Programming assignments from all courses in the Coursera TensorFlow Developer specialization offered by deeplearning.ai.
The TensorFlow Developer specialization on Coursera contains four courses:
- Course 1: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
- Course 2: Convolutional Neural Networks in TensorFlow
- Course 3: Natural Language Processing in TensorFlow
- Course 4: Sequences, Time Series and Prediction
- TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models.In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. This program can help you prepare for the Google TensorFlow Certificate exam and bring you one step closer to achieving the Google TensorFlow Certificate.
In the DeepLearning.AI TensorFlow Developer Professional Certificate program, you'll get hands-on experience through 16 Python programming assignments. By the end of this program, you will be ready to:
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Build and train neural networks using TensorFlow
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Improve your network’s performance using convolutions as you train it to identify real-world images
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Teach machines to understand, analyze, and respond to human speech with natural language processing systems
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Process text, represent sentences as vectors, and train a model to create original poetry!
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Best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for a computer vision applications.
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Handle real-world image data and explore strategies to prevent overfitting, including augmentation and dropout.
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Build natural language processing systems using TensorFlow.
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Apply RNNs, GRUs, and LSTMs as you train them using text repositories.
I share the assignment notebooks with my prefilled and from the contributors code structred as in the course Course/Week The assignment notebooks are subject to changes through time.
Once you enrolled to the course, you are invited to join a slack workspace for this specialization: Please join the Slack workspace by going to the following link deeplearningai-nlp.slack.com This Slack workspace includes all courses of this specialization.
Course 1: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
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Week 2 Labs & Assignments:
- Week 2/ungraded_labs
- No Assignments
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Week 4 Labs & Assignments:
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Week 1 Labs & Assignments:
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Week 2 Labs & Assignments:
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Week 3 Labs & Assignments:
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Week 4 Labs & Assignments:
- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
- Convolutional Neural Networks in TensorFlow
- Natural Language Processing in TensorFlow
- Sequences, Time Series and Prediction
- TensorFlow Developer specialization(Final Certificate)
The gem is available as open source under the terms of the MIT license.
I recognize the hard time people spend on building intuition, understanding new concepts and debugging assignments. The solutions uploaded here are only for reference. They are meant to unblock you if you get stuck somewhere. Please do not copy any part of the code as-is (the programming assignments are fairly easy if you read the instructions carefully). Similarly, try out the quizzes yourself before you refer to the quiz solutions.