Course Details
Advanced Deep Learning for Computer Vision and NLP
Data Science
Created On
June 29, 2023Description
An intensive course on leveraging deep learning for computer vision and advanced NLP, enhancing visual intelligence and language understanding through practical application of cutting-edge techniques and models.
Overview
Our course, "Deep Learning for NLP and Computer Vision," immerses you in the world of transformative deep learning. Explore RNNs, CNNs, attention mechanisms, and cutting-edge models like BERT and YOLO. Gain hands-on experience, building chatbots, image classifiers, and more. Master versatile tools like TensorFlow, PyTorch, Spacy, and Keras. Learn deployment, optimization, and cloud integration. Elevate your expertise, making you sought-after in diverse fields. By course end, tackle complex NLP and CV projects with confidence. Join us and unlock the potential of deep learning in NLP and CV.
Features
- Comprehensive Curriculum
- Hands-On Learning
- Cutting-Edge Technologies including BERT, YOLO, TensorFlow, PyTorch, and more
- Cloud Integration
- Practical Projects
- Interactivity
- Project Portfolio
- Completion Certificate
- One to One Resume Discussion
- Doubt Clearing session
- Email Support
- Career Guidance
- All 7 Days in a week Skype Support
- Chatbot Development
- Deep Learning
- NLP & Computer Vision
- Internal Hiring
What you'll learn
- Deep Learning Foundations
- Computer Vision
- NLP Fundamentals
- Advanced NLP
- GPU Acceleration
- Chatbot Development
- Object Detection
- Sequence Modeling
- Attention Mechanisms in NLP and CV.
- Model Deployment
- Cloud Integration
- Word Embeddings
- Evaluation and Metrics
- Knowledge Graphs
- Semantic Segmentation
- Transfer Learning
- Multi-modal Applications
- Efficient Architectures
- Co-reference Resolution
- RNN
- Mini NLP Project.
- NLP Transfer learning project with deployment and integration with UI
- NLP end to end project with architecture and deployment.
- NLP project end to end with deployment in various cloud and UI integration.
- Computer Vision Project.
Prerequisites
Curriculum
- 21 modules
The Science Behind Natural Language Processing
Unveiling the Historical Evolution of NLP:
Exploring its Applications and Impact in Various Fields
Harnessing the Potential of NLP:
Setting Up the Environment for TensorFlow
TensorFlow Installation 1.6 with Virtual Environment
Exploring TensorFlow 2.0 Functionality
Creating Neural Networks with TensorFlow 2.0
Working with TensorFlow 1.6 Functions
Building Neural Networks and Utilizing Functions in TensorFlow 1.6
Introduction to Keras for Deep Learning
In-Depth Neural Network Creation with Keras
Implementing a Mini Project in TensorFlow
Setting up PyTorch Framework
Exploring Functionalities and Features
Building Deep Learning Models with PyTorch
Understanding the Basics of Neural Networks
Exploring the Fundamentals of Perceptrons in Neural Networks
Understanding the Framework and its Applications
An Overview of Different Network Structures
Neural Network Use Cases in NLP and Computer Vision
Multilayer Networks
Loss Functions
The Learning Mechanism: How Neural Networks Adapt and Update Weights
Optimizers: Enhancing Neural Network Training Efficiency and Performance
Forward and Backward Propagation
Gradient Descent: Gradient-Based Optimization Techniques
● Understanding CNN and Various Architectures ● Training CNNs from Input to Output ● Deployment in Azure/AWS/GCP ● Optimizing the Performance of CNN Models in Cloud Platforms
GAN
Generative Model Using GAN
BERT
Semi-Supervised learning using GAN
Restricted Boltzmann Machine (RBM) and Autocoders
CNN Architectures
LeNet-5
AlexNet
GoogleNet
VGGNet
ResNet
SSD
SSD lite
Faster R CNN
SCNN
Masked R-CNN
Xception
SENet
Facenet
Implementing a ResNet – 34 CNN using Keras
Pertained Models from Keras
Pertained Models for Transfer Learning
Understanding Natural Language, Understanding in Chatbots
Fulfillment and Integration
Building Chatbots with Microsoft Bot Builder and LUIS: Development for Telegram and Skype
