Neural networks are a fundamental concept in artificial intelligence and machine learning, inspired by the human brain’s structure. They consist of interconnected nodes that process information, enabling machines to learn and make decisions. When selecting the best neural networks courses, consider your expertise level and learning goals. Beginners may benefit from courses covering basic concepts, such as feedforward networks and backpropagation. Intermediate learners could explore courses focusing on specific types of neural networks like convolutional or recurrent networks. Advanced practitioners may opt for courses in advanced topics like deep reinforcement learning or generative adversarial networks.
Choose courses from reputable platforms like Coursera, Udemy, edX, or specialized providers. Look for courses with practical applications, real-world projects, and updated content reflecting the latest advancements. Consider instructor expertise and participant reviews to ensure course quality. Hands-on coding and implementation exercises are crucial for mastering neural networks. Making an informed choice will empower you to navigate the dynamic field of artificial intelligence effectively.
Here we listed Neural Networks tutorials which will help you learn Neural Networks from scratch, and are suitable for beginners, intermediate learners as well as experts.
Deep Learning A-Z™ 2024: Neural Networks, AI & ChatGPT Bonus
What you’ll learn
- Understand the intuition behind Artificial Neural Networks
- Apply Artificial Neural Networks in practice
- Understand the intuition behind Convolutional Neural Networks
- Apply Convolutional Neural Networks in practice
- Understand the intuition behind Recurrent Neural Networks
- Apply Recurrent Neural Networks in practice
- Understand the intuition behind Self-Organizing Maps
- Apply Self-Organizing Maps in practice
- Understand the intuition behind Boltzmann Machines
- Apply Boltzmann Machines in practice
- Understand the intuition behind AutoEncoders
- Apply AutoEncoders in practice
The Complete Neural Networks Bootcamp: Theory, Applications
What you’ll learn
- Understand How Neural Networks Work (Theory and Applications)
- Understand How Convolutional Networks Work (Theory and Applications)
- Understand How Recurrent Networks and LSTMs work (Theory and Applications)
- Learn how to use PyTorch in depth
- Understand how the Backpropagation algorithm works
- Understand Loss Functions in Neural Networks
- Understand Weight Initialization and Regularization Techniques
- Code-up a Neural Network from Scratch using Numpy
- Apply Transfer Learning to CNNs
- CNN Visualization
Deep Learning: Recurrent Neural Networks in Python
What you’ll learn
- Apply RNNs to Time Series Forecasting (tackle the ubiquitous “Stock Prediction” problem)
- Apply RNNs to Natural Language Processing (NLP) and Text Classification (Spam Detection)
- Apply RNNs to Image Classification
- Understand the simple recurrent unit (Elman unit), GRU, and LSTM (long short-term memory unit)
- Write various recurrent networks in Tensorflow 2
- Understand how to mitigate the vanishing gradient problem
Deep Learning: Convolutional Neural Networks in Python
What you’ll learn
- Understand convolution and why it’s useful for Deep Learning
- Understand and explain the architecture of a convolutional neural network (CNN)
- Implement a CNN in TensorFlow 2
- Apply CNNs to challenging Image Recognition tasks
- Apply CNNs to Natural Language Processing (NLP) for Text Classification (e.g. Spam Detection, Sentiment Analysis)
Artificial Neural Network and Machine Learning using MATLAB
What you’ll learn
- Develop a multilayer perceptron neural networks or MLP in MATLAB using Toolbox
- Apply Artificial Neural Networks in practice
- Building Artificial Neural Network Model
- Knowledge on Fundamentals of Machine Learning and Artificial Neural Network
- Understand Optimization methods
- Understand the Mathematical Model of a Neural Network
- Understand Function approximation methodology
- Make powerful analysis
- Knowledge on Performance Functions
- Knowledge on Training Methods for Machine Learning
Neural Networks and Deep Learning – Coursera Certification Course
What you’ll learn
- Understand the major technology trends driving Deep Learning
- Be able to build, train and apply fully connected deep neural networks
- Know how to implement efficient (vectorized) neural networks
- Understand the key parameters in a neural network’s architecture
Convolutional Neural Networks in TensorFlow
What you’ll learn
- Handle real-world image data
- Plot loss and accuracy
- Explore strategies to prevent overfitting, including augmentation and dropout
- Learn transfer learning and how learned features can be extracted from models
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
What you’ll learn
- Understand industry best-practices for building deep learning applications.
- Be able to effectively use the common neural network “tricks”, including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
- Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
- Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
- Be able to implement a neural network in TensorFlow.
Introduction to Deep Learning & Neural Networks with Keras
Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.
Deep Neural Networks with PyTorch
What you’ll learn
Demonstrate your comprehension of deep learning algorithims and implement them using Pytorch.
Explain and apply knowledge of Deep Neural Networks and related machine learning methods.
Describe how to use Python libraries such as PyTorch for Deep Learning applications.
Build Deep Neural Networks using PyTorch.
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