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Deep Learning

Build innovative AI solutions that fetch from raw data to provide improvised results. 

(DEEP-LEARNING.AE1) / ISBN : 978-1-64459-507-7
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About This Course

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).

Skills You’ll Get

  • 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
Download Course Outline

1

Introduction

  • About This Course
  • Icons Used in This Course
  • Where to Go from Here
2

Introducing Deep Learning

  • Defining What Deep Learning Means
  • Using Deep Learning in the Real World
  • Considering the Deep Learning Programming Environment
  • Overcoming Deep Learning Hype
3

Introducing the Machine Learning Principles

  • Defining Machine Learning
  • Considering the Many Different Roads to Learning
  • Pondering the True Uses of Machine Learning
4

Getting and Using Python

  • 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
5

Leveraging a Deep Learning Framework

  • Presenting Frameworks
  • Working with Low-End Frameworks
  • Understanding TensorFlow
6

Reviewing Matrix Math and Optimization

  • Revealing the Math You Really Need
  • Understanding Scalar, Vector, and Matrix Operations
  • Interpreting Learning as Optimization
7

Laying Linear Regression Foundations

  • Combining Variables
  • Mixing Variable Types
  • Switching to Probabilities
  • Guessing the Right Features
  • Learning One Example at a Time
8

Introducing Neural Networks

  • Discovering the Incredible Perceptron
  • Hitting Complexity with Neural Networks
  • Struggling with Overfitting
9

Building a Basic Neural Network

  • Understanding Neural Networks
  • Looking Under the Hood of Neural Networks
10

Moving to Deep Learning

  • 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

  • Beginning the CNN Tour with Character Recognition
  • Explaining How Convolutions Work
  • Detecting Edges and Shapes from Images
12

Introducing Recurrent Neural Networks

  • Introducing Recurrent Networks
  • Explaining Long Short-Term Memory
13

Performing Image Classification

  • Using Image Classification Challenges
  • Distinguishing Traffic Signs
14

Learning Advanced CNNs

  • Distinguishing Classification Tasks
  • Perceiving Objects in Their Surroundings
  • Overcoming Adversarial Attacks on Deep Learning Applications
15

Working on Language Processing

  • Processing Language
  • Memorizing Sequences that Matter
  • Using AI for Sentiment Analysis
16

Generating Music and Visual Art

  • Learning to Imitate Art and Life
  • Mimicking an Artist
17

Building Generative Adversarial Networks

  • Making Networks Compete
  • Considering a Growing Field
18

Playing with Deep Reinforcement Learning

  • Playing a Game with Neural Networks
  • Explaining Alpha-Go
19

Ten Applications that Require Deep Learning

  • 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

  • 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

  • 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

1

Getting and Using Python

  • Exploring Jupyter Notebook
  • Understanding Cells of Jupyter Notebook
  • Understanding Indentation and Adding Comments in a Notebook
2

Leveraging a Deep Learning Framework

3

Reviewing Matrix Math and Optimization

  • Working with Matrices
4

Laying Linear Regression Foundations

  • Analyzing Data Using Linear Regression
  • Using Polynomial Expansion to Model Complex Relations
  • Analyzing Data Using Logistic Regression
5

Introducing Neural Networks

6

Building a Basic Neural Network

  • Creating a Neural Network Model
7

Explaining Convolutional Neural Networks

  • Building a LeNet5 Network
8

Performing Image Classification

  • Creating an Image Classifier Using CNNs
9

Working on Language Processing

  • Processing Text Using NLP
  • Building a Sentiment Analysis Algorithm Using RNNs

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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.

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.

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.

You’ll be exploring the Python programming language here.

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.

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.

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