Practical Guide to Tensorflow and Keras
If you’re a data scientist familiar with machine learning, this course will provide you with a solid, practical introduction to deep learning, the fastest-growing and most significant subfield of machine learning.
What you’ll learn
- Understand the intuition behind Artificial Neural Networks.
- Apply Artificial Neural Networks in practice.
- Understand the intuition behind Recurrent Neural Networks.
- Understand the intuition behind Convolution Neural Networks.
- Learn how to apply neural networks in several practical examples.
- Build model in tensorflow and keras.
Course Content
- Introduction –> 3 lectures • 22min.
- Building Theoretical Concept –> 3 lectures • 25min.
- Building Practical Concept –> 7 lectures • 55min.
- Getting started with Neural Network –> 6 lectures • 51min.
- ML concepts for Deep Learning –> 4 lectures • 27min.
- Deep Learning for Computer Vision –> 4 lectures • 15min.
Requirements
If you’re a data scientist familiar with machine learning, this course will provide you with a solid, practical introduction to deep learning, the fastest-growing and most significant subfield of machine learning.
If you’re a deep-learning expert looking to get started with the Keras framework, you’ll find this course to be the best Keras crash course available.
If you’re a graduate student studying deep learning in a formal setting, you’ll find this course to be a practical complement to your education, helping you build intuition around the behavior of deep neural networks and familiarizing you with key best practices.
[Note: This course will be updated every weeks with tons of projects, and deep learning concepts]
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
Deep Learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.
In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are ‘trained’ to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data.
Deep Learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.
Google’s AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.
Topics covered in this course:
1. Building Theoretical Concept for Deep Learning: Neurons, Neural Networks, Activation Function etc
2. Building Practical Concept: Tensor, Tensor Operations, Gradient Descent, Backpropagation etc
3. Neural Networks in Details for Deep Learning building Projects: Movie Review Classification, Newswire classification, house price predictions.
4. Machine Learning concepts for Deep Learning: Data preprocessing, Network size, dropout etc
5. Deep Learning for Computer Vision: Convolution Neural Network
6. Recurrent Neural Network (will be added in 25 Nov, 2022)