN architectures
- Concepts of transfer learning
- Enhancement of data
- Practical: CNN for classifying imagesÂ
- Concepts of sequential data
- GRU, LSTM, and RNN architectures
- Time-series and text applications
- Fundamentals of embeddings
- Sequence model training
- Practical: Text classification model
- Overview of attention mechanisms
- Introduction to Transformers
- Autoencoders
- Fundamentals of generative models
- Concepts of model interpretability
- Practical: Construct an autoencoderÂ
- Model loading and saving
- Pipelines for inference
- Optimisation of models for implementation
- Using APIs to serve models
- AI’s ethical implications
- Practical: Install the trained model locallyÂ
- Complete project development
- Preprocessing and dataset selection
- Design and tweaking of the model
- Performance assessment
- Code review and presentation
- Capstone Project: A practical deep learning application (such as an anomaly detector, sentiment analyzer, or picture classifier)
While not required, a basic understanding of linear algebra and Python is beneficial.
It greatly accelerates training but is not necessary.
PyTorch’s versatility makes it popular in both industry and research.
Indeed, the course makes use of real-world datasets.
The essential maths is used to intuitively illustrate key ideas.
Yes, but it is advised to have some basic programming expertise.
Indeed, basic deployment techniques are presented.
Indeed, both domains are presented.
Data scientists, ML engineers, and AI engineers.
Yes, in Week 8, a complete end-to-end project.
We currently offer online sessions with flexible weekday/weekend batches for 8 weeks. All sessions are recorded. You’ll have access to the recordings, along with support from instructors and peers in our learning portal.
You can register via our websitehttps://mediumblue-spoonbill-560747.hostingersite.com/, or reach out to our support teams via phone, email, or WhatsApp. We’ll help you with batch schedules and payment options.
Email info@mediumblue-spoonbill-560747.hostingersite.com  Call Us +1-347-4082054
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Job opportunities in USA and Canada
Automation Test Engineer: Creating and running Python and Selenium-based automated test scripts to guarantee the caliber of web apps.
QA Automation Engineer: Creating web application automated testing frameworks and tactics to guarantee excellent software quality.
Software Development Engineer in Test (SDET): The Software Development Engineer in Test (SDET) creates automated testing solutions by combining development and testing expertise.
Test Analyst/Software Tester: Software testers and test analysts use Python and Selenium to test web applications and ensure effective test cycles.
Selenium and Python experts are in high demand in the USA and Canada due to the increasing need for automation testing, particularly in sectors like technology, finance, healthcare, and e-commerce. They offer competitive pay and excellent prospects for career advancement.
