Deep Learning & Natural Language Processing Course by Lasso Pacific 

From math basics of deep learning to advance research in NLP. Developing machine translation projects to deploying real-world chatbots. This course will go from scratch to the end of State of the Art approaches in modern Deep Learning and Natural Language Processing. Every mathematical and programming concept will be cleared in an easy way. Even non-tech background people can join and become Natural Language Processing engineers after enrolling in this course. If you were not able to get an interview call we will get your money back into your account safely. This is the promise Lasso Pacific makes to its customers. Happy Learning

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2,999

About Course

WHAT YOU WILL LEARN

Intro CNN
Activation Functions
Early Stopping
RNN,LSTM, GRU
Text Preprocessing
Word2Vec
Tensorboard Callbacks
Model Checkpointing
Loss Functions
Gradient Descent
Word Embedding
Bi-directional LSTM
Artificial Neural Network
Forward Propagation
Backward Propagation
Encoder Decoder
Attention Mechanism
Transformers

Natural language processing is the process by which computers can understand, analyze and generate human languages. In this course, you'll learn about how deep learning can be used to build a chatbot and how it's implemented in machine translation projects. You'll also learn about NLP models like word vectorization and neural machine translation. This course is designed to help students who want to enter the field of natural language processing and deep learning or enhance their skills in these areas. It will cover the basics and advanced deep learning and natural language processing and interview preparation including convolutional neural networks, recurrent neural networks, and modern techniques such as attention models and Transformers. The course will have multiple projects on implementing and training a classifier for sentiment analysis, question answering, chatbots, machine translation, etc. From basics to advanced techniques of deep learning and natural language processing. You will start by learning about neural networks, training them, transfer learning, and more. Towards the end of this course, you will develop chatbots and deploy them. Finally, you will develop an NLP app that can understand users' messages in different languages and reply back correctly! The course is divided into three major sections. First, we will go through the basics of deep learning and natural language processing. Second, we will teach you advanced research in NLP.  Third, we will teach you to develop machine translation projects. In this course, you'll learn how to: - Design a deep learning architecture for text data - Learn about Natural Language Processing (NLP) - Implement an attention model that has been used widely in many publications - Use attention models to train your own machine translation system from scratch! :)

Transfer Learning
BERT & GPT-3
Google LaMDa Overview

Syllabus

      Module     
            1           

Introduction to field

- AI/ML/DL/DS

- Explaining machine learning algorithms for deep learning understanding

- Introduction & Evolution of Deep Learning

- Biological neurons & it's working

- The Invention of the First Neural Network (Research)

      Module     
            2           

Perceptron

- Introduction to Perceptron

- Visualizing more about Perceptron Research

- Implementing Perceptron with Python in Google Colab

- Session about Python Programming in Deep Learning

      Module     
            3           

Anatomy of Artificial Neural Network

- Multi-layer Perceptron

- Forward Propagation in Artificial Neural Network

- Activation Functions and Use

- Implementing ANN using Tensorflow Keras

- Discussing Callbacks | Tensorboard | Early Stopping| Model Checkpointing

      Module     
            4           

More in Mathematics and Deep Learning Optimization

- Vector, Differentiation, and Partial Differentiation

- Global, Local Minima, and Maxima theory

- Gradient Descent and mathematics in brief

- More about Gradient Descent and the maths behind it.

      Module     
            5           

Backward Propagation in ANN & Mathematics

- Chain Rule Explained

- Everything about Backward Propagation

      Module     
            6           

Moving Forward in ANN & Problems we face

- Why Exploding and Vanishing gradient problems.

- Back to Biological Neuron and Activation functions.

- Transfer Learning Concept briefly.

- Batch Normalization

      Module     
            7           

Everything about Optimizers

- Brief Introduction to Optimizers

- Stochastic Gradient Descent    

- Stochastic Gradient Descent with momentum

- Mini-batch Gradient Descent

- NAG

- Elongated Bown problem | AdaGrad Optimizer

- Adam Optimizer

      Module     
            8           

Everything about Loss Functions

- MSE (Mean Squared Error)

- MAE (Mean Absolute Error)

- Huber Loss

- Binary and Categorical Cross Entropy

Overfitting Problem

- Regularization 

- Dropout

      Module     
            9           

Convolutional Neural Network

- Introduction to CNN

- Kernels, Channels, -Feature Map, Stride, Padding

- Receptive Fields and Dropout

      Module     
            10           

NLP Start

      Module     
            11           

Natural Language Processing

- Introduction to NLP

- Machine Learning vs Deep Learning for NLP

Text Preprocessing part 1

- Tokenization and Lemmatization

- Stop-words and Stemming

      Module     
            12          

      Module     
            13          

Text Preprocessing part 2

- Bow, TF-IDF

- Unigrams, Bigrams, Ngrams

      Module     
            14          

Text Preprocessing part 3

- Word2Vec

- Average Word2Vec

State of the Art NLP

- RNN (Recurrent Neural Network with research paper

- Backward Propagation in RNN

- LSTM (Long Short Term Memory) with research paper

- GRU RNN

      Module     
            15          

Text Preprocessing part 4

- Word Embedding & Word2Vec

      Module     
            16          

State of the Art NLP Part 2

- Bi-directional LSTM​

- Encoder-Decoder

- Attention Mechanism

      Module     
            17          

State of the Art NLP 

- Transformers In-depth

      Module     
            18          

      Module     
            19          

Transfer Learning in NLP

- GPT-3

- BERT

      Module     
            20          

Overview of Google LaMDa and PaLM

- Modern Research in NLP

- Research Topics 

- NLP in Use 

- Conversation AI

Interview Preparation

- Most Frequently Asked Interview Questions

- Sharing Docs with Everyone

- Giving Advise to Everyone After Completion of the Course

      Module     
            21          

      Module     
            22          

NLP Projects

Frequently Asked Questions

What is the refund policy?

After you enroll in this course you can explore the products at Lasso Pacific. in case you are not satisfied with our hospitality you can ask for a refund in the first 15 days. After that, we don’t give refunds.

Is financial aid available?

Yes, Lasso Pacific provides financial aid to learners who cannot afford the fee. To get successful Financial Aid you need full the conditions written here. You'll be prompted to complete an application and will be notified if you are approved. 

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings, and projects anytime and anywhere via the web or your mobile device

Do I need GPU for practicing code?

No, you don't need to buy any expensive hardware for practicing code. We will write every line of code on Google Colab.

What are pre-requisites?

- Understanding of basic Python programming language.
If you have issues with these no worries, we will try to help you out when you reach us.

If I am from non-computer science background. Will I make it?

No problem we will go from base to advance. We will teach that way any middle school kid can understand. 

I never studied or don't know mathematics.

Everything will be taught in easy ways from the basics. If you are worried about the mathematics behind deep learning don't worry we will explain every single concept in easy ways.