Get hands-on experience in NLP with challenging applications.

Mastering Natural Language Processing

In this module, we’ll learn advanced deep learning concepts and practice building some advanced deep learning and natural language processing (NLP) projects. By the end, we’ll be able to utilize deep learning algorithms used at large in the industry. This is a project-based module with 12 projects in total. This will get us used to building real-world applications that are being used in a wide range of industries. We’ll be exposed to the most common tools used for machine learning projects, including NumPy, Matplotlib, scikit-learn, Tensorflow, and more.
Once we’re finished, we’ll have the experience to start building our amazing projects and some great new additions to our portfolio.

Building Advanced Deep Learning and NLP Projects

## Points to consider 

There might be a variety of questions in your mind such as:
1. When do you fine-tune?
2. How do you fine-tune?
3. How do you decide what type of transfer learning you should perform on a new dataset?



The answer to the above questions depends on two factors: the size of the new dataset and its similarity to the original dataset on which your pre-trained model was trained. We have listed four common rules that you should keep in mind while performing transfer learning strategies. These are:


* ***New dataset is large and very different from the original dataset:*** since the dataset is large, we can go ahead and train the ConvNet from scratch, but it would take some time to train a complex network. So, it is common to use a fine-tune approach in this case. It might be the case that fine-tuning the ConvNet may not produce good results. This is due to the fact that the new dataset is very different from the original one. In that case, you can train ConvNet from scratch.

# Points to consider 

There might be a variety of questions in your mind such as:
1. When do you fine-tune?
2. How do you fine-tune?
3. How do you decide what type of transfer learning you should perform on a new dataset?



In this lesson, we will discuss some of the important points to note while using transfer learning.

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?

Conslusion

Rule of Thumb in Transfer Learning

Points to consider