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- User Authentication
- JITENDER SINGH's Credentials
- JITENDER SINGH's Wallet
- Microsoft Security: Compliance Concepts & Methodologies
- Functions in Python: Introduction
- Functions in Python: Gaining a Deeper Understanding of Python Functions
- Functions in Python: Working with Advanced Features of Python Functions
- Conditional Statements & Loops: The Basics of for Loops in Python
- Conditional Statements & Loops: While Loops in Python
- Data Structures & Algorithms in Python: Implementing Data Structures
- Conditional Statements & Loops: If-else Control Structures in Python
- Socket Programming in Python: Introduction
- Data Structures & Algorithms in Python: Implementing Trees & Graphs
- Data Structures & Algorithms in Python: Fundamental Data Structures
- Complex Data Types in Python: Shallow & Deep Copies in Python
- Data Structures & Algorithms in Python: Sorting Algorithms
- Complex Data Types in Python: Working with Lists & Tuples in Python
- Complex Data Types in Python: Working with Dictionaries & Sets in Python
- Conditional Statements & Loops: Advanced Operations Using for Loops in Python
- Data Structures & Algorithms in Python: Implementing Sorting Algorithms
- Data Structures & Algorithms in Python: Trees & Graphs
- Python Requests: HTTP Requests with Python
- Socket Programming in Python: Advanced Topics
- Improving Neural Networks: Neural Network Performance Management
- Convo Nets for Visual Recognition: Computer Vision & CNN Architectures
- Security Programming: Python Scripting Essentials
- Introduction
- Building Neural Networks: Artificial Neural Networks Using Frameworks
- Training Neural Networks: Advanced Learning Algorithms
- Getting Started with Neural Networks: Biological & Artificial Neural Networks
- ConvNets: Introduction to Convolutional Neural Networks
- Training Neural Networks: Implementing the Learning Process
- Convolutional Neural Networks: Implementing & Training
- Improving Neural Networks: Data Scaling & Regularization
- Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms
- Building Neural Networks: Development Principles
- Improving Neural Networks: Loss Function & Optimization
- ConvNets: Working with Convolutional Neural Networks
- Fundamentals of Sequence Model: Language Model & Modeling Algorithms
- Build & Train RNNs: Neural Network Components
- Build & Train RNNs: Implementing Recurrent Neural Networks
- Convolutional Neural Networks: Fundamentals
- Convo Nets for Visual Recognition: Filters and Feature Mapping in CNN
- Getting Started with Python: Introduction
- Fragments & Customization
- Development Features, Installation, & Usage
- Components, Classes, Services, & Methods
- Manipulating Android Databases
- User Interfaces & Controls
- TensorFlow: Simple Regression & Classification Models
- TensorFlow: K-means Clustering
- Development Life Cycle Management & Debugging
- TensorFlow: Word Embeddings & Recurrent Neural Networks
- Components, Activities, & Layout
- New Features For Developers
- Designing & Creating Android Databases
- Updates for Developers
- TensorFlow: Sentiment Analysis with Recurrent Neural Networks
- iOS 12 Development: Testing & Error Handling
- TensorFlow: Deep Neural Networks & Image Classification Using Estimators
- TensorFlow: Convolutional Neural Networks for Image Classification
- TensorFlow: Building Autoencoders
- iOS 12 Development: Working With Layout & Controls in iOS
- iOS 12 Development: Getting Started with Xcode & iOS
- Python - Introduction to NumPy for Multi-dimensional Data
- SQL in FSD Development
- iOS 12 Development: Best Practices in iOS Security
- iOS 12 Development: Augmented Reality & HealthKit
- iOS 12 Development: Working With Data & Gestures
- FSD Programming Languages: FSD Back End & Miscellaneous Tools
- FSD Development with Python: API Development in Flask
- FSD Programming Languages: FSD Front-end Primer
- Principles & Essential Services
- Agile Principles and Methodologies
- Cloud Basics
- Applying AI to Robotics
- TensorFlow: Introduction to Machine Learning
- Project Management Introduction (PMBOK® Guide Sixth Edition)
- Machine & Deep Learning Algorithms: Introduction
- AI Framework Overview: Development Frameworks
- AI Framework Overview: AI Developer Role
- Machine & Deep Learning Algorithms: Regression & Clustering
- Machine & Deep Learning Algorithms: Data Preparation in Pandas ML
- The AI Practitioner: Optimizing AI Solutions
- Implementing AI Using Cognitive Modeling
- AI in Industry
- Final Exam: AI Developer
- Using Intelligent Information Systems in AI
- Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling
- AI Practitioner: BERT Best Practices & Design Considerations
- AI Practitioner: Practical BERT Examples
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