Deep Learning- Learn With Tensor Flow and Python

Welcome to the Complete Guide to TensorFlow for Deep Learning with Python!

This course will guide you through how to use Google’s TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google’s TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!

This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!

This course covers a variety of topics, including

• Neural Network Basics
• TensorFlow Basics
• Artificial Neural Networks
• Densely Connected Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• AutoEncoders
• Reinforcement Learning
• OpenAI Gym
• and much more!

  I hope you’re excited to learn about these advanced applications of CNNs, I’ll see you in class!

  AWESOME FACTS:

  • One of the major themes of this course is that we’re moving away from the CNN itself, to systems involving CNNs.
  • Instead of focusing on the detailed inner workings of CNNs (which we’ve already done), we’ll focus on high-level building blocks. The result? Almost zero math.
  • Another result? No complicated low-level code such as that written in Tensorflow, Theano, orPyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you.

    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

  What material you will get in this course?

• you will get study material .like notes from MIT and other reputed universities.
• you will get interveiw question Also