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.
Python, Data Science training table of contents (duration : 1h45m)
Head First into Deep Learning
- The Course Overview free 00:03:52
- What Is Deep Learning? free 00:04:09
- Open Source Libraries for Deep Learning 00:04:31
- Deep Learning "Hello World!" Classifying the MNIST Data 00:07:57
Backpropagation and Theano for the Rescue
- Introduction to Backpropagation free 00:05:24
- Understanding Deep Learning with Theano 00:05:04
- Optimizing a Simple Model in Pure Theano 00:07:54
Keras – Making Theano Even Easier to Use
- Keras Behind the Scenes 00:05:24
- Fully Connected or Dense Layers 00:04:46
- Convolutional and Pooling Layers 00:06:40
Solving Cats Versus Dogs
- Large Scale Datasets, ImageNet, and Very Deep Neural Networks 00:05:17
- Loading Pre-trained Models with Theano 00:05:16
- Reusing Pre-trained Models in New Applications 00:07:22
"for" Loops and Recurrent Neural Networks in Theano
- Theano "for" Loops – the "scan" Module 00:05:18
- Recurrent Layers 00:06:28
- Recurrent Versus Convolutional Layers 00:03:43
- Recurrent Networks –Training a Sentiment Analysis Model for Text 00:06:50
Bonus Challenge and TensorFlow
- Bonus Challenge – Automatic Image Captioning 00:04:41
- Captioning TensorFlow – Google's Machine Learning Library 00:05:15
- Sources Filesdownload
With which software version is this tutorial compatible with?Python
What is the required level to follow this tutorial ?intermediate