Deep Learning: Convolutional Neural Networks in Python

Name: Deep Learning: Convolutional Neural Networks in Python
Brand: Udemy
SKU: 43536
Price: 16.99 USD
Availability: InStock
Rating: 3.80 (5 reviews)

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Lazy Programmer Inc. 60 Machine Learning Udemy

★★★★★

(5 customer reviews)

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Instructor Details
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Reviews (5)

8.6/10 (Our Score)

Product is rated as #112 in category Machine Learning

*** NOW IN TENSORFLOW 2 and PYTHON 3 ***

Learn about one of the most powerful Deep Learning architectures yet!

The Convolutional Neural Network (CNN) has been used to obtain state–of–the–art results in computer vision tasks such as object detection, image segmentation, and generating photo–realistic images of people and things that don’t exist in the real world!

This course will teach you the fundamentals of convolution and why it’s useful for deep learning and even NLP (natural language processing).

You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself.

This course will teach you:

The basics of machine learning and neurons (just a review to get you warmed up!)

Neural networks for classification and regression (just a review to get you warmed up!)

How to model image data in code

How to model text data for NLP (including preprocessing steps for text)

How to build an CNN using Tensorflow 2

How to use batch normalization and dropout regularization in Tensorflow 2

How to do image classification in Tensorflow 2

How to do data preprocessing for your own custom image dataset

How to use Embeddings in Tensorflow 2 for NLP

How to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, parts–of–speech tagging, named entity recognition)

Instructor Details

Lazy Programmer Inc.Artificial intelligence and machine learning engineer

Votes: 1

Courses : 22

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Specification: Deep Learning: Convolutional Neural Networks in Python

Duration	11.5 hours
Year	2020
Level	All
Certificate	Yes
Quizzes	No

5 reviews for Deep Learning: Convolutional Neural Networks in Python

3.8 out of 5

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★★★★★
Eyad Alsaghir – January 14, 2020
need further organising for new jointers
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★★★★★
Hyunsik Shin – January 16, 2020
I provides practical information with a proper exercise which really helps you understand the material.
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★★★★★
Sandor Albert – February 6, 2020
Some of the selections are not supported by explanation. For instance, the equation for 2D gaussian, would’ve been useful if it was mentioned that it’s the equation of a circle. Also luminosity of the filter doesn’t follow a gaussian curve in 2D, if circle equation is used, but rather descends along the surface of the sphere.
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★★★★★
Daniel German Martinez Mu oz – July 9, 2020
Excelente course. I highly recommend this. I learn a lot and he is and amazing teacher. This is my third course with him and I alredy bought another.
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★★★★★
Seth Bata – July 27, 2020
Just getting started. So far, it’s okay.
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