Neural Network Basics
Build and train feedforward neural networks from scratch.
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After this skill you can…
- Implement a 2-layer net in NumPy and PyTorch
- Understand forward and backward pass
- Tune learning rate, batch size, and depth
Prerequisites
Watch (10 videos)
How to Use Tensorflow for Classification (LIVE)
→ Build a neural network with Tensorflow→ Predict housing prices with a neural network
How to Make a Neural Network (LIVE)
→ Build a neural network from scratch in Python→ Implement a neural network using live coding
How to Make a Tensorflow Neural Network (LIVE)
→ Build a convolutional neural network with Tensorflow→ Classify images using a neural network
Autoencoder in Tamil | Denoising Autoencoder Explained with Architecture & Live Project | Tamil
→ Build a denoising autoencoder→ Implement deep learning models
Understanding AI from Scratch – Neural Networks Course
→ Implement Neural Networks using Vanilla JavaScript→ Tweak network parameters for optimal performance→ Apply Neural Networks to real-world problems
Neural Network from scratch - Part 2 (Forward Propagation)
→ Implement forward propagation in a neural network→ Derive mathematical notation for neural networks
Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 1/2
→ Build regression models with TensorFlow→ Implement classification models with TensorFlow
L16.2 A Fully-Connected Autoencoder
→ Build a fully-connected autoencoder→ Implement deep learning models in Python
Training Convnet - Deep Learning and Neural Networks with Python and Pytorch p.6
→ Build a Convnet with Pytorch→ Train a neural network with Python
Build Your First Machine Learning AI With Neural Networks
→ Create a fully functional AI using neural networks→ Implement a neural network in Python
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