Table of Contents
Best TensorFlow Courses 2023
Best TensorFlow tutorials 2023
Complete Guide to TensorFlow for Deep Learning with Python
This course will walk you through using 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 an easy to understand way. Other courses and tutorials tend to stay away from the flow of pure tension 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 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
This is the best TensorFlow tutorial in 2023.
A Complete Guide on TensorFlow 2.0 using Keras API
You will learn:
How to use Tensorflow 2.0 in Data Science
Important differences between Tensorflow 1.x and Tensorflow 2.0
How to implement artificial neural networks in Tensorflow 2.0
How to implement convolutional neural networks in Tensorflow 2.0
How to implement recurrent neural networks in Tensorflow 2.0
How to Create Your Own Transfer Learning App in Tensorflow 2.0
How to Create a Stock Trading Bot Using Reinforcement Learning (Deep-Q Network)
How to create a machine learning algorithms in Tensorflow 2.0
How to perform data validation and preprocessing of datasets using TensorFlow data validation and TensorFlow transformation.
Production of a TensorFlow 2.0 model
How to create a Fashion API with Flask and TensorFlow 2.0
How to serve a TensorFlow model with the RESTful API
Tensorflow 2.0: Deep Learning and Artificial Intelligence
Tensorflow is Google’s library for deep learning and artificial intelligence. Deep Learning has recently been responsible for some amazing achievements, such as:
Generate beautiful photorealistic images of people and objects that never existed (GAN)
Defeat World Champions in Go Strategy Game and Complex Video Games like CS: GO and Dota 2 (Deep Reinforcement Learning)
Autonomous cars (computer vision)
Speech recognition (e.g. Siri) and machine translation (natural language processing)
Even create videos of people doing and saying things they’ve never done (DeepFakes – a potentially harmful deep learning app)
You will learn:
Artificial Neural Networks (ANN) / Deep Neural Networks (DNN)
Predict stock returns
Time series forecasts
Computer vision
How to Create a Deep Reinforcement Learning Stock Trading Bot
GANs (Generative Adversarial Networks)
Recommendation systems
Image recognition
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Use Tensorflow Serving to serve your model using a RESTful API
Low-level tensorflow, gradient ribbon and how to create your own custom models
Natural Language Processing (NLP) with Deep Learning
Demonstrate Moore’s Law Using Code
Transfer learning to create state-of-the-art image classifiers
Complete Tensorflow 2 and Keras Deep Learning Bootcamp
This course will guide you in using Google’s latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course covers a variety of topics:
Intensive NumPy course
Introductory course in Pandas data analysis
Introductory course in data visualization
Learning Neural Networks Basics
TensorFlow Basics
Basics of Keras syntax
Artificial neural networks
Densely connected networks
Convolutional neural networks
Recurrent neural networks
Automatic encoders
GAN – Generative Conflict Networks
Deployment of TensorFlow in production
Learn how to use TensorFlow 2.0 for Deep Learning
Leverage the Keras API to quickly build models that run on Tensorflow 2
Perform image classification with convolutional neural networks
Using Deep Learning for Medical Imaging
Predicting time series data with recurrent neural networks
Use generative conflicting networks (GANs) to generate images
Use deep learning for style transfer
Generate text with RNNs and natural language processing
Serve Tensorflow models via an API
Use GPUs for accelerated deep learning