DEEP-LEARNING.AE1
Deep Learning
Build innovative AI solutions that fetch from raw data to provide improvised results.
- Practice in 12 Hands-On Labs — nothing to install
- 21 Interactive Lessons and 88 topics mapped to the official exam objectives
Intermediate Self-paced · 1 year access
12 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
21Interactive Lessons
88Topics
12LiveLab
76Flashcards
76Glossary of terms
01 / Skills you'll get
What you will be able to do
You’ll be learning the machine learning fundamentals of multi-layered neural networks, convolutional neural networks, and more. Get hands-on training with real-world applications on deep learning frameworks like TensorFlow and Keras. This Deep Learning course is based on Python, the most preferred programming language amongst data scientists. Take the first step towards making a career in the highly competitive field of Artificial Intelligence (AI).
- Expertise in Python programming
- Using libraries like NumPy and Pandas for data manipulation and analysis
- Understanding of the core machine learning concepts like supervised, unsupervised, and more
- Proficient with deep learning frameworks TensorFlow and PyTorch
- Ability to create Neural network architectures including CNNs, RNNs, and GANs
- Ability to train and optimize deep learning models like gradient descent, optimization algorithms and more
- Evaluate the performance of the model and interpret the results
- Proficient in mathematical areas like linear algebra, calculus, and probability theory for comprehending deep learning concepts
- Problem-solving and critical thinking approach to challenges
Course Highlights
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21 Structured Lessons Comprehensive coverage of core course objectives
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12 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
21 Interactive Lessons · 88 topics01 Introduction 3 topics +
- About This Course
- Icons Used in This Course
- Where to Go from Here
02 Introducing Deep Learning 4 topics +
- Defining What Deep Learning Means
- Using Deep Learning in the Real World
- Considering the Deep Learning Programming Environment
- Overcoming Deep Learning Hype
03 Introducing the Machine Learning Principles 3 topics +
- Defining Machine Learning
- Considering the Many Different Roads to Learning
- Pondering the True Uses of Machine Learning
04 Getting and Using Python 8 topics · 8 LiveLab +
- Working with Python in this Course
- Obtaining Your Copy of Anaconda
- Downloading the Datasets and Example Code
- Creating the Application
- Understanding the Use of Indentation
- Adding Comments
- Getting Help with the Python Language
- Working in the Cloud
8 LiveLab in this lesson — see the labs panel →
05 Leveraging a Deep Learning Framework 3 topics · 1 LiveLab +
- Presenting Frameworks
- Working with Low-End Frameworks
- Understanding TensorFlow
1 LiveLab in this lesson — see the labs panel →
06 Reviewing Matrix Math and Optimization 3 topics · 4 LiveLab +
- Revealing the Math You Really Need
- Understanding Scalar, Vector, and Matrix Operations
- Interpreting Learning as Optimization
4 LiveLab in this lesson — see the labs panel →
07 Laying Linear Regression Foundations 5 topics · 4 LiveLab +
- Combining Variables
- Mixing Variable Types
- Switching to Probabilities
- Guessing the Right Features
- Learning One Example at a Time
4 LiveLab in this lesson — see the labs panel →
08 Introducing Neural Networks 3 topics · 1 LiveLab +
- Discovering the Incredible Perceptron
- Hitting Complexity with Neural Networks
- Struggling with Overfitting
1 LiveLab in this lesson — see the labs panel →
09 Building a Basic Neural Network 2 topics · 2 LiveLab +
- Understanding Neural Networks
- Looking Under the Hood of Neural Networks
2 LiveLab in this lesson — see the labs panel →
10 Moving to Deep Learning 5 topics +
- Seeing Data Everywhere
- Discovering the Benefits of Additional Data
- Improving Processing Speed
- Explaining Deep Learning Differences from Other Forms of AI
- Finding Even Smarter Solutions
11 Explaining Convolutional Neural Networks 3 topics · 1 LiveLab +
- Beginning the CNN Tour with Character Recognition
- Explaining How Convolutions Work
- Detecting Edges and Shapes from Images
1 LiveLab in this lesson — see the labs panel →
12 Introducing Recurrent Neural Networks 2 topics +
- Introducing Recurrent Networks
- Explaining Long Short-Term Memory
