Deep Learning with TensorFlow 2
- Offered by365DataScience
Deep Learning with TensorFlow 2 at 365DataScience Overview
Duration | 5 hours |
Mode of learning | Online |
Credential | Certificate |
Deep Learning with TensorFlow 2 at 365DataScience Highlights
- Earn a certificate of achievement from 365datascience
- Get 28 Practical Tasks
Deep Learning with TensorFlow 2 at 365DataScience Course details
- Grasp the mathematics behind deep learning algorithms
- Understand backpropagation, stochastic gradient descent, batching
- Build ML algorithms from scratch in Python
- Carry out pre-processing, standardization, normalization, and one-hot encoding
- Grasp overfitting and combat it with early stopping
- Hands on experience with TensorFlow 2
- Machine and deep learning are some of those quantitative analysis skills that differentiate the data scientist from the other members of the team
- Not to mention that the field of machine learning is the driving force of artificial intelligence
- This course will teach you how to leverage deep learning and neural networks for the purposes of data science
- The technology we employ is TensorFlow 2.0, which is the state-of-the-art deep learning framework
Deep Learning with TensorFlow 2 at 365DataScience Curriculum
Introduction
Why machine learning
Neural Networks Intro
Introduction to neural networks
Types of machine learning
The linear model. Multiple inputs
The linear model. Multiple inputs and multiple outputs
Graphical representation
The objective function
L2-norm loss
Cross-entropy loss
One-parameter gradient descent
N-parameter gradient descent
Setting Up The Environment
Setting up the environment - Do not skip, please!
Why Python and why Jupyter
Installing Anaconda
Jupyter Dashboard - Part 1
Jupyter Dashboard - Part 2
Installing the TensorFlow package
Minimal Example
Generating the data (optional)
Initializing the variables
Training the model
Introduction To TensorFlow 2
TensorFlow Outline
TensorFlow 2 Intro
A note on coding in TensorFlow
Types of file formats in Tensorflow and data handling
Model layout - inputs, outputs, targets, weights, bias, optimizer, and loss
Interpreting the result and extracting the weights and bias
Customizing your model
Deep Nets Overview
The layer
What is a deep net
Really understand deep nets
Why do we need non-linearities
Activation functions
Softmax activation
Backpropagation
Backpropagation - intuition
Backpropagation (Optional)
Backpropagation mathematics
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