Deep Learning and NLP with Python- 2-in-1

Deep Learning and NLP with Python: 2-in-1, available at $19.99, has an average rating of 3.5, with 31 lectures, 2 quizzes, based on 3 reviews, and has 34 subscribers.

You will learn about Learn popular algorithms in NLP and deep learning Transform text tokens into numerical vectors by vectorizing Compute the gradients of all output tensors Create a machine learning architecture from scratch Apply convolutional neural networks for image analysis Discover the methods of image classification and harness object recognition using deep learning Get to know recurrent neural networks for the textual sentiment analysis model This course is ideal for individuals who are This Learning Path is for anyone interested to enter the field of data science and are new to machine learning. It is particularly useful for This Learning Path is for anyone interested to enter the field of data science and are new to machine learning.

Enroll now: Deep Learning and NLP with Python: 2-in-1

Summary

Title: Deep Learning and NLP with Python: 2-in-1

Price: $19.99

Average Rating: 3.5

Number of Lectures: 31

Number of Quizzes: 2

Number of Published Lectures: 31

Number of Published Quizzes: 2

Number of Curriculum Items: 33

Number of Published Curriculum Objects: 33

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Learn popular algorithms in NLP and deep learning

Transform text tokens into numerical vectors by vectorizing

Compute the gradients of all output tensors

Create a machine learning architecture from scratch

Apply convolutional neural networks for image analysis

Discover the methods of image classification and harness object recognition using deep learning

Get to know recurrent neural networks for the textual sentiment analysis model

Who Should Attend

This Learning Path is for anyone interested to enter the field of data science and are new to machine learning.

Target Audiences

This Learning Path is for anyone interested to enter the field of data science and are new to machine learning.

Deep learning is a popular subset of machine learning that allows you to build complex models that are faster and give more accurate predictions. Natural Language Processing (NLP) offers powerful ways to interpret and act on spoken and written language. It’s used to help deal with customer support enquiries, analyse how customers feel about a product, and provide intuitive user interfaces.

This comprehensive 2-in-1 course teaches you to write? applications using two popular data science concepts, deep learning and NLP. You’ll learn through practical demonstrations, clear explanations, and interesting real-world examples. It will give you a versatile range of deep learning and NLP skills, which you will put to work in your own applications.

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Getting Started with NLP and Deep Learning with Python, starts off with an introduction to Natural Language Processing (NLP) and recommendation systems which enables you to run multiple algorithms simultaneously. You will then learn the concepts of deep learning and TensorFlow. You will also learn how to create machine learning architecture.

The second course, Deep Learning with Python, takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understanding automatic differentiation. You will then learn convolutional, recurrent neural networks and build up the theory that focuses on supervised learning and integrate into your product offerings such as search, image recognition, and object processing. You will also learn to examine the performance of the sentiment analysis model. Finally, you be glanced through TensorFlow.

By the end of this training program, you’ll comfortably leverage the power of machine learning and deep learning algorithms? to build high performing day-to-day apps.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

Giuseppe Bonaccorso is a machine learning and big data consultant with more than 12 years of experience. He has pursued his masters in electronics engineering from the University of Catania, Italy, and further post graduation specialization from the University of Rome, Tor Vergata, Italy, and the University of Essex, UK. During his career, he has covered different IT roles in several business contexts, including public administration, military, utilities, healthcare, diagnostics, and advertising. He has developed and managed projects using many technologies, including Java, Python, Hadoop, Spark, Theano, and TensorFlow. His main interests are in artificial intelligence, machine learning, data science, and philosophy of mind.

Eder Santana is a PhD candidate in Electrical and Computer Engineering. His thesis topic is on deep and recurrent neural networks. After working for 3 years with Kernel Machines (SVMs, Information theoretic learning, and so on), he moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, he contributes to Keras; deep learning library for Python. Besides deep learning, he also likes data visualization and teaching machine learning, either on online forums or as a teacher assistant.

Course Curriculum

Chapter 1: Getting Started with NLP and Deep Learning with Python

Lecture 1: The Corse Overview

Lecture 2: NLTK and Built-In Corpora

Lecture 3: The Bag-Of-Words Strategy

Lecture 4: A Sample Text Classifier

Lecture 5: Latent Semantic Analysis

Lecture 6: Probabilistic Latent Semantic Analysis

Lecture 7: Latent Dirichlet Allocation

Lecture 8: Deep Learning at a Glance

Lecture 9: Introduction to TensorFlow

Lecture 10: A Quick Glimpse Inside Keras

Lecture 11: Machine Learning Architecture

Lecture 12: Scikit-learn Tools for Machine Learning Architectures

Chapter 2: Deep Learning with Python

Lecture 1: The Course Overview

Lecture 2: What Is Deep Learning?

Lecture 3: Open Source Libraries for Deep Learning

Lecture 4: Deep Learning Hello World! Classifying the MNIST Data

Lecture 5: Introduction to Backpropagation

Lecture 6: Understanding Deep Learning with Theano

Lecture 7: Optimizing a Simple Model in Pure Theano

Lecture 8: Keras Behind the Scenes

Lecture 9: Fully Connected or Dense Layers

Lecture 10: Convolutional and Pooling Layers

Lecture 11: Large Scale Datasets, ImageNet, and Very Deep Neural Networks

Lecture 12: Loading Pre-trained Models with Theano

Lecture 13: Reusing Pre-trained Models in New Applications

Lecture 14: Theano for Loops – the scan Module

Lecture 15: Recurrent Layers

Lecture 16: Recurrent Versus Convolutional Layers

Lecture 17: Recurrent Networks –Training a Sentiment Analysis Model for Text

Lecture 18: Bonus Challenge – Automatic Image Captioning

Lecture 19: Captioning TensorFlow – Googles Machine Learning Library

Instructors

Packt Publishing
Tech Knowledge in Motion

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2 stars: 1 votes

3 stars: 1 votes

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5 stars: 1 votes

Frequently Asked Questions

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