Skills you’ll Learn
CNN
ANN
RNN
Tensorflow
Deep Learning Algorithms
Module
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Foundations of Neural Networks
- Perceptrons and Multilayer Perceptrons (MLPs)
- Activation Functions (ReLU, Sigmoid, Tanh)
- Forward and Backpropagation
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Deep Learning Architectures
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs) and LSTMs
- Transformers and Self-Attention
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Optimization and Training
- Loss Functions (Cross-Entropy, MSE)
- Gradient Descent and Variants (SGD, Adam, RMSprop)
- Overfitting and Regularization (Dropout, L2 Regularization)
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Practical Implementation with Python
- Using TensorFlow and PyTorch
- Hands-on Coding with Jupyter Notebooks
- Training and Evaluating Models
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Real-World Applications
- Image and Speech Recognition
- Natural Language Processing (NLP)
- Reinforcement Learning
Description
This Neural Networks and Deep Learning course gives valuable insights into deep learning applications in various fields and a better understanding of the different frameworks used in neural network applications. You will discover and understand various deep learning concepts and get familiarized with Artificial Neural Network topics. You will also be walked through a few fundamental concepts covering things like the history of the subject and the need to learn it. Further, you will study CNN, RNN, and LSTM and compare types of chatbots and conventional interfaces, Machine learning, and Deep Learning. Earn a certificate for this course after completing the assigned quiz.
Patricia –
“This course provided a solid foundation in neural networks and deep learning. The concepts were explained clearly and concisely, making it easy to grasp the fundamentals. I particularly appreciated the practical examples and hands-on exercises, which helped me solidify my understanding and apply what I learned. The course content was well-structured and progressed logically, building my knowledge step-by-step. Overall, it was a valuable learning experience that I would recommend to anyone interested in entering the field of deep learning.”
Franklin –
“This course provided a fantastic and accessible introduction to neural networks and deep learning. The concepts were explained clearly and concisely, with plenty of practical examples to help solidify my understanding. I especially appreciated the well-structured curriculum and the focus on building a strong foundation for further learning in this exciting field.”
Destiny –
“This course provided a really solid foundation in neural networks and deep learning. The concepts were explained clearly and concisely, and the practical examples helped me understand how to apply the theory. I especially appreciated the well-structured content and the supportive learning environment. I feel much more confident exploring this field now.”
Auwal –
“This course, ‘Introduction to Neural Networks and Deep Learning’, provided a solid foundation in the core concepts. The material was well-structured and easy to follow, even for someone with limited prior knowledge. The practical examples and hands-on exercises were incredibly helpful in solidifying my understanding and building confidence in applying these techniques. I feel well-equipped to explore more advanced topics in the field now.”