Certificate in RNN for Predictive Modeling
-- ViewingNowThe Certificate in RNN for Predictive Modeling is a comprehensive course that focuses on Recurrent Neural Networks (RNNs), a powerful tool for predictive modeling. This course highlights the importance of RNNs in various industries, including finance, healthcare, and technology, where predictive modeling is crucial for decision-making and forecasting.
5,711+
Students enrolled
GBP £ 149
GBP £ 215
Save 44% with our special offer
ě´ ęłźě ě ëí´
100% ě¨ëźě¸
ě´ëěë íěľ
ęłľě ę°ëĽí ě¸ěŚě
LinkedIn íëĄíě ěśę°
ěëŁęšě§ 2ę°ě
죟 2-3ěę°
ě¸ě ë ěě
ë기 ę¸°ę° ěě
ęłźě ě¸ëśěŹí
⢠Introduction to Recurrent Neural Networks (RNNs): Understanding the basics of RNNs, their architecture, and how they differ from feedforward neural networks.
⢠Long Short-Term Memory (LSTM) Networks: Learning about LSTMs, a special kind of RNN that can learn long-term dependencies and is commonly used for predictive modeling.
⢠Gated Recurrent Units (GRUs): Getting familiar with GRUs, another popular variant of RNNs that can capture long-term dependencies with fewer parameters than LSTMs.
⢠Time Series Analysis with RNNs: Exploring how to apply RNNs for predicting future values in time series data, including data preprocessing techniques.
⢠Sequence-to-Sequence Modeling: Understanding how RNNs can be used for sequence-to-sequence modeling, such as machine translation, text summarization, and speech recognition.
⢠Training RNNs with Backpropagation Through Time (BPTT): Learning how to train RNNs using BPTT, a variant of backpropagation that is specifically designed for RNNs.
⢠Regularization Techniques for RNNs: Exploring regularization techniques for RNNs, such as weight decay, dropout, and zoneout, to prevent overfitting and improve generalization.
⢠Evaluation Metrics for Predictive Modeling: Understanding how to evaluate the performance of RNNs for predictive modeling, including metrics such as mean squared error, mean absolute error, and R-squared.
⢠Implementing RNNs in TensorFlow and Keras: Practicing how to implement RNNs in TensorFlow and Keras, including building and training LSTMs and GRUs for predictive modeling tasks.
ę˛˝ë Ľ 경ëĄ
ě í ěęą´
- 죟ě ě ëí 기본 ě´í´
- ěě´ ě¸ě´ ëĽěë
- ěť´í¨í° ë° ě¸í°ëˇ ě ꡟ
- 기본 ěť´í¨í° 기ě
- ęłźě ěëŁě ëí íě
ěŹě ęłľě ěę˛Šě´ íěíě§ ěěľëë¤. ě ꡟěąě ěí´ ě¤ęłë ęłźě .
ęłźě ěí
ě´ ęłźě ě ę˛˝ë Ľ ę°ë°ě ěí ě¤ěŠě ě¸ ě§ěęłź 기ě ě ě ęłľíŠëë¤. ꡸ę˛ě:
- ě¸ě ë°ě 기ę´ě ěí´ ě¸ěŚëě§ ěě
- ęśíě´ ěë 기ę´ě ěí´ ęˇě ëě§ ěě
- ęłľě ě겊ě ëł´ěě
ęłźě ě ěąęłľě ěźëĄ ěëŁí늴 ěëŁ ě¸ěŚě뼟 ë°ę˛ ëŠëë¤.
ě ěŹëë¤ě´ ę˛˝ë Ľě ěí´ ě°ëŚŹëĽź ě ííëę°
댏롰 ëĄëŠ ě¤...
ě죟 돝ë ě§ëŹ¸
ě˝ě¤ ěę°ëŁ
- 죟 3-4ěę°
- 쥰기 ě¸ěŚě ë°°ěĄ
- ę°ë°Ší ëąëĄ - ě¸ě ë ě§ ěě
- 죟 2-3ěę°
- ě 기 ě¸ěŚě ë°°ěĄ
- ę°ë°Ší ëąëĄ - ě¸ě ë ě§ ěě
- ě 체 ě˝ě¤ ě ꡟ
- ëě§í¸ ě¸ěŚě
- ě˝ě¤ ěëŁ
ęłźě ě ëł´ ë°ę¸°
íěŹëĄ ě§ëś
ě´ ęłźě ě ëšěŠě ě§ëśí기 ěí´ íěŹëĽź ěí ě˛ęľŹě뼟 ěě˛íě¸ě.
ě˛ęľŹěëĄ ę˛°ě ę˛˝ë Ľ ě¸ěŚě íë