Chatbot Development with Amazon Lex: Deployment to Telegram and Skype
Building Chatbots with RASA NLU: Deployment to Telegram and Skype
Semantic Segmentation: Accurate Pixel-level Image Understanding
Classification and Localization: Simultaneous Object Classification and Localization
TensorFlow Object Detection: Implementing Object Detection Models with TensorFlow
You Only Look Once (YOLO): Real-time Object Detection with Efficient Single-Shot Architecture
Text processing
Importing Text
Web Scrapping
Text Processing
Understanding Regex
Text Normalization
Word Count
Frequency Distribution
Text Annotation
Use of Annotator
String Tokenization
Annotator Creation
Sentence processing
Lemmatization in text processing
POS
Named Entity Recognition
Dependency Parsing in text
Sentimental Analysis
Exploring the Features and Capabilities of Spacy
Utilizing Key Functions in Spacy for Natural Language Processing
Applying Spacy Functions for Text Analysis and Manipulation
POS Tagging: Challenges and Accuracy in Part-of-Speech Tagging with Spacy
Interpolation and Language Models in Spacy
Morphology and Diversity: Exploring the Richness and Variation of Language Structure
Unraveling the Challenges and Patterns of Ambiguous Language
Structures and Meanings
Understanding the Interplay of Lexical Knowledge and Language Processing
Lexical Ambiguity
Polysemy and Homonymy
Conference Resolution
Anaphora and Cataphora Resolution
Resolving Ambiguities and Coherence across Multiple Sentences
Humans and Ambiguity
Machines and Ambiguity
Co-occurrence and Distributional Similarity
Measuring the Degree of Similarity and Relatedness between Words
Knowledge Graphs and Repositories
Computational Linguistics
Word Embedding and Co-occurrence Vectors
Case Studies and Analysis Using the Word Sim353 Dataset
Word2Vec
Part of Speech Tagging
Understanding RNN and their Applications
Long Short-Term Memory (LSTM)
Bi-LSTM: Bidirectional Long Short-Term Memory Networks for Enhanced Sequence Modeling
GRU Implementation
Generating Text with RNN at the Character Level
Understanding Sequence-to-Sequence Models in Deep Learning
Encoders and Decoders: Seq2Seq Models
Attention Mechanism
Attention Neural Networks
Self-Attention
Exploring the Basics of Graphics Processing Units
Understanding Different GPU Setup Options
Comparing Different GPU Providers and their Cost Structures
Configuring and Utilizing GPUs on the Paperspace Platform
Running Models on GPU
Introduction to transformers
BERT Model
ELMo Model
GPT1 Model.
GPT2 Model
ALBERT Model
DistilBERT Model
Topic Modeling
Word sense disambiguation
Text to speech
Keyword Spotting
Document Ranking
Text Search (with Synonyms)
Language Modeling
Spam Detector
Image Captioning
Machine Translation
Abstractive text summarization
Keyword spotting
Language modeling
Document summarization
Deep learning model deployment strategies
Deep learning project architecture
Model deployment phase
Model retraining phase
Model deployment in AWS
Model deployment in AZURE
Model deployment in Google Cloud
Deployment and integration with ui machine translation
Question answering (like chat – bot)
Sentiment analysis imdb
Text search (with synonyms)
Text classifications
Spelling corrector
Entity (person, place or brand) recognition
Text summarization
Text similarity (paraphrase)
Topic detection
Language identification
Document ranking
Fake news detection
Plagiarism checker
Text summarization extractive
Text summarization abstractive
Movie review using Bert
Ner using Bert
Pos bert
Text generation gpt 2
Text summarization xlnet
Abstract Bert
Machine Translation
NLP text summarization custom
Keras/tensorflow
Language identification
Text classification using fast Bert
Neural core
Detecting fake text using gltr with Bert and gpt2
Fake news detector using gpt2
Python plagiarism checker type a message
Question answering
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Instructors
Skoliko Faculty
₹4500.00
- 21 Modules
- 75 Hours
- Data Science
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