Python for Deep Learning: Build Neural Networks in Python

DFE WP

What you’ll be taught

Be taught the basics of the Deep Studying concept

Learn to use Deep Studying in Python

Learn to use totally different frameworks in Python to unravel real-world issues utilizing deep studying and synthetic intelligence

Make predictions utilizing linear regression, polynomial regression, and multivariate regression

Construct synthetic neural networks with Tensorflow and Keras

Description

Python is famed as probably the greatest programming languages for its flexibility. It really works in nearly all fields, from net growth to creating monetary functions. Nonetheless, it’s no secret that Python’s greatest software is in deep studying and synthetic intelligence duties.

Whereas Python makes deep studying straightforward, it should nonetheless be fairly irritating for somebody with no data of how machine studying works within the first place.

If you understand the fundamentals of Python and you’ve got a drive for deep studying, this course is designed for you. This course will make it easier to learn to create applications that take information enter and automate function extraction, simplifying real-world duties for people.

There are a whole lot of machine studying sources accessible on the web. Nonetheless, you’re susceptible to studying pointless classes in case you don’t filter what you be taught. Whereas creating this course, we’ve helped with filtering to isolate the important fundamentals you’ll want in your deep studying journey.

It’s a fundamentals course that’s nice for each inexperienced persons and consultants alike. If you happen to’re looking out for a course that begins from the fundamentals and works as much as the superior matters, that is the very best course for you.

It solely teaches what it’s good to get began in deep studying with no fluff. Whereas this helps to maintain the course fairly concise, it’s about the whole lot it’s good to get began with the subject.

English
language

Content material

Introduction to Deep Studying

What’s a Deep Studying ?
Why is Deep Studying Necessary?
Software program and Frameworks

Synthetic Neural Networks (ANN)

Introduction
Anatomy and performance of neurons
An introduction to the neural community
Structure of a neural community

Propagation of knowledge in ANNs

Feed-forward and Again Propagation Networks
Backpropagation In Neural Networks
Minimizing the fee perform utilizing backpropagation

Neural Community Architectures

Single layer perceptron (SLP) mannequin
Radial Foundation Community (RBN)
Multi-layer perceptron (MLP) Neural Community
Recurrent neural community (RNN)
Lengthy Quick-Time period Reminiscence (LSTM) networks
Hopfield neural community
Boltzmann Machine Neural Community

Activation Features

What’s the Activation Operate?
Necessary Terminologies
The sigmoid perform
Hyperbolic tangent perform
Softmax perform
Rectified Linear Unit (ReLU) perform
Leaky Rectified Linear Unit perform

Gradient Descent Algorithm

What’s Gradient Respectable?
What’s Stochastic Gradient Respectable?
Gradient Respectable vs Stochastic Gradient Respectable

Abstract Overview of Neural Networks

How synthetic neural networks work?
Benefits of Neural Networks
Disadvantages of Neural Networks
Purposes of Neural Networks

Implementation of ANN in Python

Introduction
Exploring the dataset
Downside Assertion
Information Pre-processing
Loading the dataset
Splitting the dataset into impartial and dependent variables
Label encoding utilizing scikit-learn
One-hot encoding utilizing scikit-learn
Coaching and Take a look at Units: Splitting Information
Function scaling
Constructing the Synthetic Neural Community
Including the enter layer and the primary hidden layer
Including the following hidden layer
Including the output layer
Compiling the bogus neural community
Becoming the ANN mannequin to the coaching set
Predicting the take a look at set outcomes

Convolutional Neural Networks (CNN)

Introduction
Parts of convolutional neural networks
Convolution Layer
Pooling Layer
Absolutely linked Layer

Implementation of CNN in Python

Dataset
Importing libraries
Constructing the CNN mannequin
Accuracy of the mannequin
 

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