Practical Neural Networks and Deep Learning in Python

Practical Neural Networks and Deep Learning in Python, available at $59.99, has an average rating of 4.2, with 85 lectures, based on 89 reviews, and has 844 subscribers.

You will learn about Harness The Power Of Anaconda/iPython For Practical Data Science (Including AI Applications) Learn How To Install & Use Important Deep Learning Packages Within Anaconda (Including Keras, H20, Tensorflow and PyTorch) Implement Statistical & Machine Learning Techniques With Tensorflow Implement Neural Network Modelling With Deep learning Packages Including Keras This course is ideal for individuals who are Students interested in using the Anaconda environment for Python data science applications or Students interested in getting started with the Keras, Tensorflow,PyTorch environment or Students Interested in Learning the Basic Theoretical Concepts behind Neural Networks techniques Such as Convolutional neural network or Implement ANN on Real Data or Implement Deep Neural Networks or Implement Convolutional Neural Networks (CNN) on Imagery data or Build Image Classifiers Using Real Imagery Data and Evaluate Their Performance It is particularly useful for Students interested in using the Anaconda environment for Python data science applications or Students interested in getting started with the Keras, Tensorflow,PyTorch environment or Students Interested in Learning the Basic Theoretical Concepts behind Neural Networks techniques Such as Convolutional neural network or Implement ANN on Real Data or Implement Deep Neural Networks or Implement Convolutional Neural Networks (CNN) on Imagery data or Build Image Classifiers Using Real Imagery Data and Evaluate Their Performance.

Enroll now: Practical Neural Networks and Deep Learning in Python

Summary

Title: Practical Neural Networks and Deep Learning in Python

Price: $59.99

Average Rating: 4.2

Number of Lectures: 85

Number of Published Lectures: 85

Number of Curriculum Items: 85

Number of Published Curriculum Objects: 85

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Harness The Power Of Anaconda/iPython For Practical Data Science (Including AI Applications)
Learn How To Install & Use Important Deep Learning Packages Within Anaconda (Including Keras, H20, Tensorflow and PyTorch)
Implement Statistical & Machine Learning Techniques With Tensorflow
Implement Neural Network Modelling With Deep learning Packages Including Keras

Who Should Attend

Students interested in using the Anaconda environment for Python data science applications
Students interested in getting started with the Keras, Tensorflow,PyTorch environment
Students Interested in Learning the Basic Theoretical Concepts behind Neural Networks techniques Such as Convolutional neural network
Implement ANN on Real Data
Implement Deep Neural Networks
Implement Convolutional Neural Networks (CNN) on Imagery data
Build Image Classifiers Using Real Imagery Data and Evaluate Their Performance

Target Audiences

Students interested in using the Anaconda environment for Python data science applications
Students interested in getting started with the Keras, Tensorflow,PyTorch environment
Students Interested in Learning the Basic Theoretical Concepts behind Neural Networks techniques Such as Convolutional neural network
Implement ANN on Real Data
Implement Deep Neural Networks
Implement Convolutional Neural Networks (CNN) on Imagery data
Build Image Classifiers Using Real Imagery Data and Evaluate Their Performance

THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH PYTORCH, H2O, KERAS & TENSORFLOW IN PYTHON!

It is a full 5-Hour+ Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using one of the most important Python Deep Learning frameworks- PyTorch, H2O, Keras & Tensorflow.

HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:

This course is your complete guide to practical machine & deep learning using the PyTorch, H2O, Keras and Tensorflow framework in Python.

This means, this course coversthe important aspects of these architectures and if you take this course, you can do away with taking other courses or buying books on the different Python-based- deep learning architectures.

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of frameworks such as PyTorch, Keras, H2o, Tensorflow is revolutionizing Deep Learning…

By gaining proficiency in PyTorch, H2O, Keras and Tensorflow, you can give your company a competitive edge and boost your career to the next level.

THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE!

But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals.

Over the course of my research, I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.

This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the PyTorch, H2O, Tensorflow and Keras framework.

Unlike other Python courses and books, you will actually learn to use PyTorch, H20, Tensorflow and Keras on real data! Most of the other resources I encountered showed how to use PyTorch on in-built datasets which have limited use.

DISCOVER 7 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF IMPORTANT DEEP LEARNING FRAMEWORKS:

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about PyTorch, H2o, Tensorflow and Keras installation and a brief introduction to the other Python data science packages
• A brief introduction to the working of important data science packages such as Pandas and Numpy
• The basics of the PyTorch, H2o, Tensorflow and Keras syntax
• The basics of working with imagery data in Python
• The theory behind neural network concepts such as artificial neural networks, deep neural networks and convolutional neural networks (CNN)
• You’ll even discover how to create artificial neural networks and deep learning structures with PyTorch, Keras and Tensorflow (on real data)

BUT, WAIT! THIS ISN’T JUST ANY OTHER DATA SCIENCE COURSE:

You’ll start by absorbing the most valuable PyTorch, Tensorflow and Keras basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.

My course will help youimplement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python-based data science in real -life.

After taking this course, you’ll easily use packages like Numpy, Pandas, and PIL to work with real data in Python along with gaining fluency in the most important of deep learning architectures. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !!

The underlying motivation for the course is to ensure you can apply Python-based data science on real data into practice today, start analyzing data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, the majority of the course will focus on implementing different techniques on real data and interpret the results. Some of the problems we will solve include identifying credit card fraud and classifying the images of different fruits.

