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Applied Deep Learning with PyTorch
Nivå: Intermediate
Snittbetyg:
3,00/5 Based on 1 Reviews
Starting with the basics of deep learning and their various applications, Applied Deep Learning with PyTorch shows you how to solve trending tasks, such as image classification and natural language processing by understanding the different architectures of the neural networks.
Nyckelfunktioner:
- After-course instructor coaching benefit
- After-course computing sandbox included
- Learning Tree end-of-course exam included
Du kommer lära dig att:
- Detect a variety of data problems to which you can apply deep learning solutions
- Learn the PyTorch syntax and build a single-layer neural network with it
- Build a deep neural network to solve a classification problem
- Develop a style transfer model
- Implement data augmentation and retrain your model
- Build a system for text processing using a recurrent neural network
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LIVE, LÄRARLEDD
I klass & Live, Online-utbildning
- 2-day instructor led training course
- One-on-one after-course instructor coaching
- Pay later by invoice -OR- at the time of checkout by credit card
FÖRETAGSINTERN UTBILDNING
Teamträning
- Använd denna eller någon annan utbildning i ditt företag
- Fullskalig programutveckling
- Levereras när, var och hur du vill
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- Coaching av ett expertteam
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Kontakta ossUtveckla dig och ditt team med anpassade eller öppna kurser alternativt e-learning
Learning Tree erbjuder kundanpassad utbildning hos er, öppna kurser i Stockholm, London eller Washington, möjlighet att delta via våra Anywhere centers (Malmö, Göteborg, Linköping, Stockholm eller Borlänge) eller olika former av e-learning med lärarstöd. Läs mer på www.learningtree.se/priser .
I klass & Live, Online-utbildning
Note: This course runs for 2 dagar
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8 - 9 mar 9:00 - 4:30 GMT Online (AnyWare)
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24 - 25 maj 9:00 - 4:30 BST Online (AnyWare)
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23 - 24 aug 9:00 - 4:30 BST Online (AnyWare)
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3 - 4 nov 9:00 - 4:30 GMT Online (AnyWare)
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15 - 16 mar 9:00 - 4:30 EDT Online (AnyWare)
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11 - 12 maj 9:00 - 4:30 EDT Herndon, VA / Online (AnyWare)
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13 - 14 sep 9:00 - 4:30 EDT Ottawa / Online (AnyWare)
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8 - 9 nov 9:00 - 4:30 EST Herndon, VA / Online (AnyWare)
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Kurs med startgaranti
När du ser symbolen för “Guaranteed to Run” vid ett kurstillfälle vet du att kursen blir av. Garanterat.Important Applied Deep Learning with PyTorch Course Information
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Requirements
Some working knowledge of Python and familiarity with the basics of machine learning are a must. However, knowledge of NumPy and pandas will be beneficial, but not essential.
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Who Should Attend This Course
Applied Deep Learning with PyTorch is designed for data scientists, data analysts, and developers who want to work with data using deep learning techniques. Anyone looking to explore and implement advanced algorithms with PyTorch will also find this course useful.
Applied Deep Learning with PyTorch Course Outline
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Lesson 1: Introduction to Deep Learning and PyTorch
- Understanding Deep Learning
- PyTorch Introduction
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Lesson 2: Building Blocks of Neural Networks
- Introduction to Neural Networks
- Data Preparation
- Building a Neural Network
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Lesson 3: A Classification Problem Using DNN
- Problem Definition
- Dealing with an Underfitted or Overfitted Model
- Deploying Your Model
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Lesson 4: Convolutional Neural Networks
- Building a CNN
- Data Augmentation
- Batch Normalization
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Lesson 5: Style Transfer
- Style transfer
- Implementation of Style Transfer Using the VGG-19 Network Architecture
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Lesson 6: Analysing the Sequence of Data with RNNs
- Recurrent Neural Networks
- Long Short-Term Memory Networks (LSTMs)
- LSTM Networks in PyTorch
- Natural Language Processing (NLP)
- Sentiment Analysis in PyTorch
Teamträning
Applied Deep Learning with PyTorch FAQs
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Can I take this data science course online?
Yes! We know your busy work schedule may prevent you from getting to one of our classrooms which is why we offer convenient online training to meet your needs wherever you want, including online training.
Your Training Comes with a 100% Satisfaction Guarantee!*
*Partner-delivered courses may have different terms that apply. Ask for details.