The Advent of Deep Neural Networks. Neural Network implementation without ML libraries and extending them with Tensorflow.
Aniket Chowdhury (~aniket43) |
The focus is more on teaching core concepts to programmers rather than using libraries. More than one neural network will be implemented.
An Easy way to learn Machine Learning
An interactive way to learn ML. With ML being a leading platform in the market, the workshop introduces to one of the most important fields of Machine Learning that is Deep Neural Networks. Only basic introduction to Mathematics required.
- Python for Machine Learning
- What is Machine Learning?
- Why learn Machine Learning?
- Types of Machine Learning
- Regression and Classification
- Supervised and Unsupervised
- Deep Neural Networks
- Feed forward Neural Networks
- Convolutional Neural Networks CNN
- Recurrent Neural Networks
- Layers in Neural Networks
- Sigmoid Neuron
- Binary Threshold
- Stochastic Binary
Cost Functions (A Loss or Objective function)
- Gradient Descent
- Gradient Boosting
- Stochastic Gradient Descent
Implementing the classic MNIST dataset problem
- A Neural Network for handwritten digit recognition
- Classification using individual pixels
- A simple implementation using deeper networks
- Expanding the Neural Network using Google's Library for Machine Learning Might change to Caffe - nVIDIA's library for Machine Learning
- A brief introduction to Deep Learning practices
- Auto Encoders
- Other areas of Deep Learning (A qualitative study)
- Core Python - lists, dict, string including functions and classes
- NumPy, SciPy - not necessary but preferred
- Elementary Calculus - Differentiation and Integration (Understanding qualitatively is enough)
- Linear Algebra
- 32/64-bit Windows/Linux architecture with at least 2GB RAM
- Python3 compiler with NumPy, SciPy and TensorFlow library
- PDF reader
Other Requirements but not necessarily needed
- Anaconda3 (or support for ipynb files, Jupyter preferred)
- A graphic card
While I have been programming for more than a decade, my chosen language for the lesser half of the decade has been C++, with a wandering interest in Java, MySql, PHP and Ruby. The last few years were spent in cultivating the language we now all know as Python. The enamoured feasibility of the language over C++ and the ease of understanding over PERL. While being a bit slower due to being it's interpreted nature, better speed benchmarks are being discover by it's PyPy implementation.
My field of interest is Deep Neural Networks. Machine Learning may perhaps helps us to cure even cancer using gene sequencing.
Apart from that I am an avid reader. I read book from all genres and time. My hobbies include football, music, art, drama and of course, programming.