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What you would learn in Modern Natural Language Processing(NLP) using Deep Learning course?
This course will examine the fundamental Deep Learning concepts and apply our understanding to solve real-world issues within Natural Language Processing using the Python Programming Language and TensorFlow 2. We will discuss the most fundamental Machine Learning topics like Linear Regression, Logistic Regression, Multi-class classification, and Neural Networks. If you've reached this point, that means you're interested in learning Deep Learning For NLP and using your skills to tackle real-world issues.
Perhaps you already have experience with Machine learning, Natural Language Processing, or Deep Learning, or you might be getting acquainted with the concept of Deep Learning for the first time. It doesn't matter which side you're from since at the end of the course you'll be an expert, with a lot of practical knowledge.
Using the skills you've learned through this course, you will be working on various projects such as Sentiment Analysis, Machine Translation, Question Answering, Image captioning, Speech Recognition, and many other things.
If you're ready to leap to your current career goals, this program is for you, and we're thrilled to assist you in achieving your objectives!
This course is made available via Neuralearn. Like every other course offered by Neuralearn, We place great importance on feedback. Your comments and questions on the forum will assist us in improving this course. Please feel free to post any questions you want to ask in the forum. We try our best to answer within the shortest time possible.
These are the key concepts you'll be able to master after completing this course.
Fundamentals Machine Learning.
Essential Python Programming
The selection of a machine model is Based on the task
Training and optimization
Validation and Testing
Designing Machine Learning models by hand in Python.
Overfitting and underfitting
Learning rate decay
Training neural networks by using TensorFlow 2.
Imagenet training using TensorFlow
Convolutional Neural Networks
Transfer Learning and FineTuning
Monitoring using Tensorboard
Recurrent Neural Networks.
Bi directional RNN
Implementing a Deep Learning Model with Google Cloud Functions
Content of the Course
An introduction to Python and more advanced concepts such as Object-Oriented Programming, decorators generators, and specific libraries such as Numpy and Matplotlib
Understanding the fundamental concepts in Machine Learning and The Machine Learning Development Lifecycle.
Linear Regression, Logistic Regression, and Neural Networks built from scratch.
TensorFlow installation, basics, and learning neural networks using TensorFlow 2.
Convolutional Neural Networks, Modern ConvNets for training object recognition models using TensorFlow 2.
Recurrent Neural Networks, Modern RNNs, and training sentiment analysis models that use TensorFlow 2.