After each video, you will learn a new concept or technique which you may apply to your own projects!

JOIN THE COURSE NOW!

Course Curriculum

Chapter 1: Introduction to the Course

Lecture 1: Introduction

Lecture 2: Data and Scripts

Lecture 3: Why Artificial Intelligence and Deep Learning?

Lecture 4: Get Started With the Python Data Science Environment: Anaconda

Lecture 5: Anaconda for Mac Users

Lecture 6: The iPython Environment

Chapter 2: Introduction to Common Python Data Science Packages

Lecture 1: Python Packages for Data Science

Lecture 2: NUMPY:Introduction to Numpy

Lecture 3: Create Numpy Arrays

Lecture 4: Numpy Operations

Lecture 5: Numpy for Basic Vector Arithmetric

Lecture 6: Numpy for Basic Matrix Arithmetic

Lecture 7: PANDAS: What are Pandas?

Lecture 8: Read in CSV data

Lecture 9: Read in Excel data

Lecture 10: Basic Data Exploration With Pandas

Chapter 3: Theoretical Foundations of Artificial Neural Networks (ANN) & Deep Learning (DL)

Lecture 1: Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)

Lecture 2: Perceptrons for Binary Classification

Lecture 3: ANN For Binary Classification

Lecture 4: What Are Activation Functions? Theory

Lecture 5: More on Backpropagation

Lecture 6: Multi-label classification with MLP

Lecture 7: Regression with MLP

Lecture 8: Other Accuracy Metrics

Chapter 4: Introduction to Artificial Intelligence Python Packages:PyTorch

Lecture 1: Start With H20

Lecture 2: Welcome to Tensorflow

Lecture 3: Install Tensorflow

Lecture 4: What are Tensors?

Lecture 5: Introduction to Computational Graphs

Lecture 6: Common Tensorflow Operations

Lecture 7: Welcome to Keras

Lecture 8: Keras Installation on Windows 10

Lecture 9: Keras Installation on Mac OS

Lecture 10: Written Instructions

Lecture 11: Why PyTorch?

Lecture 12: Install PyTorch

Lecture 13: PyTorch Basics: What Is a Tensor?

Lecture 14: Explore PyTorch Tensors and Numpy Arrays

Lecture 15: Some Basic PyTorch Tensor Operations

Chapter 5: Implementing ANN With Python

Lecture 1: Implement Multi Layer Perceptron (MLP) with Tensorflow

Lecture 2: Multi Layer Perceptron (MLP) With Keras

Lecture 3: Keras MLP For Binary Classification

Lecture 4: Keras MLP for Multiclass Classification

Lecture 5: Keras MLP for Regression

Lecture 6: Implement ANN With H2O

Lecture 7: PyTorch ANN Syntax

Lecture 8: Setting Up ANN Analysis With PyTorch

Lecture 9: How the Different Components of Neural Networks Come Together: PyTorch Example

Chapter 6: Implementing DNNs With Python

Lecture 1: Deep Neural Network (DNN) Classifier With Tensorflow

Lecture 2: Deep Neural Network (DNN) Classifier With Mixed Predictors

Lecture 3: Deep Neural Network (DNN) Regression With Tensorflow

Lecture 4: Wide & Deep Learning (Tensorflow)

Lecture 5: DNN Classifier With Keras

Lecture 6: DNN Classifier With Keras-Example 2

Lecture 7: DNN Classifier With H2O

Lecture 8: DNN Analysis with PyTorch

Lecture 9: More DNNs

Lecture 10: DNNs For Identifying Credit Card Fraud

Chapter 7: Unsupervised Learning with Deep Learning

Lecture 1: What is Unsupervised Learning?

Lecture 2: Autoencoders for Unsupervised Classification

Lecture 3: Autoencoders in Tensorflow (Binary Class Problem)

Lecture 4: Autoencoders in Tensorflow (Multiple Classes)

Lecture 5: Autoencoders in Keras (Sparsity Constraints)

Lecture 6: Autoencoders in Keras (Simple)

Lecture 7: Deep Autoencoder With Keras

Lecture 8: Denoise

Chapter 8: Working With Imagery Data and Computer Vision

Lecture 1: What Are Images?

Lecture 2: Read in Images in Python

Lecture 3: Some Basic Image Conversions

Lecture 4: Basic Image Resizing

Chapter 9: Convolution Neural Networks (CNN)

Lecture 1: What are CNNs?

Lecture 2: Implement a CNN for Multi-Class Supervised Classification

Lecture 3: What Are Activation Functions?

Lecture 4: More on CNN

Lecture 5: Pre-Requisite For Working With Imagery Data

Lecture 6: CNN on Image Data-Part 1

Lecture 7: CNN on Image Data-Part 2

Lecture 8: Implement CNN With TFLearn

Lecture 9: CNN Workflow for Keras

Lecture 10: CNN With Keras

Lecture 11: CNN on Image Data with Keras-Part 2

Chapter 10: Transfer Learning

Lecture 1: Theory Behind Transer Learning

Lecture 2: Implement an InceptionV3 model on Real Images

Chapter 11: Miscellaneous Lectures

Lecture 1: Github Intro

Lecture 2: Posit On POSIT

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 6 votes
2 stars: 1 votes
3 stars: 8 votes
4 stars: 9 votes
5 stars: 65 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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