Deep Learning Specialization
The objective of this course is to simplify the creation of deep learning and natural language processing algorithms using Python for students, data scientists, and machine learning engineers.
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Introduction to Artificial Neural Networks
- Understand the concept of Perceptron
- Multi-layer perceptron
- Significance of Aritificial Neurons
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Fully Connected Neural Networks
- Activation functions
- Optimizers
- Loss functions
- Backpropagation
- Gradient Descent
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Apply Artificial Neural Networks (ANNs) in practice
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Convolutional Neural Networks
- Kernels
- Strides
- Padding
- Pooling - Max Pooling operation
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Image Classification with CNN
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How to prevent overfitting in CNN and ANNs
- Applying Dropout layers
- BatchNormalization
- Early Stopping
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Data Augmentation
- Rotation
- Horizontal Flipping
- Vertical Flipping
- Translation
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Transfer Learning - Applying pre-trained computer vision models
- VGG16
- ResNet50
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Introduction to Natural Language Processing (NLP)
- Applications of NLP
- Sentiment Analysis
- Text Summarization
- Machine Translation
- Sentence Completion
- Chatbot
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Text Preprocessing
- Tokenization
- Lowercasing text
- HTML Tags Removal
- Stopwords Removal
- Stemming
- Lemmatization
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Text Analytics
- Word Cloud
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Text Vectorization
- Bag of Words (BOW) Model
- Term Frequency-Inverse Document Frequency (TF-IDF)
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Application of NLP
- Sentiment Analysis
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Deep Neural Networks for NLP
- Recurrent Neural Networks (RNNs)
- Long Short Term Memory (LSTM)
- Text Classification with Recurrent Networks
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Introduction to Word Embeddings
- Word2Vec
- GloVe
- Applying Word Embeddings to Text Classification
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Transformers
- Attention Mechanism
- Positional Encoding
- Introduction to Transformers
- Bi-Directional Encoder Representation from Transfofrmers (BERT)
- Variants of BERT - SciBERT, DistilBERT, RoBERTa
In this course, you will learn how to neural network models. You will learn how to apply fully connected networks to both regression and classification problems. You will also learn how to build convolutional neural networks from scratch for image classification problem. You will learn how to combat the problem of overfitting using Dropout and Early Stopping techniques.
In addition, you will learn how to perform data cleaning such as removal of stopwords, html tags, stemming, and lemmatization on text data. You will learn how to create word cloud visualization for your text data, and build advanced sequential models such as LSTM with pretrained word embeddings like GloVe and Word2Vec. All concepts taught will be implemented on real-world projects.
What you'll learn
- Understand the intuition behind Artificial Neural Network
- Understand the concept of Perceptron
- Multi-layer perceptron
- Fully Connected Networks
- Activation functions
- Optimizers
- Loss functions
- Backpropagation
- Gradient Descent
- Understand the intuition behind Convolutional Neural Networks (CNNs)
- Kernels, Strides, Padding, and Pooling
- Image Classification with CNN
- Understand how to prevent overfitting in CNN and ANNs
- Applying Dropout layers
- BatchNormalization
- Early Stopping
- Data Augmentation - Rotation, Horizontal and Vertical Flipping
- Transfer Learning - VGG16, ResNet50
- Introduction to Natural Language Processing (NLP)
- Applications of NLP to Real-World Problems
- Text Preprocessing - Tokenization, Lowercasing text, HTML Tags Removal, Stopwords Removal
- Stemming
- Lemmatization
- Text Vectorization - Bag of Words, TF-IDF
- Applying NLP in practice - Sentiment Analysis
- Deep Neural Networks for NLP
- Recurrent Neural Networks (RNNs)
- Long Short Term Memory (LSTM)
- Text Classification with Recurrent Networks
- Introduction to Word Embeddings
- Word2Vec
- GloVe
- Applying Word Embeddings to Text Classification
- Introduction to Transformers
- BERT
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Transcript from the "Introduction" Lesson
Course Overview [00:00:00]
My name is John Deo and I work as human duct tape at Gatsby, that means that I do a lot of different things. Everything from dev roll to writing content to writing code. And I used to work as an architect at IBM. I live in Portland, Oregon.
Introduction [00:00:16]
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
Why Take This Course? [00:00:37]
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
A Look at the Demo Application [00:00:54]
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
Summary [00:01:31]
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
Course - Frequently Asked Questions
How this course help me to design layout?
My name is Jason Woo and I work as human duct tape at Gatsby, that means that I do a lot of different things. Everything from dev roll to writing content to writing code. And I used to work as an architect at IBM. I live in Portland, Oregon.
What is important of this course?
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
Why Take This Course?
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
Is able to create application after this course?
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
What's included
- Certificate
- 16 Modules
- Live Classes
- Lifetime access
