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How to Train Deep Learning Model on Google Colab for FREE | Train Neural Network on GPU Machine

Google Colaboratory for free GPU -(Neural Network Model Training) | Part 2

Differentiate between Deep Learning and Machine Learning | Tensorflow Tutorial Series

Tensorflow Tutorial Series Introduction | A Hands-on Learning Experience

What is Deep Learning | Tensorflow Tutorial Series

Artificial Neural Network Tutorial | Tensorflow Tutorial Series

Activation Functions in Neural Networks | Tensorflow Tutorial Series

How Neural Network gets Trained | Tensorflow firsthand Tutorial Series for Beginners

Google Colaboratory for Tensorflow | Tensorflow firsthand Tutorial Series for Beginners

Tensorflow Math Operations using Constants | Tensorflow Tutorial Series

How Data travels in Deep Neural Networks | Scalar vs Vector vs Matrix vs Tensor

What are Placeholders in Tensorflow | Usage of Placeholders in Tensorflow

Tensorflow Variables and Associated Computations | Optimize Model parameter during Training

What is Loss Function in Deep Learning | Loss Function in Machine Learning | Loss Function Types

Backpropagation Explained in a simple manner | Backpropagation in Neural Networks

Learning from the past events using Recurrent Neural Network | A Gentle introduction to RNN

Basic Building Blocks of Recurrent Neural Network | Recurrent Neural Network (RNN/LSTM)

Cases where Backpropagation fails in Neural Networks | Inherent problems with Recurrent Neural Net

Why Long Memory Neurons are Important in Recurrent Neural Network | Deep Learning

Understand LSTM cells to build Neural Network based Applications | LSTM Architecture

Convert Text into Numeric Encoding for Recurrent Neural Network | How RNN read Text Data

Convolution Neural Network (CNN) Introduction and Intuition | Convolution Neural Network Explained

How to Detect Features of an Image using CNN (Convolution Neural Network)?

Why Rectified Linear Unit (ReLU) is required in CNN? | ReLU Layer in CNN

Why do we use max POOLING Layer in CNN | What is Pooling Layer in CNN?

Why do we use Flattening Layer in CNN | What is Flattening Layer in CNN?

How to address Overfitting in Neural Network using Dropout Layer | What is Dropout Layer in CNN?

What is Fully Connected Layer | How does Fully Connected Layer works

How to Utilize Pre-Trained Models for building Deep Learning Models | VGG16 ResNET Object Detection

Increase ACCURACY of Model on Small Dataset | DATA AUGMENTATION for Small Image Dataset

Description:

Dive into a comprehensive tutorial series on deep learning neural networks, covering essential concepts and practical implementations. Learn how to train models using Google Colab's free GPU resources, differentiate between deep learning and machine learning, and explore TensorFlow fundamentals. Understand artificial neural networks, activation functions, and the training process. Discover key components like placeholders, variables, and loss functions. Delve into advanced topics such as backpropagation, recurrent neural networks (RNNs), long short-term memory (LSTM) cells, and convolutional neural networks (CNNs). Master techniques for text encoding, image feature detection, and addressing overfitting. Explore pre-trained models like VGG16 and ResNet for object detection, and learn data augmentation strategies to improve model accuracy on small datasets.

Deep Learning Neural Network Tutorials

The AI University

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#Computer Science #Deep Learning #Machine Learning #Artificial Intelligence #Neural Networks #TensorFlow #Activation Functions #Backpropagation #Loss Functions

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Deep Learning Neural Network Tutorials

1 How to Train Deep Learning Model on Google Colab for FREE | Train Neural Network on GPU Machine