Short Link to repo - bit.ly/amity_mlcc
This repository is created to teach students how to build Machine Learning and Deep Learning projects using TensorFlow.
- IntoToTF-Part1: General introduction to tensors in TensorFlow and how can we perform the common mathematical operations with them. IntroToTF-Part1 common_maths_ops
- IntoToTF-Part2: See how we can perform the large matrix operations with tensors and know about some commonly used methods in matrix operations. IntroToTF-Part2 common_matrix_methods
- IntoToTF-Part3: Make your own first perceptron and apply different activation functions to it. IntroToTF-Part3 activation_funs
- IntroToTF-Part4: See some more commonly performed methods while training deep neural network. IntroToTF-Part4
- IntoToTF-Eager Execution: More pythonic way of working with tensorflow api. Eager_Execution
- IntroToTF-Autograd: How to perform automatic differentiation. Autograd
- IntoToTF-SimpleLinearRegression: Create a simple linear regression model with only one dependent and independent variable. simple-linear-regression
- IntroToTF-MultiVariateRegression: Predict the fuel efficiency on Auto-MPG dataset. regression_problem one_hot_encoding
- MNIST_Classification: Predict the hand written digits using Keras sequentional model and functional model. mnist mnist_functional_model
- Face Detection: Detect human faces in an image. face_detection_using_openvc face_detection_using_tensorflow
- More tutorials are comming soon. Star the repository to be updated with latest tutorials.
- Fork the repo on GitHub
- Clone the project to your own machine
To run this notebook you need to install necessary packages, listed down. If you have not done so, you will need to install them first, as these are not in the Anaconda distribution as of now. From a command prompt on your computer type the following command. If no error occurs, you will have installed them.
'pip install -r requirements.txt'
A bit of experience with Python, Pandas and Jupyter Notebook is sufficient. If you are a beginner in python then you can follow along with:
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These tutorials are prepared by Praneet Nigam. He was a part of Google Machine Learning Facilitator Program (launched last year in India) for the Google Machine Learning Crash Course. You can get in touch with the speaker/author, by following him on social media handles list down below:
Email Ids: [email protected], [email protected]
Feel free to submit issues and enhancement requests.
Star the repository in order to spread it in the community of machine learning and deep learning.
