Keras: Deep Learning in Python
Build complex deep learning algorithms easily in Python
Bullstrack
Keras: Deep Learning in Python udemy course
Build complex deep learning algorithms easily in Python
What you'll learn:
- To describe what Deep Learning is in a simple yet accurate way
- To explain how deep learning can be used to build predictive models
- To distinguish which practical applications can benefit from deep learning
- To install and use Python and Keras to build deep learning models
- To apply deep learning to solve supervised and unsupervised learning problems involving images, text, sound, time series and tabular data.
- To build, train and use fully connected, convolutional and recurrent neural networks
- To look at the internals of a deep learning model without intimidation and with the ability to tweak its parameters
- To train and run models in the cloud using a GPU
- To estimate training costs for large models
- To re-use pre-trained models to shortcut training time and cost (transfer learning)
Requirements:
- Knowledge of Python, familiarity with control flow (if/else, for loops) and pythonic constructs (functions, classes, iterables, generators)
- Use of bash shell (or equivalent command prompt) and basic commands to copy and move files
- Basic knowledge of linear algebra (what is a vector, what is a matrix, how to calculate dot product)
- Use of ssh to connect to a cloud computer
Description:
Do you want to build complex deep learning models in Keras? Do you want to use neural networks for classifying images, predicting prices, and classifying samples in several categories?
Keras is the most powerful library for building neural networks models in Python. In this course we review the central techniques in Keras, with many real life examples. We focus on the practical computational implementations, and we avoid using any math.
The student is required to be familiar with Python, and machine learning; Some general knowledge on statistics and probability is recommended, but not strictly necessary.
Among the many examples presented here, we use neural networks to tag images belonging to the River Thames, or the street; to classify edible and poisonous mushrooms, to predict the sales of several video games for multiple regions, to identify bolts and nuts in images, etc.
We use most of our examples on Windows, but we show how to set up an AWS machine, and run our examples there. In terms of the course curriculum, we cover most of what Keras can actually do: such as the Sequential model, the model API, Convolutional neural nets, LSTM nets, etc. We also show how to actually bypass Keras, and build the models directly in Theano/Tensorflow syntax (although this is quite complex!)
After taking this course, you should feel comfortable building neural nets for time sequences, images classification, pure classification and/or regression. All the lectures here can be downloaded and come with the corresponding material.
Who this course is for:
- Students beginning with machine learning but who already are comfortable with Python
- Business analytics professionals aiming to expand their toolkit of analytical techniques
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Course Details:
- 10 hours on-demand video
- 44 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
Keras: Deep Learning in Python udemy free download
Build complex deep learning algorithms easily in Python
Demo Link: https://www.udemy.com/course/keras-deep-learning-in-python/
