Gain insights into advanced deep learning and NLP by building 12 real-world projects using tools like TensorFlow and scikit-learn. Enhance your portfolio with industry-relevant skills.

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In this course, you'll not only learn advanced deep learning concepts, but you'll also practice building some advanced deep learning and Natural Language Processing (NLP) projects. By the end, you will be able to utilize deep learning algorithms that are used at large in industry.

 

This is a project-based course with 12 projects in total. This will get you used to building real-world applications that are being used in a wide range of industries. You will be exposed to the most common tools used for machine learning projects including: NumPy, Matplotlib, scikit-learn, Tensorflow, and more.

 

It’s recommended that you have a firm grasp in these topic areas: Python basics, Numpy and Pandas, and Artificial Neural Networks.

 

Once you’re finished, you will have the experience to start building your own amazing projects, and some great new additions to your portfolio.

Building Advanced Deep Learning and NLP Projects


# Problem with previous word representations
We already saw that one simple way to create a numeric representation of the text is to create a vocabulary of our dataset and assign a number to each word. However, this has one major problem: there will be too many sparse vectors. This means that, after padding, we could have many vectors that will contain almost more than half of the values as 0s.

Therefore, we will need a way to represent the vectors in a compact form; this representation is called `Embedding Distributed Representation`.
* This representation is very powerful (if you have a large dataset).
* Using the vocabulary approach is not a good method to use for deep learning based models.

In the next lesson, we are going to start our project. There we will be using embedding layers and will see how we can convert the vocabulary based vectors to compact vectors.

# What is an embedding layer?
It is basically a big matrix of size `(vocabulary, K)` where K represents the length of the vector. For example, we discussed GloVe in previous chapters; here the value of K would be 300.

# How does an embedding layer work?
An embedding layer is just a matrix as we discussed above. Initially, we passed the input directly to the RNN cell, but now we will pass our input to the embedding layer, and the output from this layer will be fed into the RNN cell. Look at the simple architecture below to get a better understanding.

In this lesson, we are going to learn about embedding layers.

Welcome to the Course

Project: Build a COVID-19 Detection System Using X-Rays

Project: Building a Pokemon Classifier Using Transfer Learning

Project: Text Generation Using Markov Chains

Word Embedding: Two Mini Projects

Project: IMDB Reviews Sentiment Analysis

Project: Deciphering Text Using Character-Level RNNs

Project: Emoji Predictor Using Transfer Learning in NLP

Final Exam

Where to Go Next?

Embedding Layer: Word Representation

Problem with previous word representations