Women in Technology Association Organizes Data Scientist Deep Learning Training

About Education

• The training will be held between September and December, and there will be lessons at individually determined times for 10 hours per week in self-paced training. A one-on-one mentor meeting schedule will be prepared on a weekly determined day.
• Live lessons will be held every Friday between 19:00 and 22:00.
• The training capacity is 30 people and only female candidates will take part in this project.
• Udacity's international Nanodegree certificate will be awarded to those who successfully complete the program.

Conditions of Participation

• Graduated from universities, especially Engineering and STEM departments
• could not find a job
• Persons who have not received training from Wtech Academy before
• Fluent in English,
• Python programming knowledge

Selection Criteria:

• Success in the Hackerrank exam (English)
• Success in Hackerrank exam (Python)
• Positive interview process
• 100% participation of candidates in training

Click on the link to apply for the training.

Gains

• You will learn the basics of neural networks and create your first network with Python and NumPy. You will build multilayer neural networks and use the modern deep learning framework PyTorch to analyze real data.
• You will learn how to create convolutional meshes and use them to classify images (faces, melanomas, etc.) according to the patterns and objects that appear in them. You will use these networks to learn data compression and image noise removal.
• With PyTorch you will create your recurrent meshes and long short-term memory meshes; Do sentiment analysis and you'll use repetitive meshes to generate new text from TV scripts.
• With Ian Goodfellow, inventor of GANs, and Jun-Yan Zhu, creator of CycleGANs, you will learn to understand and apply the Deep Convolutional GAN ​​(generative competitor network) to create realistic images.
• You will train and deploy your own PyTorch sentiment analysis model using Amazon SageMaker on AWS. This model will be trained to do sentiment analysis in movie reviews (positive or negative reviews). You will build the model, deploy it, and create a gateway to access this model from a website.