50 Hrs
3291+ Enrollments
AI & Deep Learning with TensorFlow Training
The AI & Deep Learning with TensorFlow Training course is designed to teach you the fundamentals of artificial intelligence, deep learning, and TensorFlow. The course will cover topics such as machine learning, neural networks, convolutional neural networks, recurrent neural networks, and more. You'll also learn how to use TensorFlow, a popular open-source software library for dataflow and differentiable programming across a range of tasks, to build intelligent systems.
AI & Deep Learning with TensorFlow Training
Overview of Course
Our AI & Deep Learning with TensorFlow Training is a comprehensive course designed to teach you the fundamentals of artificial intelligence, deep learning, and TensorFlow. With this course, you'll learn how to develop intelligent systems that can solve complex problems and make predictions based on data. This course is perfect for anyone interested in pursuing a career in AI.
Course Highlights
Learn from industry experts with years of experience in AI and Deep Learning
Lifetime access to course materials, including video lectures, assignments, and quizzes
Course completion certificate
Key Differentiators
Personalized Learning with Custom Curriculum
Training curriculum to meet the unique needs of each individual
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Trusted by over 100+ Fortune 500 Companies
We help organizations deliver right outcomes by training talent
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Flexible Schedule & Delivery
Choose between virtual/offline with Weekend options
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World Class Learning Infrastructure
Our learning platform provides leading virtual training labs & instances
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Enterprise Grade Data Protection
Security & privacy are an integral part of our training ethos
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Real-world Projects
We work with experts to curate real business scenarios as training projects
Skills You’ll Learn
Understanding of artificial intelligence and its applications
Knowledge of machine learning algorithms and techniques
Ability to design and build neural networks
Expertise in using TensorFlow for deep learning tasks
Understanding of natural language processing and computer vision
Knowledge of real-world applications of AI and deep learning
Training Options
1-on-1 Training
Access to live online classes- Flexible schedule including weekends
- Hands-on exercises with virtual labs
- Session recordings and learning courseware included
- 24X7 learner support and assistance
- Book a free demo before you commit!
Corporate Training
- Everything in 1-on-1 Training plus
- Custom Curriculum
- Extended access to virtual labs
- Detailed reporting of every candidate
- Projects and assessments
- Consulting Support
- Training aligned to business outcomes
Unlock Organizational Success through Effective Corporate Training: Enhance Employee Skills and Adaptability- Choose customized training to address specific business challenges and goals, which leads to better outcomes and success.
- Keep employees up-to-date with changing industry trends and advancements.
- Adapt to new technologies & processes and increase efficiency and profitability.
- Improve employee morale, job satisfaction, and retention rates.
- Reduce employee turnovers and associated costs, such as recruitment and onboarding expenses.
- Obtain long-term organizational growth and success.
Course Reviews
Curriculum
- What is deep learning?
- Brief history of deep learning
- Applications of deep learning
- Advantages and disadvantages of deep learning
- Neural networks vs deep learning
- Commonly used deep learning frameworks
- What is a neural network?
- Components of a neural network
- Types of neurons
- Activation functions
- Loss functions
- Backpropagation algorithm
- Gradient descent optimization
- Deep neural networks vs shallow neural networks
- Architecture of deep neural networks
- Initialization methods
- Regularization techniques
- Hyperparameter tuning
- Transfer learning
- Autoencoders
- Advanced activation functions
- Advanced optimization techniques
- Dropout regularization
- Batch normalization
- Gradient clipping
- Early stopping
- Model ensembling
- What are convolutional neural networks (CNNs)?
- Architecture of CNNs
- Convolutional layers
- Pooling layers
- Activation and loss functions for CNNs
- Transfer learning with CNNs
- Applications of CNNs
- What are recurrent neural networks (RNNs)?
- Architecture of RNNs
- Types of RNNs
- Backpropagation through time
- Long short-term memory (LSTM) cells
- Gated recurrent units (GRUs)
- Applications of RNNs
- What is a restricted Boltzmann machine (RBM)?
- Energy-based models
- Probability distributions
- Contrastive divergence algorithm
- Deep belief networks (DBNs)
- Applications of RBMs and DBNs
- RBM implementation in Python
- Introduction to Keras
- Keras vs other deep learning frameworks
- Installation and setup
- Model building with Keras
- Layers and activations in Keras
- Loss functions and optimizers in Keras
- Model evaluation and prediction in Keras
- Introduction to TFlearn
- Installation and setup
- Model building with TFlearn
- Layers and activations in TFlearn
- Loss functions and optimizers in TFlearn
- Model evaluation and prediction in TFlearn
- Comparison with other deep learning frameworks
Meet the instructor and learn about the course content and teaching style.- Make informed decisions about whether to enroll in the course or not.
- Get a perspective with a glimpse of what the learning process entails.
Description
Target Audience:
- Software Engineers
- Data Scientists
- Machine Learning Engineers
- Anyone interested in pursuing a career in AI or Deep Learning
Prerequisite:
- Basic knowledge of Python programming language
- Familiarity with linear algebra and calculus concepts
- Basic knowledge of machine learning concepts
Benefits of the course:
- Gain expertise in AI and Deep Learning concepts
- Get hands-on experience with TensorFlow
- Build a strong foundation for a career in AI or Data Science
- Learn from industry experts with years of experience in the field
- Flexible learning options to fit your schedule
- Showcase your skills with a course completion certificate
Exam details to pass the course:
- There is no specific exam to pass the AI & Deep Learning with TensorFlow Training course.
- However, to successfully complete the course and receive a course completion certificate, you will need to complete all the assignments and quizzes, as well as the final project.
- The final project will require you to apply the knowledge and skills you have learned throughout the course to build a deep learning model for a real-world problem.
Certification path:
- TensorFlow Developer Certificate
- Microsoft Certified: Azure AI Engineer Associate
- AWS Certified Machine Learning - Specialty
Career options:
- Machine Learning Engineer
- Data Scientist
- AI Engineer
- Deep Learning Engineer
- Research Scientist
Why should you take this course from Skillzcafe:
At Skillzcafe, we are committed to providing high-quality training that is designed to help you achieve your career goals.- Our AI & Deep Learning with TensorFlow Training course is taught by industry experts with years of experience in the field.
- With this course, you'll get hands-on training with TensorFlow, as well as real-world projects that will give you practical experience.
- Additionally, our flexible learning options make it easy for you to fit the course into your busy schedule.
FAQs
Yes, this course offers self-paced learning options. You can complete the course at your own pace and according to your own schedule.
Yes, this course is designed for both beginners and professionals who want to enhance their knowledge and skills in the field of AI and Deep Learning.
This course primarily uses Python programming language for AI and Deep Learning tasks.
Yes, upon successfully completing the course and all the assignments, quizzes, and the final project, you will receive a course completion certificate from Skillzcafe.
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