30 days of Tensorflow and Keras with Projects Series
Vertical series ( One post that will house all the projects as we build/implement them)
Welcome back peeps. Happy to share that we have just finished —
Finished Series —
60 Days of Data Science and Machine Learning with projects Series
We are now starting a new series — 30 days of Tensorflow and Keras with Projects Series . This series would run in parallel with —
Ongoing Series —
Projects Videos —
All the projects, data structures, SQL, algorithms, system design, Data Science and ML , Data Analytics, Data Engineering, , Implemented Data Science and ML projects, Implemented Data Engineering Projects, Implemented Deep Learning Projects, Implemented Machine Learning Ops Projects, Implemented Time Series Analysis and Forecasting Projects, Implemented Applied Machine Learning Projects, Implemented Tensorflow and Keras Projects, Implemented PyTorch Projects, Implemented Scikit Learn Projects, Implemented Big Data Projects, Implemented Cloud Machine Learning Projects, Implemented Neural Networks Projects, Implemented OpenCV Projects,Complete ML Research Papers Summarized, Implemented Data Analytics projects, Implemented Data Visualization Projects, Implemented Data Mining Projects, Implemented Natural Leaning Processing Projects, MLOps and Deep Learning, Applied Machine Learning with Projects Series, PyTorch with Projects Series, Tensorflow and Keras with Projects Series, Scikit Learn Series with Projects, Time Series Analysis and Forecasting with Projects Series, ML System Design Case Studies Series videos will be published on our youtube channel ( just launched).
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What is Tensorflow?
Tensorflow is an open source platform for machine learning and deep learning developed by Google Brain Team and written in C++, Python, and CUDA created for large numerical computations and deep learning. It ingests the data in the form of tensors which are nothing but multi-dimensional arrays of higher dimensions to handle large amounts of data. It works on the data flow graphs that have nodes and edges and supports both CPUs and GPUs. It works by preprocessing the data, building the model, training and estimating the model.
What is Keras?
Keras is a very powerful open source Python library which is runs on top of top of other open source machine libraries like TensorFlow, Theano etc, used for developing and evaluating deep learning models and leverages various optimization techniques.
Features —
- Keras fully supports recurrent neural networks and convolution neural networks
- Keras runs smoothly on both CPU and GPU
- Keras NN are written in Python which advocates simplicity and great debugging power
- Keras is known for its incredibly expressive, flexible, minimal structure
- Keras is consistent, simple and extensible API
- Keras is also known for its highly computational scalability
- Extensive support for various platforms and backends
Goal
Note : Everyday new tensorflow and Keras topics and projects will be uploaded/posted here. This is a vertical post so check this post regularly for new topics/projects.
Let’s set a clear objective.
The goal is to develop an intuition and understand (in the depth) the practical side of Tensor flow and Keras and build projects.
I have created a GitHub repo for this series where we will be maintaining our code.
Tools
We will be using Google Colabs and Jupyter Notebooks.
Prerequisite to this series
Complete 60 days of Data Science and Machine Learning before starting this series ( link below) —
Let’s talk about topics and projects we are going to cover in this series
We will be covering —
TensorFlow Basics
What is TensorFlow?
How it Works?
How to Download & Install TensorFLow
Jupyter Notebook Tutorial
TensorFlow Basics
TensorBoard Tutorial
Python Pandas Tutorial
Import CSV Data
Linear Regression with TensorFlow
Binary Classification in TensorFlow
Gaussian Kernel
TensorFlow Perceptron
Single Layer Perceptron
Hidden Layer Perceptron
Multi-layer Perceptron
ANN in TensorFlow
What is Artificial Neural Network
Implementation of Neural Network
Classification of Neural Network
CNN in TensorFlow
CNN Introduction
Working of CNN
CNN project
RNN in TensorFlow
RNN Introduction
Working of RNN
RNN Time Series
LSTM RNN in Tensorflow
Training of RNN
Types of RNN
Autoencoders
TensorFlow Autoencoder
Style Transferring
Style Transferring in TensorFlow
Gram Matrix in Style Transferring
Style Transferring Working
TensorFlow Debugging
TensorFlow Debugging
Keras Basics
What is Keras?
Installation of Keras
Keras Backends
Keras Models
Keras layers
Keras Models and Layers
Keras Model class
Keras Sequential class
Keras Core Layers
Recurrent Layers
Embedding Layers
Keras Merge Layers
Convolutional Layer
Pooling Layers
Tensorflow and Keras Projects (40)
That’s it for now. We will keep updating this post covering above topics.
Let me know if you have questions in the comment section below. Subscribe/ Follow, Like/Clap as it would encourage me to write more in my free time
Stay Tuned and Keep coding!!
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