Here's how you can expand your understanding of neural networks in machine learning.

Start with foundational concepts like neurons, layers (input, hidden, output), and activation functions. Key Points: Neurons and Layers: Learn how they work and are organized. Activation Functions: Study sigmoid, tanh, ReLU. Backpropagation: Understand its role in training. Personal Experience: "Online courses like those on Coursera and edX helped me grasp the basics."

If you're getting started then somewhat controversially I suggest not going too deep into the theory at the beginning. If you've done some coding before I can highly recommend taking the Deep Learning for Coders course run by Fast.ai. Once you have a high level understanding of what a neural net is and how it's trained, then start to dive more into the detail and the theory behind it.

Visualization would help a lot in understanding deep learning. There are many good videos with good visualizations of a wide range of neural networks ranging from perceptrons to massive transformer models. Once the intuition is there, it is easier to translate this into the math.

Neural networks resemble computer minds that acquire knowledge by simulating the interconnected layers of real brains They are even several sorts of neural networks designed for particular obligations such CNNs which do a great job in image recognition and RNNs which are also good at sequence understanding. To understand how these networks work internally, there are cool online sources with visualizations! You can see how CNNs process images step by step or how RNNs handle sequences like sentences.

To dive deep into neural networks, it's essential to move beyond the basics and explore more advanced concepts. This includes understanding how different types of neural networks work, like those used for images (CNNs) or sequences (RNNs) and learning about algorithms that help networks learn better, such as gradient descent. Practicing with real-world data and staying updated with new research also play a key role in expanding your knowledge and skills in this field.

Explore advanced topics like CNNs, RNNs, and GANs. Key Points: CNNs: For image processing. RNNs: For sequential data. GANs: For data generation. Personal Experience: "Implementing algorithms and using Kaggle datasets solidified my understanding."

My take on this is: Backpropagation is the algorithm used to compute the gradients of the loss function with respect to the weights in a neural network, and these gradients are then used in conjunction with gradient descent to update the weights and minimise the loss.

If you learn a concept in Machine Learning, try to translate it into concrete code. This approach has multiple benefits: it enhances learning through practice. I recommend the book 'Hands-On Machine Learning' by Aurélien Géron and consulting the Keras documentation. Enjoy coding!

Hands-on experimentation is crucial. Key Points: Frameworks: Use TensorFlow, Keras, PyTorch. Hyperparameters: Tweak to improve models. Data Augmentation: Expand datasets. Personal Experience: "Participating in hackathons and competitions taught me a lot."

Learn from real-world applications and case studies. Key Points: Industry Applications: Healthcare, finance, etc. Success and Failure: Learn from both. Personal Experience: "Case studies in healthcare showed me the practical impact of neural networks."

Engage with online communities for support and updates. Key Points: Forums and Groups: Reddit, Stack Overflow, LinkedIn. Social Media: Follow experts on Twitter. Meetups: Attend events. Personal Experience: "Active participation on LinkedIn and Reddit kept me updated."

Continuous learning is essential. Key Points: Online Courses: Coursera, Udacity. Books: "Deep Learning" by Goodfellow et al. Research Papers: arXiv.org. Personal Experience: "I read a research paper weekly to stay informed."