Each project comes with 2-5 hours of micro-videos explaining the solution.

Get access to 50+ solved projects with iPython notebooks and datasets.

Add project experience to your Linkedin/Github profiles.

Understanding the problem statement

Directly downloading and using MNIST dataset

Understanding One-hot encoded vectors

Converting encoded vector to images using helper function

What is Tensorflow and how does it works

What are Placeholder variables

Understanding a deep learning model and terms associated with it

Softmax activation function

"Gradient Descent Optimizer " and "Cross_entropy" loss function

Defining and Initiating a Tensorflow session

Plotting graphs after each epoch

Plotting graphs for weights after each optimization iteration

Making function for plotting graphs for each Convolution layer

Visualization of Weights and Optimization iteration using Confusion Matrix

Using Seaborn's heatmap function for visualizing Confusion Matrix

Learn computer vision fundamentals with the famous MNIST dataset.

Start here if...

you’re new to computer vision. This tensorflow project is the perfect introduction to techniques like neural networks using a classic dataset including pre-extracted features.

MNIST ("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.

The goal of this machine learning project using tensorflow is to correctly identify digits from a dataset of tens of thousands of handwritten images. We encourage you to experiment with different algorithms to learn first-hand what works well and how techniques compare.

Data Introduction:

Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive.

The training data set, (train.csv), has 785 columns. The first column, called "label", is the digit that was drawn by the user. The rest of the columns contain the pixel-values of the associated image.

Each pixel column in the training set has a name like *pixelx*, where *x* is an integer between 0 and 783, inclusive. To locate this pixel on the image, suppose that we have decomposed *x* as *x = i * 28 + j*, where *i* and *j* are integers between 0 and 27, inclusive. Then *pixelx* is located on row *i* and column *j* of a 28 x 28 matrix, (indexing by zero).

Practice Skills:

Computer vision fundamentals including simple neural networks.

Acknowledgements:

More details about the MNIST dataset, including algorithms that have been tried on it and their levels of success, can be found at http://yann.lecun.com/exdb/mnist/index.html. The dataset is made available under a Creative Commons Attribution-Share Alike 3.0 license.

In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset using Keras in Python.

In this tensorlfow project, our goal is to correctly identify digits from a dataset of tens of thousands of handwritten images.

In this data science project, we are going to work on video recognization data and a robust level of image recognization MNIST data.

4-June-2017

03h 53m