NOTE: Please donot use the solved assignments to cheat through the course. Use them only as a reference when stuck somewhere.
This projject contains solved assignments and labs for the Coursera course provided by DeepLearning.AI (DeepLearning.AI TensorFlow Developer Professional Certificate). Please donot use the materials to clear your course. This should be referred only when you have been struggling for a long time to solve the project and are unsuccessful.
Course 1: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
- Week 1: A New Programming Paradigm: soft introduction to what Machine Learning and Deep Learning are, and how they offer you a new programming paradigm, giving you a new set of tools to open previously unexplored scenarios.
- Week 2: Introduction to Computer Vision: solve problems of computer vision with just a few lines of code.
- Week 3: Enhancing Vision with Convolutional Neural Networks: Improve computer vision using convolutional neural networks.
- Week 4: Using Real-world Images: work on handling real life images.
- Week 1: Exploring a Larger Dataset: understand how to use much larger datasets in tensorflow.
- Week 2: Augmentation: A technique to avoid overfitting: understand implementation of augmentation to solve over-fitting problem.
- Week 3: Transfer Learning: Transfer learning can help solve the problem of unavailablity of large datasets -- where people with models trained on large datasets train them, so that you can either use them directly, or, you can use the features that they have learned and apply them to your scenario.
- Week 4: Multiclass Classifications: work on handling multi class classification problems.
- Week 1: Sentiment in text: The first step in understanding sentiment in text, and in particular when training a neural network to do so is the tokenization of that text. This is the process of converting the text into numeric values, with a number representing a word or a character. This week you'll learn about the Tokenizer and pad_sequences APIs in TensorFlow and how they can be used to prepare and encode text and sentences to get them ready for training neural networks!
- Week 2: Word Embeddings: This week you'll learn about Embeddings, where these tokens are mapped as vectors in a high dimension space. With Embeddings and labelled examples, these vectors can then be tuned so that words with similar meaning will have a similar direction in the vector space. This will begin the process of training a neural network to understand sentiment in text -- and you'll begin by looking at movie reviews, training a neural network on texts that are labelled 'positive' or 'negative' and determining which words in a sentence drive those meanings.
- Week 3: Sequence models: Sentiment can also be determined by the sequence in which words appear. For example, you could have 'not fun', which of course is the opposite of 'fun'. This week you'll start digging into a variety of model formats that are used in training models to understand context in sequence!
- Week 4: Sequence models and literature: Given a body of words, you could conceivably predict the word most likely to follow a given word or phrase, and once you've done that, to do it again, and again. With that in mind, this week you'll build a poetry generator. It's trained with the lyrics from traditional Irish songs, and can be used to produce beautiful-sounding verse of it's own!
- Week 1: Sequences and Prediction: take a look at some of the unique considerations involved when handling sequential time series data -- where values change over time, like the temperature on a particular day, or the number of visitors to your web site. We'll discuss various methodologies for predicting future values in these time series, building on what you've learned in previous courses!
- Week 2: Deep Neural Networks for Time Series: Having explored time series and some of the common attributes of time series such as trend and seasonality, and then having used statistical methods for projection, let's now begin to teach neural networks to recognize and predict on time series!
- Week 3: Recurrent Neural Networks for Time Series: Recurrent Neural networks and Long Short Term Memory networks are really useful to classify and predict on sequential data. This week we'll explore using them with time series..
- Week 4: UsingReal-world time series data: On top of DNNs and RNNs, let's also add convolutions, and then put it all together using a real-world data series -- one which measures sunspot activity over hundreds of years, and see if we can predict using it.
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