Deep Learning with Python
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VIDEO TUTORIAL Deep Learning with Python

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Dive into the future of data science and implement intelligent systems using deep learning with Python.

About This Deep Learning with Python Video course

  • Gain an insight into the world of deep learning based AI programs
  • Implement automatic image recognition and text analysis models using deep learning
  • Get to know each concept along with its practical implementation

Deep Learning with Python In Detail

Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python. The aim of deep learning is to develop deep neural networks by increasing and improving the number of training layers for each network, so that a machine learns more about the data until it’s as accurate as possible. Developers can avail the techniques provided by deep learning to accomplish complex machine learning tasks, and train AI networks to develop deep levels of perceptual recognition.

Deep learning is the next step to machine learning with a more advanced implementation. Currently, it’s not established as an industry standard, but is heading in that direction and brings a strong promise of being a game changer when dealing with raw unstructured data. Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language processing. Developers can avail the benefits of building AI programs that, instead of using hand coded rules, learn from examples how to solve complicated tasks. With deep learning being used by many data scientists, deeper neural networks are evaluated for accurate results.

This video course takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understand automatic differentiation. Through the tutorial, we will cover thorough training in 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. Also, we will examine the performance of the sentimental analysis model and will conclude with the introduction of Tensorflow.

By the end of this course, you can start working with deep learning right away. This course will make you confident about its implementation in your current work as well as further research.

What will you learn in this course?

Course plan
Chapter 1
Head First into Deep Learning
Chapter 2
Backpropagation and Theano for the Rescue
Chapter 3
Keras – Making Theano Even Easier to Use
Chapter 4
Solving Cats Versus Dogs
Chapter 5
"for" Loops and Recurrent Neural Networks in Theano
Chapter 6
Bonus Challenge and TensorFlow

Detailed course plan

Chapter 1 : Head First into Deep Learning
Lesson 1The Course Overview
Lesson 2What Is Deep Learning?
Lesson 3Open Source Libraries for Deep Learning
Lesson 4Deep Learning "Hello World!" Classifying the MNIST Data
Chapter 2 : Backpropagation and Theano for the Rescue
Lesson 1Introduction to Backpropagation
Lesson 2Understanding Deep Learning with Theano
Lesson 3Optimizing a Simple Model in Pure Theano
Chapter 3 : Keras – Making Theano Even Easier to Use
Lesson 1Keras Behind the Scenes
Lesson 2Fully Connected or Dense Layers
Lesson 3Convolutional and Pooling Layers
Chapter 4 : Solving Cats Versus Dogs
Lesson 1Large Scale Datasets, ImageNet, and Very Deep Neural Networks
Lesson 2Loading Pre-trained Models with Theano
Lesson 3Reusing Pre-trained Models in New Applications
Chapter 5 : "for" Loops and Recurrent Neural Networks in Theano
Lesson 1Theano "for" Loops – the "scan" Module
Lesson 2Recurrent Layers
Lesson 3Recurrent Versus Convolutional Layers
Lesson 4Recurrent Networks –Training a Sentiment Analysis Model for Text
Chapter 6 : Bonus Challenge and TensorFlow
Lesson 1Bonus Challenge – Automatic Image Captioning
Lesson 2Captioning TensorFlow – Google's Machine Learning Library

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