Deep Learning for Beginners Training Courses
Unlocking the Power of Neural Networks Step by Step
- Experienced instructors with industry expertise
- Access to official guides and materials
- Covers all exam topics thoroughly
- Flexible study at your own pace
- Support for exam service
- Anytime access to study resources
What you'll learn
This course gives you an introduction to deeplearning by covering all the concepts and techniques inthe domain.In this course, you will learn about the deeplearning fundamentals, TensorFlow and its installation,different Deep Learning frameworks, convolutional neuralnetworks,recurrent neural networks in Python, and DeepLearning applications.
Training Course Outline
Deep Learning for Beginners
1.1 Introduction To Deep Learning
2.1 Introduction to Deep Learning and Its Applications
3.1 What is a Netural Network
4.1 Why Are Neural Networks Hard To Train?
5.1 What are the Top Deep Learning Libraries?
6.1 What is TensorFlow?
7.1 What is a Data Flow Graph?
8.1 Use Case Implementation using TensorFlow
9.1 TensorFlow Object Detection
10.1 Deep Learning Frameworks
11.1 How image recognition works
12.1 Types of Recurrent Neural Network
13.1 Working of LSTMs
14.1 Deep Learning Applications
15.1 Deep Learning Frequently Asked Interview Questions
Training Options
Self Paced Learning
- Lifelong access to high-quality content
- Curated by industry experts
- Customized learning progress
- 24/7 learner assistance and support
- Follow the latest technology trends
Exam Dump
- 100% Real Exam Practice Tests
- 100% Verified Exam Questions & Answers
- 100% Guarantee Passing Rate
- Average 7 Days to Practice & Pass
Description
course introduces deep learning: fundamentals, TensorFlow, frameworks, CNNs, RNNs, Python applications.
Pre-requisites
The course has no specific prerequisites.
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Deep Learning and Its Applications FAQs
What are the applications of deep learning?
Deep learning finds use in a broad range of applications. For instance, it plays a critical role in image recognition, where it identifies objects and features—such as people, animals, or landmarks—in images. It is also extensively applied in natural language processing to interpret and analyze text, powering tools like customer service chatbots and spam filters.
What is TensorFlow and what is it used for?
TensorFlow is an open-source platform designed for machine learning that utilizes data flow graphs. In these graphs, nodes perform mathematical operations, while the edges carry multidimensional data arrays, known as tensors. This platform is widely adopted by data scientists, software developers, and educators to build and deploy machine learning models.
What is the best framework for deep learning?
There is a wide array of deep learning frameworks available, but TensorFlow and PyTorch are two of the most popular choices, especially for beginners. Their user-friendly design, along with comprehensive built-in tools and functions, allows users to start developing models quickly, making them suitable for both newcomers and experienced practitioners.
Is TensorFlow a deep learning framework?
Yes, TensorFlow is indeed a deep learning framework. It is open source and supports multiple programming languages including Python, C++, Java, and JavaScript. TensorFlow is used to create machine learning models for diverse applications such as image recognition, natural language processing, and automation. It was developed by Google and first released in 2015.
What is deep learning in CNN?
In the realm of Convolutional Neural Networks (CNNs), deep learning refers to the use of sophisticated algorithms and artificial neural networks to analyze large datasets. This approach trains computers to learn from experience, enabling them to classify and recognize data or images in a manner similar to human cognitive processes.