13 Performing Image Classification 2 topics · 2 LiveLab +
- Using Image Classification Challenges
- Distinguishing Traffic Signs
2 LiveLab in this lesson — see the labs panel →
14 Learning Advanced CNNs 3 topics +
- Distinguishing Classification Tasks
- Perceiving Objects in Their Surroundings
- Overcoming Adversarial Attacks on Deep Learning Applications
15 Working on Language Processing 3 topics · 2 LiveLab +
- Processing Language
- Memorizing Sequences that Matter
- Using AI for Sentiment Analysis
2 LiveLab in this lesson — see the labs panel →
16 Generating Music and Visual Art 2 topics +
- Learning to Imitate Art and Life
- Mimicking an Artist
17 Building Generative Adversarial Networks 2 topics +
- Making Networks Compete
- Considering a Growing Field
18 Playing with Deep Reinforcement Learning 2 topics +
- Playing a Game with Neural Networks
- Explaining Alpha-Go
19 Ten Applications that Require Deep Learning 10 topics +
- Restoring Color to Black-and-White Videos and Pictures
- Approximating Person Poses in Real Time
- Performing Real-Time Behavior Analysis
- Translating Languages
- Estimating Solar Savings Potential
- Beating People at Computer Games
- Generating Voices
- Predicting Demographics
- Creating Art from Real-World Pictures
- Forecasting Natural Catastrophes
20 Ten Must-Have Deep Learning Tools 10 topics +
- Compiling Math Expressions Using Theano
- Augmenting TensorFlow Using Keras
- Dynamically Computing Graphs with Chainer
- Creating a MATLAB-Like Environment with Torch
- Performing Tasks Dynamically with PyTorch
- Accelerating Deep Learning Research Using CUDA
- Supporting Business Needs with Deeplearning4j
- Mining Data Using Neural Designer
- Training Algorithms Using Microsoft Cognitive Toolkit (CNTK)
- Exploiting Full GPU Capability Using MXNet
21 Ten Types of Occupations that Use Deep Learning 10 topics +
- Managing People
- Improving Medicine
- Developing New Devices
- Providing Customer Support
- Seeing Data in New Ways
- Performing Analysis Faster
- Creating a Better Work Environment
- Researching Obscure or Detailed Information
- Designing Buildings
- Enhancing Safety
Hands-On Labs Our edge
12 LiveLabs- Exploring Jupyter Notebook
- Understanding Cells of Jupyter Notebook
- Understanding Indentation and Adding Comments in a Notebook
- Working with Matrices
- Analyzing Data Using Linear Regression
- Using Polynomial Expansion to Model Complex Relations
- Analyzing Data Using Logistic Regression
- Creating a Neural Network Model
- Building a LeNet5 Network
- Creating an Image Classifier Using CNNs
- Processing Text Using NLP
- Building a Sentiment Analysis Algorithm Using RNNs
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
What is Deep Learning?+
Using artificial neural networks for Machine Learning is referred to as Deep Learning. This model uses multiple layers of neural networks (inspired by the human brain) to learn complex features and patterns from data. It’s used for building innovative AI solutions.
Who should take this course?+
All those curious about AI and Machine Learning and wanting to make a career in this in-demand field should do this course. Professionals and students in the field of Data Science will also benefit greatly from this course.
What are the prerequisites for this Deep Learning course?+
You should have a strong foundation in Python, numerical computing libraries, mathematics, and machine learning fundamentals. This course is designed for learners from various backgrounds so you can gradually build upon your basics with consistent practice and dedication.
Which programming language is covered here?+
You’ll be exploring the Python programming language here.
How does this course aid in career advancement?+
A Deep Learning certificate course places you as a high-demand employee across various industries. It gives you a competitive edge in today's AI-driven world, and opens various exciting job opportunities with high salaries.
Deep Learning vs Machine Learning. What is the difference?+
Both the learning models are used for Artificial Intelligence with menial differences. Machine learning covers a wider range of algorithms and techniques and runs on manual feature extraction. Whereas, deep learning runs on artificial neural networks and automatically learns from raw data.
Keep going
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- 1 year of full access
- 12 LiveLab included
- Certificate of completion